Audio-visual affective expression recognition
Author(s):
Thomas S. Huang;
Zhihong Zeng
Show Abstract
Automatic affective expression recognition has attracted more and more attention of researchers from different
disciplines, which will significantly contribute to a new paradigm for human computer interaction (affect-sensitive
interfaces, socially intelligent environments) and advance the research in the affect-related fields including psychology,
psychiatry, and education. Multimodal information integration is a process that enables human to assess affective states
robustly and flexibly. In order to understand the richness and subtleness of human emotion behavior, the computer
should be able to integrate information from multiple sensors. We introduce in this paper our efforts toward machine
understanding of audio-visual affective behavior, based on both deliberate and spontaneous displays. Some promising
methods are presented to integrate information from both audio and visual modalities. Our experiments show the
advantage of audio-visual fusion in affective expression recognition over audio-only or visual-only approaches.
Audio-visual gender recognition
Author(s):
Ming Liu;
Xun Xu;
Thomas S. Huang
Show Abstract
Combining different modalities for pattern recognition task is a very promising field. Basically, human always
fuse information from different modalities to recognize object and perform inference, etc. Audio-Visual gender
recognition is one of the most common task in human social communication. Human can identify the gender
by facial appearance, by speech and also by body gait. Indeed, human gender recognition is a multi-modal
data acquisition and processing procedure. However, computational multimodal gender recognition has not
been extensively investigated in the literature. In this paper, speech and facial image are fused to perform a
mutli-modal gender recognition for exploring the improvement of combining different modalities.
Rotation invariant texture classification based on Gabor wavelets
Author(s):
Xudong Xie;
Jianhua Lu;
Jie Gong;
Ning Zhang
Show Abstract
In this paper, an efficient rotation invariant texture classification method is proposed. Comparing with the previous texture classification method, which is also based on Gabor wavelets, two modifications are made in this paper. Firstly, an adaptive circular orientation normalization scheme is proposed. Because both the effects of orientation and frequency to Gabor features are considered, our method can effectively eliminate the disturbance from inter-frequency, and therefore has the ability to reduce the effect of image rotation. Secondly, besides the Gabor features, which mainly represent the local texture information of an image, the statistical property of the intensity values of an image is also used for texture classification in our algorithm. Our method is evaluated based on the Brodatz album, and the experimental results show that it outperforms the traditional algorithms.
Fuzzy recognition method for radar target based on KPCA and SVDD
Author(s):
Lei Guo;
Huaitie Xiao;
Qiang Fu
Show Abstract
Radar target's HRRP always has some information redundancy, and is easily to be affected by noise or lack of
separability. In this paper, using the advantage of kernel methods for solving nonlinear forms, we propose a radar
target's HRRP feature extraction method based on Kernel Principal Component Analysis (KPCA) and a radar target
fuzzy recognition method based on Support Vector Data Description (SVDD). In the course of feature extraction, KPCA
method is used to reduce radar target's HRRP and to compress the dimension of HRRP, so that we can depress the noise
and the sensitivity of target posture; in the course of recognition, we first find the smallest hyper-sphere including every
class of training samples in feature space, then construct the fuzzy membership function according to the distance
between every testing sample and the hyper-sphere surface, so we can recognize every testing sample based on its fuzzy
membership. Simulation results of multi-target recognition reveal that the new method proposed in this paper not only
achieves high recognition accuracy, but also has excellent generalization performance, for instance, we can achieve high
recognition accuracy in lower SNR. So the new feature extraction and recognition method proposed in this paper is
particularly suitable for radar target recognition.
Group clustering and its visualization in group support systems
Author(s):
Caiquan Xiong;
Dehua Li;
Lianghai Jin;
Xianbin Sun
Show Abstract
Consensus building in group support systems relies on the mutual-question and mutual-elicitation of experts, so a
feedback mechanism is required to conduct experts to converge their thinking by visualizing the individual opinion and
the consistent state of the group. This paper proposes a new feedback mechanism, which first clusters the experts'
preferences into a set of subgroups, and then uses different line-types or line-colors to display the clustered opinions in
parallel coordinate. By using this mechanism, the group consistency is analyzed and the group discussion is conducted
efficiently. One of the characteristics of the proposed method is that it can protect the minority views automatically. An
example is presented to illustrate the application of the method.
Scene classification using low-level feature and intermediate feature
Author(s):
Pu Zeng;
Jun Wen;
Ling-Da Wu
Show Abstract
This paper presents a novel scene classification method using low-level feature and intermediate feature. The purpose of
the proposed method is to improve the performance of scene classification and reduce the labeled data required using the
complementary information between low-level and intermediate feature. The proposed method uses the co-training
algorithm to classify scenes, in which the low-level feature and intermediate feature are two views of co-training
algorithm. For low-level feature, Block Based Gabor Texture (BBGT) feature is extracted to describe the texture
property of images incorporating the spatial layout information. For intermediate feature, Bag Of Word (BOW) feature is
extracted to describe the distribution of local semantic concepts in images based on quantized local descriptors.
Experiment results show that this proposed method has satisfactory classification performances on a large set of 13 categories of complex scenes.
New method of 3D point reconstruction from monocular camera
Author(s):
Lei Qi;
Xiaojuan Wu;
Yuanyuan Zhang;
Jun Yang
Show Abstract
Put forward a method of 3D point reconstruction from an image taken by monocular camera. In algorithm, the coordinate
conversion factor of the given point can be calculated through a pair of parallel lines, whose positions in the scene are
known. Then reconstruction of any points in 3D space which contains parallel lines can be achieved using the coordinate
conversion factor. In experiments, the algorithm is used to estimate the trajectory of walking people.
3D scene reconstruction based on the panoramic cameras system of the lunar rover
Author(s):
Chunlin Jiao;
Mantun Gao;
Yikai Shi
Show Abstract
To reconstruct a 3D scene around the lunar rover from stereo image-pairs captured by the panoramic cameras, based on
which an intuitive platform can be put up for scientists to plan exploration commands, we set about to study the 3D
scene reconstruction. This paper mainly presents a scheme of registering local scene models to reconstruction a large
scene. When a few of local 3D scene models have been reconstructed respectively, we firstly find the common 3D point
sets between every two adjacent local models based on edge detection and image matching, secondly fit the matrix of
coordinate transformation employing the technique of separating rotation matrix and translate vector, and lastly register
these local 3D models into a uniform coordinate system. In this scheme, we don't need to set a few of control points in a
scene beforehand; and we determine the rotation matrix by a system of linear equations based on Cayley transformation.
Experimental results of reconstructing indoor and outdoor scenes show that our scene registration method is feasible.
Probability output modeling for support vector machines
Author(s):
Xiang Zhang;
Xiaoling Xiao;
Jinwen Tian;
Jian Liu
Show Abstract
In this paper we propose an approach to model the posterior probability output of multi-class SVMs. The sigmoid
function is used to estimate the posterior probability output in binary classification. This approach modeling the posterior
probability output of multi-class SVMs is achieved by directly solving the equations that are based on the combination of
the probability outputs of binary classifiers using the Bayes's rule. The differences and different weights among these
two-class SVM classifiers, based on the posterior probability, are considered and given for the combination of the
probability outputs among these two-class SVM classifiers in this method. The comparative experiment results show that
our method achieves the better classification precision and the better probability distribution of the posterior probability
than the pairwise couping method and the Hastie's optimization method.
Binocular stereo vision system design for lunar rover
Author(s):
Jun Chu;
Chunlin Jiao;
Hang Guo;
Xiaoyu Zhang
Show Abstract
In this paper, we integrate a pair of CCD cameras and a digital pan/title of two degrees of freedom into a binocular stereo
vision system, which simulates the panoramic cameras system of the lunar rover. The constraints for placement and
parameters choice of the stereo cameras pair are proposed based on science objective of Chang'e-IImission. And then
these constraints are applied to our binocular stereo vision system and analyzed the location precise of it. Simulation and
experimental result confirm the constraints proposed and the analysis of the location precise.
Shape classification using Hidden Markov Model and structural feature
Author(s):
Bangwang Xie;
Zhiyong Wang;
Jiajun Wang
Show Abstract
A novel shape classification method based on Hidden Markov Models (HMMs) is proposed in the paper. Instead of
characterizing points along an object contour, our method employs HMMs to model the relationship among structural
segments of the contour. Firstly, an object contour is partitioned into segments at points with zero curvature value.
Secondly, each segment is represented with structural features. Finally, a HMMs is utilized to characterize the object
contour by treating each segment as an observation of a hidden state. Promising experimental results obtained on two
popular shape datasets demonstrate that the proposed method is efficient in classifying shapes, particularly unclosed
shapes and similar shapes.
Research on target recognition techniques of radar networking based on fuzzy mathematics
Author(s):
Chengbin Guan;
Guohong Wang;
Chengzhun Guan;
Jinshan Pan
Show Abstract
Nowadays there are more and more targets, so it is more difficult for radar networking to track the important targets. To
reduce the pressure on radar networking and the waste of ammunition, it is very necessary for radar networking to
recognize the targets. Two target recognition approaches of radar networking based on fuzzy mathematics are proposed
in this paper, which are multi-level fuzzy synthetical evaluation technique and lattice approaching degree technique. By
analyzing the principles, the application techniques are given, the merits and shortcomings are also analyzed, and
applying environments are advised. Another emphasis is the compare between the multiple mono-level fuzzy synthetical
evaluation and the multi-level fuzzy synthetical evaluation, an instance is carried out to illuminate the problem, then the
results are analyzed in theory, the conclusions are gotten which can be instructions for application in engineering.
3D profilometry reconstructs based on two-frequency projecting grating method
Author(s):
Yanjun Fu;
Wendong Zou;
Huirong Xiao;
Minggang Chai
Show Abstract
In the industry, the three dimension of the object is often measured. But the method is usually the contact measurement.
And the speed is slowly. The measurement is needed both high precision and fast speed, so the non-contact measurement
is required. The grating projecting is the non-contact measurement with prospects. But there are some difficulties in the
method. Firstly, when the object has the steps shape or there are shadows in the grating stripes, the disconnected phase
can't be correctly unwrapped. Secondly, it is very difficulty to realize the real time digital filter. Now the digital filter is
man-machine conversation, so the speed is slowly. Thirdly, in order to measurement the different object, the adaptive
grating is needed.
In order to resolve the above problems, the grating program is created on the computer. The program has many
functions, including the phase shift, the two-frequency grating and the grating frequency is easy to adjust. So the
adaptive grating is realized. The two-frequency grating is programmed by the computer. And it is projected to the
measured object. The measurement object is placed on the exact rotary platform. The deformed grating is collected in the
Charge Coupled Device (CCD). After getting two images, the two images are mosaiced. Then the clear object image
modulated by the grating is got. The problem of the steps shape or there are shadows in the grating stripe is worked out.
Then the fourier transform is used to process the image. In the traditional fourier transform profilometry, the phase is
worked out as follows: After fourier transform, the zero frequency spectra is shifted to the origin of frequency, then filter
the needed signal. Then the needed signal is shifted to the center of frequency, and then the zero frequency is shifted to
both sides. After inverse fourier transform, the imaginary part is getting, so the phase is getting. But it has a difficult in
the above method, because of three times frequency shift, and the center frequency is difficult to confirm, the frequency
shift can't be correct and the filter can't be designed correctly, and the error can be transferred, so the result of filter is
not well, it has bad effect to the later measurement. The result of measurement is also not well. In order to conquer the
difficult, after the fourier transform, filtering the needed signal without frequency shifting, then inverse fourier
transform. So the phase relational with the frequency and coordinate is getting. The phase of the reference surface is
getting by the same method. Then the difference phase is getting. The real difference phase of low frequency is easy to
got, then the real difference phase of high-frequency is work out based on it. At last, according to the relation of
difference phase and the height, the three dimensional profilometry of the object is reconstructed.
An example of step shape object is done. The three dimensional profilometry is reconstructed successfully. It takes 3
second to reconstruct the three dimensional profilometry. The precision is 0.5mm. The result indicates that the method
has conquered the above problems.
The result indicates that the method is simple, with fast speed and high precision. Three dimension profilometry
measurement of the objected that have the step shape or the shadow in the projecting can be successfully resolved.
Novel cross correlation method for redshift determination of galaxy spectra
Author(s):
Fuqing Duan;
Ping Guo
Show Abstract
The rapid development of the astronomical observation has led to many large sky surveys such as SDSS, 2DF, LAMOST
etc. Because of the sheer size of these surveys, it becomes urgent to develop methods of reliable and automated spectral
recognition. A new cross correlation technique for redshift determination of galaxy spectra is presented in this paper. We
use principle components analysis to construct galaxy templates. According to the redshift candidates determined by
spectra line features, cross-correlation between the observed spectrum and the templates is measured by the weighted
sum of several similarity evidences. The candidate of the highest correlation is chosen as the estimated redshift. Both
simulated spectra and observed spectra are used to test the proposed method, the correct rate can reach 97% above.
Research on the city's water affairs dispatchment system based on rough sets theory
Author(s):
Xuwu Li;
Jixue Hua;
Chenghai Li;
Yanlei Li
Show Abstract
According to the main characteristic of the city's water affairs dispatchment, the structure of water affairs dispatchment
based on rough sets theory was proposed. After each factors were considered synthetically, knowledge expression system
was set up, and the water affairs dispatchment control regulation was reduced and acquired. To some extent, it's a new
method of processing the uncertain information in the water affairs dispatchment. The example demonstrates that this
method has reduced the dispatchment control, and its regulation acquired is of objectivity, so it can solve preferably the
control problem of the city's water affairs dispatchment.
Texture classification of aerial image based on Bayesian networks
Author(s):
Li Ma;
Hongjing Yu;
Jiatian Li;
Hao Chen
Show Abstract
Classification is a basic topic in data mining and pattern recognition. Following advances in computer science, a lot of
new methods have been proposed in recent years, such as artificial neural networks, decision trees, fuzzy set and
Bayesian Networks, etc. As a probabilistic network, Bayesian Networks is a powerful tool for handling uncertainty in
data mining and many other domains. Naïve Bayes Classifier (NBC) is a simple and effective classification method,
which is built on the assumption of conditional independence between the class attributes. This topology structure can
not describe the inherent relation among the features. In this paper, we apply Bayesian Networks Augmented Naïve Bayes (BAN) for the texture classification of aerial images, which relaxes the independent assumption in NBC. A new method for learning the networks topology structure based on training samples is adopted in this paper. Comparison experiments show higher accuracy of BAN classifier than NBC. The results also show the potential applicability of the proposed method.
Recognizing license plate character based on simplified PCNN
Author(s):
Jun Wu;
Zhitao Xiao
Show Abstract
In LPR system, character recognition subsystem is heavily affected by image quality. To resolve this problem and
improve recognition rate, a new algorithm is proposed, in which pulse coupled neural network (PCNN) is applied into the
recognition of license plate character. PCNN model is simplified to improve computation efficiency, and then is utilized to
extract three features from dimension-normalized binary result of input character image. Based on these features, weighted
voting is performed and final estimation of input character is made. The experiment results show that compared with common algorithms based on BP network, the new algorithm based on simplified PCNN model has higher total recognition rate and stronger robustness, and is more convenient and flexible.
Research of 3D model construction and visualization of complicated objects in 3D cyber-city
Author(s):
Peng Chen;
Linkui Meng
Show Abstract
In order to settle the conflict of the visualization efficiency and the great amount data in digital city in 3d GIS system, the
author put forward a 3D data model with LOD ability especially for the complex 3d object such as buildings in 3d city GIS
system. In this paper, the author described the components of the complex 3d object, then explained that we can settle the
conflict mentioned above by designing a new model that has the capability of describing levels of details. The author
expound the basic theory of the model with LOD ability related with the vision point, and then defined several key
conceptions, at same time, the author analyzed the principles and the visualization process of the model in 3d space. In the
end of the paper, the author verified data model through an experiment, the results of the experiment showed that the model
put forward is effective and high efficiency.
Visual inspection of industrial sheet metal part with CAD data
Author(s):
Min Tang;
Zuxun Zhang;
Jianqing Zhang
Show Abstract
Introduce a CAD-based visual inspection system, which is designed to measure geometry dimension of sheet metal parts
automatically, such as distance, angle, parameters of circle, etc. The inspection system extracts the features of sequence
images depending on CAD data, and then to reconstruct real 3D of part. It outputs the measurement results against the
various requirements of customer. The main contents of the paper include searching and matching of group of lines with
image space constrains; visual inspection and reconstruction of sheet metal parts based on the photogrammetry
generalized point photogrammetry. The result of experiments shows that the inspection system is robust and achieves the
precision level of repeated manual measurement of an experienced inspector. The arithmetic discussed in the paper has
potential to deal with another object with sharpness edges except sheet metal part.
Novel algorithm for iris localization
Author(s):
Yunxin Wang;
Tiegen Liu;
Li Liu
Show Abstract
With the emerging security demands, biometric identification technology has attracted more and more attention in recent
years, and iris recognition is one of the most reliable biometric technologies. Iris localization is a crucial part in the iris
recognition, which is quite time-consuming and easily disturbed by various noises, especially the eyelashes. A novel iris
localization method is proposed in this paper. In the location of inner iris boundary, the gray curves of a row and a
column with the pupil edge are used to estimate the coarse center and radius of pupil, which can reject the eyelash noises.
The experiments show this coarse location method has better accuracy and speed than the common gray projection. Edge
points of pupil are extracted by a gradient operator and fitted as the iris inner boundary. In the location of outer iris
boundary, the image binarization is use to mark most noises, and then the outer iris boundary is extracted by
integro-differential operator from the coarseness to fine. Performance experiments have been done, and the results show
that about 0.175 second at speed and 99.5% at precision are reached by developed algorithm. In comparison with other
classical methods, this algorithm has faster speed and better robustness.
Study of the model of probability-based covering algorithm
Author(s):
Ying Zhou;
Yangqun Xie;
Ling Zhang
Show Abstract
Probability-Based Covering Algorithm (PBCA) is a new algorithm based on probability distribution. It uses the probability of samples and decides the class of the sample on the border of coverage by voting. In the original covering algorithm, there are many tested samples that can't be classified by the spherical neighborhood gained. The network structure of PBCA is mixed structure composed of feed-forward network and feedback network. The method of adding
some samples of different class and enlarging the coverage radius is used to decrease the number of refused samples and
improve the rates of recognition. The algorithm is effected in improving the study precision.
Texture image recognition based on modified probabilistic neural network
Author(s):
Dingqiang Yang;
Shuping Xiao;
Jiafu Jiang
Show Abstract
Differential Evolution (DE) method is introduced in this paper to make up the insufficiency of basic probabilistic neural
network. Consequently, a new texture image recognition method based on Modified Probabilistic Neural Network
(MPNN) is proposed. At first, tree structure wavelet packet transformation is used to extract the energy characteristic,
and statistical method is used to extract the statistical mean value, average energy, standard deviation, and mean residual
characteristics for obtaining the feature vector; then the feature vector of texture image is trained by the MPNN, thus the
texture image is identified. The experiment result indicates that, compared to the BP neural network, RBF neural
network, and the basic probabilistic neural network, the modified probabilistic neural network has higher accuracy and
faster convergence speed.
Improved neighborhood preserving embedding approach
Author(s):
Ruicong Zhi;
Qiuqi Ruan
Show Abstract
In this paper, we proposed a manifold-based algorithm called Orthogonal Neighborhood Preserving Embedding (ONPE)
for dimensionality reduction and feature extraction. ONPE algorithm is based on the Neighborhood Preserving
Embedding (NPE) algorithm. NPE is an unsupervised dimensionality reduction method which is the linear
approximation of classical nonlinear method. However, the feature vectors obtained by NPE are nonorthogonal. ONPE
inherits NPE's neighborhood preserving property and produces orthogonal feature vectors. As orthogonal eigenvectors
preserve the metric structure of the image space, the ONPE algorithm has more neighborhood preserving power and
discriminating power than NPE. Furthermore, ONPE can find the mapping which best preserves the manifold's
estimated intrinsic geometry structure in a linear sense. Experimental results show that ONPE is an effective method for
feature extraction.
Time-critical adaptive visualization method of 3D city models
Author(s):
Yeting Zhang;
Qing Zhu;
Mingyuan Hu
Show Abstract
The development of hardware and software is not sufficient to meet the real-time visualization requirements of large
scale 3D City Models. How to adaptively coordinate the speed and quality of rendering according to the data volume and
hardware/software environment is therefore a critical issue. This paper proposes an algorithm which predicts the
rendering time according to the features of 3D City Models at first, and then to calculate the object importance value
based on the mathematical model which considers the indicators of each object: location, distance, visibility and
semantics, and finally to select the object set to be rendered by a fast recursive algorithm. There are five factors selected
to test their influence on rendering time: triangle number, vertex number, texture number, screen pixel number, and the
texture image size. According to multivariate statistical theory, experimental results prove that both geometry and texture
data size are significant for rendering time of 3D City Models. A typical 3D building group models are employed for
experimental analysis. The results show that the method introduced in this paper is accurate to predict the time of
rendering 3D models with detailed texture. The adaptive rendering performance is also significantly improved.
Textural defect detect using a revised ant colony clustering algorithm
Author(s):
Chao Zou;
Li Xiao;
Bingwen Wang
Show Abstract
We propose a totally novel method based on a revised ant colony clustering algorithm (ACCA) to explore the topic of
textural defect detection. In this algorithm, our efforts are mainly made on the definition of local irregularity
measurement and the implementation of the revised ACCA. The local irregular measurement defined evaluates the local
textural inconsistency of each pixel against their mini-environment. In our revised ACCA, the behaviors of each ant are
divided into two steps: release pheromone and act. The quantity of pheromone released is proportional to the irregularity
measurement; the actions of the ants to act next are chosen independently of each other in a stochastic way according to
some evaluated heuristic knowledge. The independency of ants implies the inherent parallel computation architecture of
this algorithm. We apply the proposed method in some typical textural images with defects. From the series of
pheromone distribution map (PDM), it can be clearly seen that the pheromone distribution approaches the textual defects
gradually. By some post-processing, the final distribution of pheromone can demonstrate the shape and area of the
defects well.
Research on methodology of image semantic understanding based on generalized computing
Author(s):
Yangu Zhang;
Min Yao;
Zhen Yuan
Show Abstract
Image semantic understanding is one of the most important techniques for solving the problem of semantic gap. By
introducing generalized computing into image semantic understanding, this paper presents a kind of third class image
description model. Then, under the guidance of the model, the approaches of image semantic information extraction is
proposed based on generalized set and generalized transformation. Finally, a kind of image semantic understanding
system based on generalized is sketched out.
People in videos from people in pictures
Author(s):
Jehanzeb Abbas;
Charlie K. Dagli;
Thomas S. Huang
Show Abstract
We propose an appearance based model for face recognition in news videos using an enormously large databank of still
images. This is a step towards building an elaborate face-query system using multimodal audio-visual data. We use the
fact that faces of the same person appear similar than of different people. We preprocess the videos, apply feature
extraction, feature matching and a unique parallel line matching algorithm to develop a simple yet a powerful face
recognition system. We tested our approach on real world data and the results show good performance both for high
resolution still images and low resolution news videos without involving any training or tasks like face rectification,
warping etc. It can be incorporated as part of a larger multimodal news video analysis system with problems of time
alignment between text and faces. Our results show that this simple approach also works well where video modality is
the only source of information.
Simulation of sea surface images in the infrared and similarity evaluate
Author(s):
Jiangbo Yang;
Yuehuan Wang;
Tianxu Zhang
Show Abstract
The simulation of ocean environment in the infrared has been a hot yet difficult problem in the field of computer
simulation. In this paper, the shortage of the simulation of infrared ocean images with Vega is analyzed, and then a new
simulation method based on 3D modeling with OpenGL is introduced. The new method abandons the high precision
mesh but uses mathematical model to manipulate vertex of the mesh and establish the model. Experiments demonstrated
that the method proposed is much more efficient and guarantees the quality of the simulation images. Finally a similarity
evaluation function based on features extracted from co-occurrence matrix such as angular second moment, entropy,
related coefficient, contrast and uniformity is put forward to evaluate the similarity of the images.
Line-based logo recognition through a web-camera
Author(s):
Xiaolu Chen;
Yangsheng Wang;
Xuetao Feng
Show Abstract
Logo recognition has gained much development in the document retrieval and shape analysis domain. As human computer
interaction becomes more and more popular, the logo recognition through a web-camera is a promising technology in view
of application. But for practical application, the study of logo recognition in real scene is much more difficult than the work
in clear scene. To cope with the need, we make some improvements on conventional method. First, moment information is
used to calculate the test image's orientation angle, which is used to normalize the test image. Second, the main structure of
the test image, which is represented by lines patterns, is acquired and modified Hausdorff distance is employed to match the
image and each of the existing templates. The proposed method, which is invariant to scale and rotation, gives good result
and can work at real-time. The main contribution of this paper is that some improvements are introduced into the exiting
recognition framework which performs much better than the original one. Besides, we have built a highly successful logo
recognition system using our improved method.
Building detection from LIDAR and images in urban areas
Author(s):
Haiyan Guan;
Fei Deng;
Jianqing Zhang
Show Abstract
Rapid texture mapping of buildings is a key aspect for reconstruction of 3D city landscapes. An effective approach by
the way of coarse-to-fine 3D building model generation by integration of LIDAR and multiple overlap images is
proposed. Classification and segmentation can be processed by combined multi-spectral information which is provided
by color aerial image and geometric information from multi-return laser scanned data. A connected graph of the segment
label image has to be created to derive the neighborhood relation of the planar segments. A line segment matching, based
on geometry and chromatic constraint, is applied for automatically getting the corresponding line features in multi target
images. Hypotheses for polyhedral surfaces are selected using topological relations and verified using geometry.
Classification method based on KCCA
Author(s):
Zhanqing Wang;
Guilin Zhang;
Guangzhou Zhao
Show Abstract
Nonlinear CCA extends the linear CCA in that it operates in the kernel space and thus implies the nonlinear
combinations in the original space. This paper presents a classification method based on the kernel canonical correlation
analysis (KCCA). We introduce the probabilistic label vectors (PLV) for a give pattern which extend the conventional
concept of class label, and investigate the correlation between feature variables and PLV variables. A PLV predictor is
presented based on KCCA, and then classification is performed on the predicted PLV. We formulate a frame for
classification by integrating class information through PLV. Experimental results on Iris data set classification and facial
expression recognition show the efficiencies of the proposed method.
Exploiting pair-wise constraints between parts for human tracking
Author(s):
Jin Zhang;
Xiaohui Shen;
Jie Zhou;
Gang Rong
Show Abstract
Human tracking has attracted much attention from the researchers in the fields of computer vision and pattern
recognition. The problem is generally extremely challenging partly because human bodies are articulated and versatile,
and partly because background clutter, both of which demand a strong human model. However, there is usually a trade-off
between the discriminative power and the complexity of a given model. This paper presents a simple yet distinctive
appearance model for real time human tracking by exploiting the pairwise constraints between parts. The parts in our
model are generated online by sampling the foreground of the scene into overlapping blocks and grouping them into
appearance coherent parts with mean shift algorithm. Constraints between the resulting parts are defined and used to
encode the structure of human body. To tolerate the possible human deformations and occlusions, the model is layered.
With this model, we design an algorithm for human tracking and test its performance on real world image sequences.
Experimental results show that the proposed appearance model although simple, has enough discriminative power to
classify multiple humans even in presence of occlusions and the associated tracking method can run in real time.
Object tracking with revised SMOG model
Author(s):
Huan Wang;
Mingwu Ren;
Jingyu Yang
Show Abstract
Spatial color Mixture Of Gaussians model (SMOG model) based similarity measure is superior to the popular
color histogram based one since it considers not only the colors in a region, but also the spatial layout of these colors.
However, two drawbacks of SMOG are still obvious, firstly, in the initialization of SMOG, some background pixels are
inevitably introduced and clustered as an object mode for tracking, this often degenerates the tracking performance.
Secondly, the weight of each Gaussian mode is restricted by the probability of the pixels belong to it, so a low
probability Gaussian mode always contribute a little in similarity measure even it has a high discrimination for
discriminating the object. A revised SMOG model is proposed to efficiently cope with these two problems by sufficiently
considering the object local background. Experiment results on synthetic and real image sequences verified the validity
of the revised model.
Self calibration of camera with non-linear imaging model
Author(s):
Wenguang Hou;
Tao Shang
Show Abstract
Being put forward by the researchers in computer vision, self calibration commonly deals with camera with linear model.
Since the distortion is practically existed especially for ordinary camera, the result of calibration can't meet the demand
of vision measurement with high accuracy regardless of the distortion. Being obedience to systematism mainly, the
distortion is the target function of distortion coefficient, principal point, principal distance ratio and skew factor etc. So
there exists a group of parameters including of distortion coefficient, principal point, principal distance ratio and skew
factor and fundamental matrix which make homologous point meets epipolar restriction theoretically. Accordingly, the
paper advances the way titled self calibration of camera with non-linear imaging model which is on basis of the Kruppa
equation. In calculating the fundamental matrix, we can obtain interior elements except principal distance by taking into
account distortion correction about image coordinate. Then the principal distance can be obtained by using Kruppa
equation. This way only need some homologous points between two images, not need any known information about
objects. Lots of experiments have proven its correctness and reliability.
Geo-registration and mosaic of UAV video for quick-response to forest fire disaster
Author(s):
Jun Wu;
Zhongkui Dong;
Guoqing Zhou
Show Abstract
UAV Video is rapidly emerging as a widely used source of imagery for many applications in recent years. This paper
presents our research on the UAV video processing system for the purpose of fire surveillance, which include: (1) UAV
video stream processing. This step involves three aspects: decoding, re-sampling and matching. Microsoft(R) DirectX(R)
technology is used to decode highly compressed video stream and re-sampled them into still video frame based on the
time base and rate of UAV navigation sensor. One feature-based image-matching algorithm is developed to quickly
obtain Tie points for latter calibration operation. (2) UAV system orientation. This step also involves three aspects:
Camera IOP, Boresight Alignment and bundle adjustment. TSAI's two-stage technique is used to obtain initial camera
focus length f, distortion coefficients k1 and six Exterior Of Parameter (EOP) for one selected video image. Meanwhile, the Boresight Matrix is deduced by the comparison of GPS/INS derived parameters with solved EOPs. Further more, all
parameters including EOPs of all re-sampled video images and camera IOP are optimally estimated based on developed
bundle adjustment algorithm. (3) UAV Video geo-registration and mosaic. All re-sampled video frames are geo-registered
into uniform geo-reference coordinate frame vice Classic photogrammetric orthorectification model and merge
with each other with developed mosaic algorithm. The results demonstrated that the geo-accuracy of mosaic image
generated from UAV video can achieve 1-2 pixels in planimetry and its combination with GIS-supported data for fast
response to time-critical event, e.g., forest fire, is descried.
Topologically clustering: a method for discarding mismatches
Author(s):
Yongtao Wang;
Dazhi Zhang;
Chenqiang Gao;
Jinwen Tian
Show Abstract
Wide baseline stereo correspondence has become a challenging and attractive problem in computer vision and its related
applications. Getting high correct ratio initial matches is a very important step of general wide baseline stereo
correspondence algorithm. Ferrari et al. suggested a voting scheme called topological filter in [3] to discard mismatches
from initial matches, but they didn't give theoretical analysis of their method. Furthermore, the parameter of their
scheme was uncertain. In this paper, we improved Ferraris' method based on our theoretical analysis, and presented a
novel scheme called topologically clustering to discard mismatches. The proposed method has been tested using many
famous wide baseline image pairs and the experimental results showed that the developed method can efficiently extract
high correct ratio matches from low correct ratio initial matches for wide baseline image pairs.
Multi-scale bi-domain Bayesian classifier designed for infrared image segmentation
Author(s):
Qianjin Zhang;
Lei Guo
Show Abstract
An extended Bayesian classifier, which is able to fuse information in original image and in its wavelet domain, is
designed for infrared image segmentation. The algorithm begins with a re-sampling process over the original image and
a wavelet transformation of the original image. Then, the Spatially Variant Mixture Model (SVMM) is applied in the
bootstrap samples and the wavelet coefficients. The corresponding parameters are estimated by EM (Expectation
Maximum) algorithm. Finally, a two-element Bayesian classifier is constructed. One part of the classifier is designed to
exploit information in the original image, and the other part is designed to exploit information obtained in the wavelet
domain. Theoretic analysis and experimental results confirms that the approach is efficient for infrared image
segmentation, robust to noise and less computationally involved.
Panning and multi-baseline digital close-range photogrammetry
Author(s):
Tao Ke;
JianQing Zhang
Show Abstract
The most methods of close-range photogrammetry are based on Direct Linear Transformation (DLT). But DLT often has
unstable solution and every image needs more than six ground control points to compute DLT parameters, so this
method is hard to acquire the high accuracy and its efficiency is low. The paper discusses a new method of digital close-range
photogrammetry - panning and multi-baseline digital close-range photogrammetry. This method enlarges the
intersection angle and improves the intersection precision by multi-baseline. At the same time this method applies the
classic aerotriangulation and bundle adjustment to the close-range photogrammetry, we need more than three ground
control points to compute the exterior orientation elements of all images. The experiments prove that this method can
acquire the high accuracy.
JBC: joint boost clustering method for synthesis aperture radar images
Author(s):
Mengling Liu;
Chu He;
Gui-Song Xia;
Xin Xu;
Hong Sun
Show Abstract
A clustering method based on Joint Boost for Synthesis Aperture Radar images is proposed. In this method, we follow
the steps of Joint Boost, but substitute weak learns with basic clustering algorithm. We compute the sharing features
between samples in order to reduce clustering times. The proposed clustering method, JBC constructs a new training set
by random sampling from the original dataset, then selects the best feature and the best clusters for sharing, and
calculates a distribution over the training samples using current shared feature and clusters, and finally a basic clustering
algorithm (e.g. K-mean) is applied to partition the new training set. The final clustering solution is produced by
aggregating the obtained partitions. The clustering results for SAR images show that the proposed method has a good performance.
New image distance and its application in object recognition
Author(s):
Bing Yang;
Jun Zhang;
Dajiang Shen;
Jinwen Tian;
Yongcai Liu
Show Abstract
This paper presents a new distance measure for image matching based on local Kullback-Leibler divergence, which we
call Image Kullback-Leibler Distance (IKLD). Unlike traditional methods, IKLD takes account into not only the spatial
relationships of pixels, but also the structure information around pixels. Therefore, it is robust enough to small changes
in viewpoint. In order to illustrate its performance, we imbed it into support vector machines for view-based object
recognition. Experimental results based on the COIL-100 show that it outperforms most existing techniques, such as
traditional PCA+LDA (principal component analysis, linear discriminant analysis), non-linear SVM, Discriminant
Tensor Rank-One Decomposition (DTROD) and Sparse Network of Winnows (SNoW).
Video shot classification with concept detection
Author(s):
Zhong Ji;
Yuting Su
Show Abstract
It is a challenging work to classify video shots into a predefined genre set according to their semantic contents, which is
helpful to video indexing, summarization and retrieval. This research proposes a novel shot classification algorithm with
concept detection for news video programs. Six semantic shot types are studied and categorized: Anchorperson,
Monologue, Reporter, Commercial, Still image and Miscellaneous, in which anchorperson shots are detected by
clustering methods, reporter and monologue shots are distinguished by Conditional Random Fields (CRFs), and the last
three categories are picked out by rule-based methods. Multimodality features are employed, such as visual, audio, face,
temporal and contextual features. The experimental results show its effectiveness and achieve a high average accuracy of 96.5%.
Classification of lunar soil from reflectance spectrum by PCA and SVM
Author(s):
Xiaoyu Zhang;
Maohai Huang;
Jun Chu;
Chunlai Li
Show Abstract
Scientists on the ground need understand the environment around the unmanned lunar rover in lunar exploration through
analyzing data obtained by various payloads. There are two main material on the moon, high land material and mare
material on the moon. We use reflectance spectrums of lunar soils from Apollo mission measured by LSCC to classify
the two kinds of materials. Principal component analysis is applied to reduce and select the feature of the reflectance
spectrums. These features input support vector machine, which base on statistical learning theory and is used widely to
classify in modern pattern recognition. Our work shows that the reflectance spectrums of lunar soils are strong link with
the material which they represent.
Image merging method based on characteristic objects' distributing statisic feature
Author(s):
Pingjiang Wang;
Wenpin Zhou;
Jiayong Wu
Show Abstract
An algorithm for merging images has been proposed based on statistic principle of region geometric shape in the paper.
The Algorithm is of high precision and speed for solving images conjoint when there is rare point's feature on those
images. For enhancing the region geometric shape features we described an Image Difference Dynamic Binary Method
firstly. And then the merging principle of images on which there is little point's feature. Last the process and steps of
merging image are described in all details.
Analysis of image quality based on perceptual preference
Author(s):
Liqin Xue;
Guangzhou Zhao;
Yaping Qi
Show Abstract
This paper deals with image quality analysis considering the impact of psychological factors involved in assessment. The
attributes of image quality requirement were partitioned according to the visual perception characteristics and the
preference of image quality were obtained by the factor analysis method. The features of image quality which support
the subjective preference were identified, The adequacy of image is evidenced to be the top requirement issues to the
display image quality improvement. The approach will be beneficial to the research of the image quality subjective
quantitative assessment method.
Application of ant colony optimization (ACO) algorithm to remote sensing image classification
Author(s):
Qin Dai;
Jianbo Liu
Show Abstract
Ant Colony Optimization (ACO) algorithm takes inspiration from the coordinated behavior of ant swarms, which has
been applied in many study fields as a novel evolutionary technology to solve optimization problems. But it has rarely
been used to process remote sensing data. Using the ACO algorithm to remote sensing image classification does not
assume an underlying statistical distribution for the pixel data, the contextual information can be taken into account, and
it has strong robustness. In this paper, taking Landsat TM data as an example, the process of ACO method in remote
sensing data classification is introduced in detail, and has achieved a good result. The study results suggest that ACO
become a new effective method for remote sensing data processing.
Extraction and trace of body joints in human motion capture system
Author(s):
Tao Wang;
Guanghong Gong;
Liang Han
Show Abstract
In the marker based human motion capture system, it's a key step to accurately extract and track the 2-D coordinates
of the body joints, that because the 3-D reconstruction process and the reliability of the capture system depend heavily
on it. Different from those traditional solutions, we use ordinary industrial cameras and take colorful balls as the markers
to solve this key point. We have also promoted our solution to solve the problem of occlusion. Finally, we got perfect
result in practical applications, and the whole process can be computed in real-time. The method will be extended in the
future use.
Parallel data processing based on image feature extraction in reverse measurement
Author(s):
Guanghui Li;
Guangjun Liu;
Liangjun Song;
Guangyu Tan
Show Abstract
A model of a measurement system composed by two CCD cameras using parallel binocular line-structured light is
proposed. The singular value decomposition is used to solve the over-determined equation to obtain the parameters in the
imaging model of the cameras. The feature recognition technique is applied to segment feature information of the image
in the process of range image acquisition. Then pre-processing (image smoothing, binaryzation and image segmentation)
of the image is processed, and the image is condensed to remove useless information. The image acquisition and
condensing are carried out in parallel to gather image and extract effective data simultaneously. The proposed method
solves the difficulty of removing the disturbed information in range image and realizes parallel data processing, which
greatly simplifies the following work of image matching and image characteristic data extraction.
Novel image matching confidence fusion evaluation algorithm based on support vector machine
Author(s):
Lamei Zou;
Zhiguo Cao;
Tianxu Zhang
Show Abstract
Confidence evaluation is an important technique in image matching process. This paper proposes a confidence level
evaluation method for image matching result based on support vector machine (SVM). We divide the matching result
into two different types: the correct result and the wrong result. So we translate the match result's confidence evaluation
problem into the matching result's classification. This paper firstly provides a method of how to prepare the character
parameters which can accurately reflect the matching performance. And then the SVM based on Gaussian kernel is used
as a classifier to classify the match result and discriminate the match result's type. The experiments show that this
method is effective. Compared with the Dempster-Shafer (D-S) evidence reasoning fusion method it has much higher
accuracy.
Analysis of facial characteristics in spectropolarimetric imagery
Author(s):
Yongqiang Zhao;
Lei Zhang
Show Abstract
Spectropolarimetric imaging can provide useful discriminating information for human face recognition that cannot be
obtained by other imaging methods. This paper examines the ability of face recognition by using spectropolarimetric
images. The Spectropolarimetric images were collected by using a CCD camera equipped with a liquid crystal tunable
filter, which could capture 32 bands of images over the visible and near-infrared light (0.4μm-0.72μm). Since
polarization techniques have better contrast mechanisms for tissue imaging and spectroscopy, and can also provide
additional information about the structure of tissues, it is expected that better discriminate performance can be obtained
by using polarimetric and spectral information than just using spectral information. An algorithm for facial
characteristics analysis is presented to exploit only the spectropolarimetric information from different types of facial
tissues. Experiments demonstrate that the proposed algorithm can distinguish efficiently the different facial tissues.
Image categorization based on multi-scale vocabulary
Author(s):
Xin Yang;
Jinhui Tang;
Xiuqing Wu
Show Abstract
Nowadays, local feature based image categorization algorithm has attracted increasing attention in the computer vision
community. In this paper, we present a local feature based image categorization scheme by using Multi-Scale
Vocabulary. This technique works by partitioning the feature space into clusters at several different levels to form multi-scale
vocabulary and generate corresponding fixed-length descriptors at different scales for each image. Then we design
particular similarity measure for multi-scale descriptors and finally apply KNN and SVM to realize image categorization
task. Experiments conducted on the ETH80 dataset have demonstrated the effectiveness of our approach.
Novel framework for producing multi-scale and multi-viewpoint images based on remote sensing stereopair
Author(s):
Ying Tan;
Zhiguo Cao;
Yukun Li
Show Abstract
Automatic target recognition(ATR) is the key of the image guidance technology, yet it is difficult to recognize the target
by merely depending on the real-time image acquired by flying vehicle cameras, moreover, the task of recognizing the
target from the real-time images by the vehicle-carrying image processing system is a hard work itself. The main trend of
the ATR nowadays is to make utilization of the images produced by high-resolution remote sensing satellite to retrieve
the front elevation of the interested region before hand. These front elevations are loaded upon the flying vehicles and
are matched with the real-time images acquired by vehicle-carrying cameras to recognize the interested target. Obviously,
the key step of this method is to recover the 3D information from 2D images. This paper proposed a framework to
produce multi-scale and multi-viewpoint projection images based on remote sensing satellite stereopair by means of
photogrammetry and computer vision. First we proposed a algorithm for reconstructing the 3D structure of the target by
digital photogrammetric techniques and establishing the 3D model of the target using the OpenGL visualization toolkit.
Then the conversion relationship between the world coordinate system and the simulation space coordinate system is
provided to produce the front elevation in the simulation space.
Learning framework for examiner-centric fingerprint classification using spectral features
Author(s):
Paul W. H. Kwan;
Yi Guo;
Junbin Gao
Show Abstract
In recent years, the tasks of fingerprint examiners have been greatly aided by the development of automatic fingerprint
classification systems. These systems operate by matching low-level features automatically extracted from fingerprint
images, often represented collectively as numeric vectors, for their decision. However, there are two major shortcomings
in current systems. First, the result of classification depends solely on the chosen features and the algorithm that matches
them. Second, the systems cannot adapt their results over time through interaction with individual fingerprint examiners
who often have different degrees of experiences. In this paper, we demonstrate by incorporating relevance feedback in a
fingerprint classification system, a personalized semantic space over the database of fingerprints for each user can be
incrementally learned. The fingerprint features that induce the initial features space from which individual semantic
spaces are being learned were obtained by multispectral decomposition of fingerprints using a bank of Gabor filters. In
this learning framework, the out-of-sample extension of a recently introduced dimensionality reduction method, called
Twin Kernel Embedding (TKE), is applied to learn both the semantic space and a mapping function for classifying novel
fingerprints. Experimental results confirm this learning framework for examiner-centric fingerprint classification.
Large-scale building scenes reconstruction from close-range images based on line and plane feature
Author(s):
Yi Ding;
Jianqing Zhang
Show Abstract
Automatic generate 3D models of buildings and other man-made structures from images has become a topic of
increasing importance, those models may be in applications such as virtual reality, entertainment industry and urban
planning. In this paper we address the main problems and available solution for the generation of 3D models from
terrestrial images. We first generate a coarse planar model of the principal scene planes and then reconstruct windows to
refine the building models. There are several points of novelty: first we reconstruct the coarse wire frame model use the
line segments matching with epipolar geometry constraint; Secondly, we detect the position of all windows in the image
and reconstruct the windows by established corner points correspondences between images, then add the windows to the
coarse model to refine the building models. The strategy is illustrated on image triple of college building.
High resolution remote sensing image classification with multiple classifiers based on mixed combining rule
Author(s):
Zhong Chen;
Jianguo Liu;
Guoyou Wang
Show Abstract
The development of the remote sensing technology makes us obtain very abundant information of nature, especially
with the appearance of high resolution remote sensing image it extends the visual field of the nature. High-resolution
satellite images such as Quickbird and IKONOS have been applied into many fields. But the challenge that faces us is
how to make use of the data effectively and obtain more useful information through some processing. Because in the
target recognition, the mutual-complementarity among the different results obtained by the different classifier making
using of the same features usually is very strong and high resolution remote sensing data have a lot of characteristics
such as spectral, texture and context and so on compared to the other lower resolution remote sensing data, the Multiple
Classifiers making use of multi-characteristic was proposed to improve the high resolution remote sensing image
classification accuracy in this paper. The experiments show that the approach can obtain higher classification accuracy
and better classification result than single classifier.
3D-reconstruction of a building from LIDAR data with first-and-last echo information
Author(s):
Guoning Zhang
Show Abstract
With the aerial LIDAR technology developing, how to automatically recognize and reconstruct the buildings from LIDAR dataset is an important research topic along with the widespread applications of LIDAR data in city modeling, urban planning, etc.. Applying the information of the first-and-last echo data of the same laser point, in this paper, a scheme of 3D-reconstruction of simple building has been presented, which mainly include the following steps: the recognition of non-boundary building points and boundary building points and the generation of each building-point-cluster; the localization of the boundary of each building; the detection of the planes included in each cluster and the reconstruction of building in 3D form. Through experiment, it can be proved that for the LIDAR data with first-and-last echo information the scheme can effectively and efficiently 3D-reconstruct simple buildings, such as flat and gabled buildings.
Face recognition under variable lighting using local qualitative representations
Author(s):
Yi Zhang;
Ying Chu;
Xingang Mou;
Guilin Zhang
Show Abstract
In this paper, a face recognition method using local qualitative representations is proposed to solve the problem of face
recognition in varying lighting. Based on the observation that the ordinal relationship between the average brightness of
image regions pair is invariant under lighting changes, Local Binary Mapping is defined as an illumination invariant for
face recognition based on Local Binary Pattern descriptor, which extracts the local variance features of an image. For the
'symbol' feature vector, hamming distance is used as similarity measurement. It has been proved that the proposed
method can provide the accuracy of 100 percent for subset 2, 3, 4 and 98.89 percent for subset 5 of the Yale facial
database B when all images in subset 1 are used as gallery.
Object category recognition using boosting tree with heterogenous features
Author(s):
Liang Lin;
Caiming Xiong;
Yue Liu
Show Abstract
The problem of object category recognition has long challenged the computer vision community. In this paper, we
address these tasks via learning two-class and multi-class discriminative models. The proposed approach integrates the
Adaboost algorithm into the decision tree structure, called DB-Tree, and each tree node combines a number of weak
classifiers into a strong classifier (a conditional posterior probability). In the learning stage, each boosted classifier in a
tree node is trained to split the training set to left and right sub-trees, and the classifier is thus used not to return the class
of the sample but rather to assign the sample to the left or right sub-tree. Therefore, the DB-Tree can be built up
automatically and recursively. In the testing stage, the posterior probability of each node is computed by the weighted
conditional probability of left and right sub-trees. Thus, the top node of the tree can output the overall posterior
probability. In addition, the multi-class and two-class learning procedures become unified, through treating the multi-class
classification problem as a special two-class classification problem, and either a positive or negative label is
assigned to each class in minimizing the total entropy in each node.
Rating of web pages based on image content analysis
Author(s):
Feng Xiao;
Ming Zhang;
Liang Tang;
Jie Zhou
Show Abstract
With the proliferation of multimedia information on the network, automatic rating of web pages becomes more and more
important for web management. Obviously image analysis is very important in these kinds of tasks. But to our best
knowledge, there are no publications reported on it. In this paper, we propose a novel framework to rate webpage using
image content analysis. The rated categories are in compliance with the standard utilized by most web browsers like
Internet Explorer, which include Normal, Revealing Attire, Exposed Breasts and Bare Buttocks. To make the rating work
feasible, we analyze the images mainly using skin detection and body region, and face detection is also used as guidance
for the detection of skin and body region. After that, all the results of image content analysis are integrated to achieve
content rating results for web pages. We tested our system on two data sets and demonstrated its effectiveness.
Time-stamped planar curves matching for reconstruction of aerocraft trajectories
Author(s):
ShuWen Wang;
Jin Wang
Show Abstract
Planar mapped trajectories (curves composed of time-stamped points) matching differs to other planar curves
correspondence in stereo vision. Most methods proposed for general planar curves matching are not much suitable for it.
In this paper, a new coarse-to-fine method for planar trajectories is proposed; some physical constraints such as time,
height, speed limitation and continuousness of aerocraft trajectories are used to make matching process more reasonable.
Similarity Measures between left and right images planar trajectories are calculated for matching, flight trajectories are
reconstructed at last, and reconstruction error is also discussed at last.
Polarimetric SAR image classification based on polarimetric decompostition and neural networks theory
Author(s):
Huanmin Luo;
Ling Tong;
Xiaowen Li
Show Abstract
In this paper an classification method based on polarimetric decomposition technique and neural network theory, is
proposed for polarimetric SAR data sets. The main advantage of this polarimetric decomposition technique is to provide
dominant polarimetric scattering properties identification information where the most important kinds of scattering
medium can be discriminated. Feature vector extracted from full POLSAR data sets by polarimetric decomposition is
used as input data of the feed-forward neural network (FNN). Neural networks have the advantage to be independent to
the input signal statistics and the ability to combine many parameters in their inputs. To speed convergence and improve
stability of the FNN Kalman filter plus scaled conjugate gradient algorithm is used in the training stage. The NASA/JPL
AIRSAR c-band data of San Francisco is used to illustrate the effectiveness of the proposed approach to classification.
Quantitative results of performance are provided, as compared to the Wishart classifier.
Performance research of Gaussian function weighted fuzzy C-means algorithm
Author(s):
Xiaofang Liu;
Xiaowen Li
Show Abstract
Fuzzy C-Means (FCM) algorithm is a fuzzy pattern recognition method. Clustering precision of the algorithm is affected
by its equal partition trend for data set of large discrepancy of each class samples number, and the optimal clustering
result of the algorithm mightn't be a right partition in this case. In order to overcome this disadvantage, a Gaussian
function Weighted Fuzzy C-Means (WFCM) algorithm is proposed, which the weighted function is produced by a
Gaussian function calculating dot density of each sample. To certain extent, the WFCM algorithm has not only overcome
the limitation of equal partition trend in fuzzy Cmeans algorithm, but also been favorable convergence and stability. The
calculation of the weighted function and the choice of sample dot density range restriction value for the algorithm are
both objective. When partially supervised information obtained from a few labeled samples is introduced to the WFCM
algorithm, the classification performance of the WFCM algorithm is further enhanced and the convergent speed of
objective function is further accelerated.
From primal sketch to 2.1D sketch with contour reorganization
Author(s):
Ru-xin Gao;
Nong Sang
Show Abstract
This paper proposed a model for 2.1D sketch based on primalsketch [6,7] and a corresponding algorithm based on
SWC(Swendsen-Wang Cut)[1,2]. Given a primal sketch graph, all boundaries' color and curvature information can be
gotten from the dictionary of image primitives of the primal sketch. Suppose all curves in the sketch graph can be
combined into closed contours and then contour can be combined into regions. Calculating boundaries similarity energy
of two regions by comparing the comparability using the color and curvature information of boundaries, then the energy
is regarded as the proposal of SWC. All regions are regarded as nodes of graph space, then the 2.1D sketch problem can
be looked as graph partition problem. In each step, all curves will be reorganized into contours and then contours
generate regions. Some experiment results based on above model and algorithm are given in the paper.
On-line estimation of image jacobian matrix by improved Broyden's method in uncalibrated visual servoing
Author(s):
Xiangjin Zeng;
Xinhan Huang;
Min Wang
Show Abstract
A novel improved broyden's method has been presented to estimate image jacobian matrix for uncalibrated visual
servoing. In this paper, we apply chebyshev polynomial as a cost function to approximate best value. Compared with
recursive least square (RLS) algorithm which is restricted by the prior knowledge for obtaining some performances,
chebyshev polynomial algorithm has a great adaptability to estimate jacobian parameter, even without the prior
knowledge. A microscopic image jacobian model has been developed for the four degree-of-freedom micromanipulator
in our microassembly system. The performance has been confirmed by simulations and experiments.
Research on automatic human chromosome image analysis
Author(s):
Delie Ming;
Jinwen Tian;
Jian Liu
Show Abstract
Human chromosome karyotyping is one of the essential tasks in cytogenetics, especially in genetic syndrome diagnoses.
In this thesis, an automatic procedure is introduced for human chromosome image analysis. According to different status
of touching and overlapping chromosomes, several segmentation methods are proposed to achieve the best results.
Medial axis is extracted by the middle point algorithm. Chromosome band is enhanced by the algorithm based on
multiscale B-spline wavelets, extracted by average gray profile, gradient profile and shape profile, and calculated by the
WDD (Weighted Density Distribution) descriptors. The multilayer classifier is used in classification. Experiment results
demonstrate that the algorithms perform well.
Line feature matching algorithm
Author(s):
Taisong Jin;
Cuihua Li
Show Abstract
This paper presents a line feature matching algorithm. Firstly, it extracts the set of line features in the image, and represents
an object using attributed relational graph (ARG). By defining relation vectors between the adjacent features, the graph can
describe the structural information of an object. Secondly, the one-to-one correspondences between model features and
image features is established by two processes - coarse match and refined match through the analysis of matching ordering
and matching number of relation vectors. Finally, the object examples in the image are extracted. Test showed that the
proposed algorithm had superior performance to the present line feature matching algorithms, which is robust to shape
deformation, or input noise, and decreased the computational cost.
Three-dimensional reconstruction from the method of CAD-based photogrammetry
Author(s):
Ling Yang;
Shunyi Zheng
Show Abstract
The three-dimensional (3D) reconstruction has become a hot research field of Photogrammetry and computer vision.
This paper presents a measurement method of CAD-based Photogrammetry to do 3D reconstruction. CAD models and
images which the exterior orientation is known are used. The visible edges of a volumetric CAD model with an initial
pose are projected back into images after a hidden line analysis. This analysis determines which edges (or parts of edges)
of the object are visible, and computes the edges of the object. Observation equations are formulated that establish the
relationship between the parameterized CAD models and position of the edges in the images. The flow of this paper is as
fellows. Firstly, parameterized CAD models are used to describe 3D objects, object models can be described in various
ways, and this paper uses CSG models combined with B-rep. Secondly, fitting models to images. Manually select an
appropriate model and approximate parameters, the parameterized models are projected back into the images, and then
compute the optimal fitting automatically. Thirdly, calculate the parameters of the model. Experiments were done to box,
sphere and column, which are the base primitive of some objects. The results of these experiments show that the method
used in this paper are effective and have high precision. All primitives used in these experiments are well reconstructed.
Stick-guided lateral inhibition for enhancement of low-contrast image
Author(s):
Shengxian Tu;
Yilun Wu;
Xuesong Lu;
Hong Huo;
Tao Fang
Show Abstract
The inhibitory interaction has long been observed in the lateral eye of the Limulus and been integrated into mechanism
of enhancing contrast. When applying to the enhancement of low-contrast image for segmenting interested objects, the
original lateral inhibition model will simultaneously amplify noises while enhancing edges contrast. This paper presents
a new lateral inhibition model, which is called Stick-Guided Lateral Inhibition, for enhancement of low-contrast image
so that week edges may exert a stronger force to catch the boundary of targets in the latter segmentation. First, the guided
inhibition term is introduced as a general framework for improving the performance of lateral inhibition models in the
presence of noises. Then, by using asymmetric sticks to guide the inhibiting process, we are able to accentuate the
intensity gradients of image-edges and contours while suppressing the amplification of noises. Experiments on synthetic
images and remote sensor images show that our model significantly enhances low-contrast images and improves the
performance of latter segmentation.
Study on adaptive BTT reentry speed depletion guidance law based on BP neural network
Author(s):
Zongzhun Zheng;
Yongji Wang;
Hao Wu
Show Abstract
Reentry guidance is one of the key technologies in hypersonic vehicle research field. In addition to the constraints on its
final position coordinates, the vehicle must also impact the target from a specified direction with high precision. And
therefore the adaptability of guidance law is critical to control the velocity of hypersonic vehicle and firing accuracy
properly in different surroundings of large airspace. In this paper, a new adaptive guidance strategy based on Back
Propagation (BP) neural network for the reentry mission of a generic hypersonic vehicle is presented. Depending on the
nicer self-learn ability of BP neural network, the guidance law considers the influence of biggish mis-modeling of
aerodynamics, structure error and other initial disturbances on the flight capability of vehicle. Consequently, terminal
position accuracy and velocity are guaranteed, while many constraints are satisfied. Numerical simulation results clearly
bring out the fact that the proposed reentry guidance law based on BP neural network is rational and effective.
Logo image clustering based on advanced statistics
Author(s):
Yi Wei;
Mohamed Kamel;
Yiwei He
Show Abstract
In recent years, there has been a growing interest in the research of image content description techniques. Among those,
image clustering is one of the most frequently discussed topics. Similar to image recognition, image clustering is also a
high-level representation technique. However it focuses on the coarse categorization rather than the accurate recognition.
Based on wavelet transform (WT) and advanced statistics, the authors propose a novel approach that divides various
shaped logo images into groups according to the external boundary of each logo image. Experimental results show that
the presented method is accurate, fast and insensitive to defects.
Fuzzy selective voting classifier with defect extraction based on comparison within an image
Author(s):
Toshifumi Honda;
Ryo Nakagaki;
Obara Kenji;
Yuji Takagi
Show Abstract
Semiconductor visual inspection is necessary for production yield control. Defect classification is a key procedure in
determing defect sources. Auttomization of this procedure is required in order to achieve efficient and high-yield
production. In the present paper, an automatic defect classification (ADC) algorithm for a semiconductor inspection is
proposed. The ADC algorithm consists of the following three parts;
1) A defect extraction algorithm to achieve high-sensitivity defect extraction even in regions in which the brightness is
unstable due to optical interference at a thin layer.
2) An appearance feature calculation from a color image inside the defect region extracted from 1).
3) A unique training type classifier called the fuzzy selective voting classifier (FSVC), which calculates the weight for
each appearance feature in order to achieve accurate classification even when the discriminancy of each feature is
different.
The performance of the developed ADC algorithm has been evaluated using defect acquired from an actual production
line. The accuracy of the classification was 85.9% and the false rejection rate was 93%.
Matching SAR image to optical image using modified Hausdorff distance and genetic algorithms
Author(s):
Haicen Mao;
Qiuze Yu;
Tianxu Zhang
Show Abstract
A novel algorithm for matching synthetic aperture radar (SAR) image to Optical image based on lineal feature using
Hausdorff distance combined with genetic algorithm is proposed in this paper. A new method is presented to extract
lineal feature from low signal to noise ratio (SNR) SAR image. Based on the edge image from SAR and Optical image,
modified Hausdorff distance is adopted as a similarity measure because it is insensitive to noise. Genetic algorithm is
used as searching strategy to achieve high computation speed for its inertial parallel. Experimental results using real SAR
and Optical images demonstrate that the algorithm is robust, fast and can achieve high matching accuracy.
Object matching using weight Hausdorff distance matrix combined with genetic algorithm
Author(s):
Qiuze Yu;
Bing Yang;
Jian Liu;
Jinwen Tian
Show Abstract
A new similarity measure based on Hausdorff Distance Matrix Frobenius Norm for object matching is proposed in this
paper. This measure is more reliable and can achieve higher location accuracy compared with other measures based on
classic and modified Hausdorff Distance under the condition of high level noise and high ratio occlusion of template.
The search strategy based on genetic algorithms is employed to make algorithm faster. Experimental results under noise
of different level demonstrate high performance of the matching algorithm.
Adaptive neural network nonlinear control for BTT missile based on the differential geometry method
Author(s):
Hao Wu;
Yongji Wang;
Jiangsheng Xu
Show Abstract
A new nonlinear control strategy incorporated the differential geometry method with adaptive neural networks is
presented for the nonlinear coupling system of Bank-to-Turn missile in reentry phase. The basic control law is designed
using the differential geometry feedback linearization method, and the online learning neural networks are used to
compensate the system errors due to aerodynamic parameter errors and external disturbance in view of the arbitrary
nonlinear mapping and rapid online learning ability for multi-layer neural networks. The online weights and thresholds
tuning rules are deduced according to the tracking error performance functions by Levenberg-Marquardt algorithm,
which will make the learning process faster and more stable. The six degree of freedom simulation results show that the
attitude angles can track the desired trajectory precisely. It means that the proposed strategy effectively enhance the
stability, the tracking performance and the robustness of the control system.
3D model retrieve based on K-means clustering
Author(s):
Hui Jing;
Meifa Huang;
Yanru Zhong
Show Abstract
Owing to its fast speed, simple operation, and strong robustness, Shape Distribution is widely used in search engines.
This method, however, only considers distances between the objects' shape distribution histograms and ignores the
information included. Actually the information of the shape distribution histograms, such as the mean value, the standard
deviation, the kurtosis and the skewness, can be used to map the 3D model. As a result, the retrieval precision of Shape
Distribution is low. To enhance the retrieve efficiency, a novel method which employs the K-means clustering method is
proposed in this paper. First, the models' shape distribution histograms are established by Shape Distribution method and
are normalized as the proper format of K-means clustering method. Then, the objects' shape distribution histograms are
served as inputs of K-means clustering method and are classified into certain groups by this algorithm. Last, all the
models that belong to the classification of the query model are exported as the retrieval results. A case study is used to
validate the proposed method. Experimental results show that the retrieval precision by using the proposed method is
higher than that of the Shape Distribution method.
Efficient stellar spectral type classification for SDSS based on nonnegative matrix factorization
Author(s):
Jinfu Yang;
Zhongtian Liu;
Fuchao Wu
Show Abstract
The problem of identifying spectra collected by large sky survey telescope is urgent to study to help astronomers
discover new celestial bodies. Due to spectral data characteristics of high-dimension and volume, principle component
analysis (PCA) technique is commonly used for extracting features and saving operations. Like many other matrix
factorization methods, PCA lacks intuitive meaning because of its negativity. In this paper, non-negative matrix
factorization (NMF) technique distinguished from PCA by its use of nonnegative constrains is applied to stellar spectral
type classification. Firstly, NMF was used to extract features and compress data. Then an efficient classifier based on
distance metric was designed to identify stellar types using the compressed data. The experiment results show that the
proposed method has good performance over more than 70,000 real stellar data of Sloan Digital Sky Survey (SDSS).
And the method is promising for large sky survey telescope projects.
Vision-based 3D registration of outdoor AR system
Author(s):
Xueling Wu;
Qingyun Du;
Fu Ren
Show Abstract
One of the key technologies of Outdoor AR is the real-time 3D registration of objects in the real world. The paper puts
forward a new hardware registration method which not only borrows the ideas of identification point registration, but
improves it to realize tracking registration in video-based outdoor AR, which uses see-through head mounted display
(STHMD) loaded on outdoor AR system for showing the result of registration, and employs one color CCD camera
capturing video to obtain the world coordinate of scene border. Furthermore, the paper utilizes 3D electronic compass
and GPS attached on user's body to calculate transition matrix from the world coordinate system to the camera
coordinate system. Then, the transition matrix from the virtual coordinate system to the image plane can be calculated
out and 3D virtual object generated by computer model is added into the STHMD as a whole. Synthetically, video-based
registration offers a superior approach to 3D registration of dynamic object. Finally, the paper provides the
implementation process and designs a test. By the case study, the new method significantly simplifies the registration
system and algorithm, and coordination errors are eliminated. The algorithm requires little computation and can be easily
realized in real time without delay. Compared with the several existing registration methods, it is significantly improved.
Adaptive skin detection based on online training
Author(s):
Ming Zhang;
Liang Tang;
Jie Zhou;
Gang Rong
Show Abstract
Skin is a widely used cue for porn image classification. Most conventional methods are off-line training schemes. They
usually use a fixed boundary to segment skin regions in the images and are effective only in restricted conditions: e.g.
good lightness and unique human race. This paper presents an adaptive online training scheme for skin detection which
can handle these tough cases. In our approach, skin detection is considered as a classification problem on Gaussian
mixture model. For each image, human face is detected and the face color is used to establish a primary estimation of
skin color distribution. Then an adaptive online training algorithm is used to find the real boundary between skin color
and background color in current image. Experimental results on 450 images showed that the proposed method is more
robust in general situations than the conventional ones.
Boosting bootstrap FLD subspaces for multiclass problem
Author(s):
Tuo Wang;
Daoyi Shen;
Lei Wang;
Nenghai Yu
Show Abstract
In this paper an ensemble feature extraction algorithm is proposed based on Adaboost.M2 for multiclass classification
problem. The proposed algorithm makes no assumption about the distribution of the data and primarily performs by
directly selecting the discriminant features with the minimum training error, which can overcome the main drawbacks of
the traditional methods, such as Principle Component Analysis (PCA), Fisher Linear Discriminant Analysis (FLD) and
Nonparametric Discriminant Analysis (NDA). The proposed method first samples a large number of bootstrap training
subsets from the original training set and implements FLD in each subset to get a large number of bootstrap FLD
projections. Then at each step of Adaboost.M2, the projection with the minimum weighted K Nearest Neighbor (KNN)
classification error is selected from a pool of linear projections to combine the final strong classifier. Experimental
results on three real-world data sets demonstrate that the proposed algorithm is superior to other feature extraction
techniques.
Self-organizing shape from shading method based on hybrid reflection model
Author(s):
Lixin Tang;
Bin Xu;
Hanmin Shi
Show Abstract
In this paper we present a novel self-organizing shape from shading method based on hybrid reflection model which
includes diffuse and specular reflection components. Using this method, the shape of an object surface is recovered in
two steps. Firstly, a grayscale image of the surface is separated into diffuse and specular components, and a new image
only composed of diffuse component is created. Secondly, a self-organizing shape from shading algorithm is performed
for the new image, and the 3D shape of the surface is generated. The experimental results for synthetic and real images
demonstrate the availability and feasibility of our method.
Research on multi-class classification of support vector data description
Author(s):
Minghua Shen;
Huaitie Xiao;
Qiang Fu
Show Abstract
Support Vector Data Description (SVDD) is a one-class classification method developed in recent years. It has been used
in many fields because of its good performance and high executive efficiency when there are only one-class training
samples. It has been proven that SVDD has less support vector numbers, less optimization time and faster testing speed
than those of two-class classifier such as SVM. At present, researches and acquirable literatures about SVDD multi-class
classification are little, which restricts the SVDD application. One SVDD multi-class classification algorithm is proposed
in the paper. Based on minimum distance classification rule, the misclassification in multi-class classification is well
solved and by applying the threshold strategy the rejection in multi-class classification is greatly alleviated. Finally, by
classifying range profiles of three targets, the effect of kernel function parameter and SNR on the proposed algorithm is
investigated and the effectiveness of the algorithm is testified by quantities of experiments.
Adaptive K-means clustering algorithm
Author(s):
Hailin Chen;
Xiuqing Wu;
Junhua Hu
Show Abstract
Clustering is a fundamental problem for a great variety fields such as pattern recognition, computer vision. A popular
technique for clustering is based on K-means. However, it suffers from the four main disadvantages. Firstly, it is slow
and scales poorly on the time. Secondly, it is often impractical to expect a user to specify the number of clusters. Thirdly,
it may find worse local optima. Lastly, its performance heavily depends on the initial clustering centers. To overcome the
above four disadvantages simultaneously, an effectively adaptive K-means clustering algorithm (AKM) is proposed in
this paper. The AKM estimates the correct number of clusters and obtains the initial centers by the segmentation of the
norm histogram in the linear normed space consisting of the data set, and then performs the local improvement heuristic
algorithm for K-means clustering in order to avoid the local optima. Moreover, the kd-tree is used to store the data set for
improving the speed. The AKM was tested on the synthetic data sets and the real images. The experimental results
demonstrate the AKM outperforms the existing methods.
Some properties of the fuzzy equivalence matrices
Author(s):
Jun Zhang;
Bing Yang;
Xiaomao Liu;
Xinxin Zhu;
Shaoqun Zeng
Show Abstract
In fuzzy clustering, we always use fuzzy equivalence matrix to find the clustering results. Although fuzzy equivalence
matrix is one of the most important tools in fuzzy clustering, it's hard to judge whether a fuzzy matrix is a fuzzy
equivalence matrix directly. In this paper, some simple and useful properties of the fuzzy equivalence matrices have been
proposed based on the basic properties proposed in the classical articles. Using these properties, the judgment whether a
fuzzy matrix is a fuzzy equivalence matrix may become an easy work.
Accuracy improvement for 3D shape measurement system based on gray-code and phase-shift structured light projection
Author(s):
Xiaobo Chen;
Juntong Xi;
Ye Jin
Show Abstract
Accuracy is one of the most important features for a 3D shape measurement system based on gray-code and phase-shift
structured light projection and it requires accurate modeling and calibration of the camera and projector that consist of
the system. In this paper, a new method is proposed to reconstruct the 3D object without the inverse projector model,
thus make it possible to use a more accurate projector model considering high to 4th order radial and tangential lens
distortion. Moreover, we propose a new system calibration method which can calibrate the camera and projector
simultaneously based on the same reference points to eliminate the disadvantage of the conventional two-step calibration
methods that the accuracy of the projector calibration is always influenced by the camera calibration error. The
experiments compare the performance of our proposed model and calibration method with the conventional ones and the
results are presented.
Improved bi-lateral filter in high dynamic range compression
Author(s):
Zhijiang Li;
Jing Huang;
Qiang Wang
Show Abstract
The dynamic range of many real-world environments exceeds the capabilities of current display technology by several
orders of magnitude. To obtain reasonable reproduction, a large number of high-quality tone-mapping operators are
currently available. All of these methods can be divided into three kinds: global operation, local operation and temporal-related
algorithm. As an effective local operation to avoid halo artifacts, bi-lateral filter is presented and discussed in
recent years. After analysis in-depth, this paper presents an improved bi-lateral filter in high dynamic range compression
focused on four points: calculating efficiency, vision theory support, scales and parameters. Experiments indicate that the
new operator can generate reasonable reproduction of high dynamic range images.
Study on for soluble solids contents measurement of grape juice beverage based on Vis/NIRS and chemomtrics
Author(s):
Di Wu;
Yong He
Show Abstract
The aim of this study is to investigate the potential of the visible and near infrared spectroscopy (Vis/NIRS) technique for
non-destructive measurement of soluble solids contents (SSC) in grape juice beverage. 380 samples were studied in this paper.
Smoothing way of Savitzky-Golay and standard normal variate were applied for the pre-processing of spectral data. Least-squares
support vector machines (LS-SVM) with RBF kernel function was applied to developing the SSC prediction model based on the
Vis/NIRS absorbance data. The determination coefficient for prediction (Rp2) of the results predicted by LS-SVM model was 0. 962
and root mean square error (RMSEP) was 0. 434137. It is concluded that Vis/NIRS technique can quantify the SSC of grape juice
beverage fast and non-destructively.. At the same time, LS-SVM model was compared with PLS and back propagation neural network
(BP-NN) methods. The results showed that LS-SVM was superior to the conventional linear and non-linear methods in predicting SSC
of grape juice beverage. In this study, the generation ability of LS-SVM, PLS and BP-NN models were also investigated. It is
concluded that LS-SVM regression method is a promising technique for chemometrics in quantitative prediction.
New point matching algorithm for panoramic reflectance images
Author(s):
Zhizhong Kang;
Sisi Zlatanova
Show Abstract
Much attention is paid to registration of terrestrial point clouds nowadays. Research is carried out towards improved
efficiency and automation of the registration process. The most important part of registration is finding correspondence.
The panoramic reflectance images are generated according to the angular coordinates and reflectance value of each 3D
point of 360° full scans. Since it is similar to a black and white photo, it is possible to implement image matching on this
kind of images. Therefore, this paper reports a new corresponding point matching algorithm for panoramic reflectance
images. Firstly SIFT (Scale Invariant Feature Transform) method is employed for extracting distinctive invariant features
from panoramic images that can be used to perform reliable matching between different views of an object or scene. The
correspondences are identified by finding the nearest neighbors of each keypoint form the first image among those in the
second image afterwards. The rigid geometric invariance derived from point cloud is used to prune false correspondences.
Finally, an iterative process is employed to include more new matches for transformation parameters computation until the
computation accuracy reaches predefined accuracy threshold. The approach is tested with panoramic reflectance images
(indoor and outdoor scenes) acquired by the laser scanner FARO LS 880. 1
Two dimensional LDA using volume measure in face recognition
Author(s):
Jicheng Meng;
Li Feng;
Xiaolong Zheng
Show Abstract
The classification criterion for the two dimensional LDA (2DLDA)-based face recognition methods has been little
considered, while we almost pay all attention to the 2DLDA-based feature extraction. The typical classification measure
used in 2DLDA-based face recognition is the sum of the Euclidean distance between two feature vectors in feature
matrix, called traditional distance measure (TDM). However, this classification criterion does not match the high
dimensional geometry space theory. So we apply the volume measure (VM), which is based on the high dimensional
geometry theory, to the 2DLDA-based face recognition in this paper. To test its performance, experiments were
performed on the YALE face databases. The experimental results show the volume measure (VM) is more efficient than
the TDM in 2DLDA-based face recognition.
Forest fire and smoke detection based on video image segmentation
Author(s):
Dengyi Zhang;
Aike Hu;
Yujie Rao;
Jinming Zhao;
Jianhui Zhao
Show Abstract
There are reported methods for fire or smoke recognition respectively. But in forest, fire and smoke always exist together,
and fire area lies within smoke area. Both fire and smoke are important features for fire detection. Based on this fact we
present a novel method to detect fire and smoke in two steps and obtain areas of fire and smoke together. With the help
of Otsu method taking gray value and red value as inputs, fire and smoke regions are segmented from the background,
and regions with very small areas are deleted as noises; then fire is segmented from the left large and continuous regions.
Area, roundness and contour of segmented results are used to further recognize smoke or fire from other regular objects,
or to describe the status and trend of forest fire.
3D surface texture synthesis using improved graph cuts
Author(s):
Junyu Dong;
Li Li;
Sha Ma
Show Abstract
This paper introduces an improved graph cut approach and combines it with commonly used surface texture representations
for synthesis. Unlike graph cut which supports iterative refinement for improvement of the patch seams, the improved graph
cut method re-synthesis the max error overlap region using k-nearest neighbor method. This paper follows a simple 3D
surface texture synthesis framework, which comprises three steps: capture, synthesis and relighting. Firstly, it represents 3D
surface textures under varied illumination directions using gradient and albedo maps. Then, it uses the surface representation
to synthesis a description of a larger surface. Finally, it renders the surface representation. It proposes an improved graph cut
method for the synthesis step, and the proposed method can generate perceptually smooth images of 3D surface texture from
small samples.
Robust face recognition using gradient map and Hausdorff distance measure
Author(s):
Jing Chi;
DuanSheng Chen
Show Abstract
A gradient-based face recognition method using Partial Hausdorff Distance (PHD) measure is proposed in this paper.
First, in order to achieve a performance independent of lighting conditions, the image is transformed into a Gradient Map
(GM). And then, Hausdorff distance measure is introduced to calculate the dissimilarity between two Gradient Maps. The
experimental data show that the measure is suitable for face recognition. As we can see later, this distance measure is
robust to lighting variations, slight pose differences and expression changes in face images. At last, recognition accuracy
is given tested on AR and FERET databases, and comparisons with Edge Map (EM) and Line segment Edge Map (LEM)
approaches are also presented.
Slide projector calibration based on calibration of digital camera
Author(s):
Jun Tao
Show Abstract
With the development of non-contact measurement in the close-range photogrammetry, the use of the projector becomes
frequent and familiar. In order to take full advantages of the slide projector, the slide projector is steered on the basis of
the traditional method of the digital camera taking images. The paper proposes a flexible and effective technique for the
slide projector calibration based on the calibration of digital camera. The technique only requires a planar grid, a rotating
platform and a computer. The planar grid is put on the center of the rotating platform functioned as a control ground. The
computer can control the rotating platform to rotate regularly and dominate the slide projector and the digital camera to
project slides and take images automatically and synchronously. The algorithm with 2D direct linear transformation (2D-DLT)
and collinear equations is used to calibrate the digital camera first. By the similar algorithm above, the interior
parameters of the slide projector can be determined too. Then the calibration of the slide projector is achieved finally.
The operation method in detail and the algorithm are addressed systematically and entirely. The feasibility and the
exactness of the technique for the calibration of the slide projector based on the digital camera calibration put forward in
this paper are verified by the results of real experimental data.
Improved self-calibration algorithm of absolute dual quadric
Author(s):
Min Sun
Show Abstract
Camera self-calibration is one key problem in 3D reconstruction from image sequences. In this paper, a further
discussion is given for self-calibration method of Absolute Dual Quadric (ADQ). This method using projective matrix as
known data to extract camera intrinsic parameters, however, projective matrix still contains some uncertain variables,
and optimization to them were ignored, in addition, the utilities of constraint conditions are not convincible in nonlinear
algorithm of this method. An improved algorithm is given to resolve these problems in this paper. The main idea of the
improved algorithm is to recalculate projective matrix with middle calibration result in each iterative process of the
algorithm, and set convergence of iterative process to the point on parabolas where the ratio of initial focal length to its
result reach to 1.0. Experiment result from real image data shows that improved algorithm is efficient.
Ontological concept extraction based on image understanding and describing of remote sensing domain
Author(s):
Liang Zhong;
Hongchao Ma;
Pengfei Liu
Show Abstract
When using ontological theory to set up remote sensing image knowledge system, the majority of scholars by now
regard ontology as logical theory for defining the object, attribution relation, affair and process of remote sending
knowledge system. But understanding and describing the real world makes that logical theory be unable to unify
concepts with the same practical meaning from different concept models, so bring grid service system drawbacks in
knowledge delivering and sharing. To solve that issue requires further improvement of the defining method and model
for concepts in ontology. This paper presents a neural network remote sensing image ontological concept extraction
model based on image understanding and describing, utilize the theory of bionic optimization, and adopts the
combination of artificial neural network with the rule-based knowledge Recognition System. Realize the knowledge
delivering and sharing among different information systems or make the knowledge delivering and sharing between
client and system possible and effective.
Fuzzy-rough membership function neural network and its application to pattern recognition
Author(s):
Dongbo Zhang;
Yaonan Wang;
Huixian Huang
Show Abstract
Generally, while designing pattern classifier, the boundaries between different classes are vague and it is often difficult
or impossible to acquire all of the necessary essential features for precisely classifying, so often both the fuzzy
uncertainty and rough uncertainty are exist in classification problems. In this work, a novel FRMFN (Fuzzy-Rough
Membership Function Neural Network) is built based on fuzzy-rough sets theory. The FRMFN integrates the ability of
processing fuzzy and rough information simultaneously. The test results of classification for infrared band combination
image of Canada Norman Wells area and five vowel characters indicate that FRMFN has better classification precision
than RBFN (Radial Basis Function Neural Network) and has the same merit of quick learning as RBFN.
Hyperspectal RS image classification based on kernel methods
Author(s):
Guopeng Yang;
Hangye Liu;
Xuchu Yu
Show Abstract
Hyperspectral RS technology organically combines the radiation information and the space information. The spectrum
information, which the hyperspectral image enriches, can be better to carry on the ground target classification, compare
with panchromatic remote sensing image and multispectral remote sensing image. As support vector machine was
applied to many fields successfully recent years, using kernel methods, the classic linear methods can cope with the
nonlinear problem, which was called the 3rd revolution of pattern analysis algorithms. This paper introduced two
classifying methods for hyperspectral image based on kernel function, Support Vector Machine and Kernel Fisher
Discriminant Analysis, and studied the selection of kernel function and its parameters as well as multi-class
decomposition. We use radial basic function kernel, one against one or one against rest decomposition methods to
construct multi-class classifier, and optimize parameter selection using cross-validating grid search to build an effective
and robust kernel classifier. It is verified that, through the OMIS and AVIRIS image classifying experiments, comparing
with common image classifying methods, kernel classifying method can avoid Hughes phenomenon, thus improve the
classifying accuracy.
Object detection based on 2D canonical correlation analysis
Author(s):
Guofeng Zhang;
Weida Zhou;
Weihua Ren
Show Abstract
A novel approach combining 2DCCA, edge detector, and corner detector for object detection is proposed in this paper.
The detection system consists of two stages. In the first stage, edge and corner information is obtained by edge detector
and corner detector. By setting range for the number of edge pixel and corner in the scanning window, a large number of
non-object windows are rejected. In the second stage, the classifier trained by 2DCCA is combined with slide window
method so that further non-object windows are rejected. For the case that one object is simultaneously contained in
several windows, the algorithm of determining the best position of object is designed. Compared with related approaches,
our method has advantage of obtaining higher precision under the similar recall. The performance of the proposed
approach is illustrated by experimental results.
Visualization analysis platform based on land surface remote sensing radiation transfer characteristics
Author(s):
Song Wu;
Qinhuo Liu;
Huaguo Huang;
Xiaozhou Xin;
Min Chen
Show Abstract
We outline a platform for rendering the canopy reflected irradiance under the irradiation of different wavelengths. Our
approach is to use different colors to symbol value of canopy reflected irradiance in different ranges. Meanwhile, we do
some statistical analysis according to the results above. We illustrate our approach for simple 3-D trees scenes under the
irradiation of 6 different wavelengths, respectively in visible light waveband and near infrared waveband. Our results are
good. We get the vivid light shadow effect in the representation of 3-D trees scenes. We use 6 colors to represent 6
consecutive canopy reflected irradiance range and make some statistical analysis to get useful information from the
results above.
Algorithm of orthogonal bi-axle for auto-separating of watermelon seeds
Author(s):
Yong Sun;
Miao Guan;
Daoqin Yu;
Jing Wang
Show Abstract
During the process of watermelon seeds characteristic extraction as well as separation, watermelon seeds' major and
minor axes, the length and width ratio have played a very important role in appearance regulating degree evaluation. It is
quite difficult to find the answer of orthogonal bi-axes because the watermelon seeds are flat and irregular in shape and
what's more there is no rule to follow. After a lot of experiments and research, the author proposed the algorithm of
orthogonal bi-axes algorithm for granulated object. It has been put into practice and proved in the application of auto-separation
system for watermelon seeds. This algorithm has the advantage of lower time complexity and higher precision
compared with other algorithms. The algorithm can be used in the solution of other similar granulated objects, and has
the widespread application value.
Discrimination of rapeseed and weeds under actual field conditions based on principal component analysis and artificial neural network by VIS/NIR spectroscopy
Author(s):
Min Huang;
Yidan Bao;
Yong He
Show Abstract
The study documented successful discrimination between five weed species and rapeseed plants under actual field
conditions using visible and near infrared (Vis/NIR) spectroscopy. A hybrid recognition model, BP artificial neural
networks (BP-ANN) combined with principal component analysis (PCA), had been established for discrimination of
weeds in rapeseed field. Spectra tests were performed on the rapeseed and five-weed species canopy of 180 samples in
the field using a spectrophotometer (325-1075 nm). 6 optimal PCs were selected as the input of BP neural networks to
build the prediction model. Rapeseed samples were marked as 1, while the five weed species marked as 2, 3, 4, 5, 6,
which were used as output set of BP-ANN. 120 samples were randomly selected as the training set, and the remainder as
prediction set. It showed excellent predictions with the correlation value of 0.9745, and the relative standard deviation
(RSD) was under 5% thus 100% of prediction accuracy was achieved. The results are promising for further work in
real-time identification of weed patches in rapeseed fields for precision weed management.
Variety identification of brown sugar using short-wave near infrared spectroscopy and multivariate calibration
Author(s):
Haiqing Yang;
Di Wu;
Yong He
Show Abstract
Near-infrared spectroscopy (NIRS) with the characteristics of high speed, non-destructiveness, high precision and
reliable detection data, etc. is a pollution-free, rapid, quantitative and qualitative analysis method. A new approach for
variety discrimination of brown sugars using short-wave NIR spectroscopy (800-1050nm) was developed in this work.
The relationship between the absorbance spectra and brown sugar varieties was established. The spectral data were
compressed by the principal component analysis (PCA). The resulting features can be visualized in principal component
(PC) space, which can lead to discovery of structures correlative with the different class of spectral samples. It appears to
provide a reasonable variety clustering of brown sugars. The 2-D PCs plot obtained using the first two PCs can be used
for the pattern recognition. Least-squares support vector machines (LS-SVM) was applied to solve the multivariate
calibration problems in a relatively fast way. The work has shown that short-wave NIR spectroscopy technique is
available for the brand identification of brown sugar, and LS-SVM has the better identification ability than PLS when the
calibration set is small.
Application of effective wavelengths and BP neural network for the discrimination of varieties of instant milk tea powders using visible and near infrared spectroscopy
Author(s):
Fei Liu;
Yong He;
Li Wang
Show Abstract
In order to implement the fast discrimination of different milk tea powders with different internal qualities, visible and
near infrared (Vis/NIR) spectroscopy combined with effective wavelengths (EWs) and BP neural network (BPNN) was
investigated as a new approach. Five brands of milk teas were obtained and 225 samples were selected randomly for the
calibration set, while 75 samples for the validation set. The EWs were selected according to x-loading weights and
regression coefficients by PLS analysis after some preprocessing. A total of 18 EWs (400, 401, 452, 453, 502, 503, 534,
535, 594, 595, 635, 636, 688, 689, 987, 988, 995 and 996 nm) were selected as the inputs of BPNN model. The
performance was validated by the calibration and validation sets. The threshold error of prediction was set as ±0.1 and an
excellent precision and recognition ratio of 100% for calibration set and 98.7% for validation set were achieved. The
prediction results indicated that the EWs reflected the main characteristics of milk tea of different brands based on
Vis/NIR spectroscopy and BPNN model, and the EWs would be useful for the development of portable instrument to
discriminate the variety and detect the adulteration of instant milk tea powders.
Feature extraction technology of 3D geometric model based on STEP-NC
Author(s):
Jie Zhou;
Tianrui Zhou;
Haipeng Pan
Show Abstract
To help solve the problems existed in current RP systems using STL data model, which are low precision, hard to
distribute efficiently among various CAX systems, and to establish some fundament for RP system based on STEP-NIC,
this article studied the gathering method of 3-d geometric model feature of STEP-NC. Based on the in-depth analysis of
AP203 neutral file's structure and its description language EXPRESS, a data gathering module for AP203 neutral
exchange file was made in the form of a dynamic link library, achieved the goal of correctly acquiring the STEP-NC 3-d
CAD model's geometric and topologic feature information. This provides the fundament for achieving slicing of
STEP-NC and developing corresponding RP system.