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- Front Matter: Volume 8004
- Recognition and Computer Vision
Front Matter: Volume 8004
Front Matter: Volume 8004
Show abstract
This PDF file contains the front matter associated with SPIE Proceedings Volume 8004, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing and Introduction.
Recognition and Computer Vision
Transfer estimation and the applications in data stream classification
Show abstract
Transfer estimation is a new parameter estimation method, which utilizes samples from not only the target distribution
but also related ones. It is based on traditional estimators and can be used to solve the problem of small sample
estimation. The key problem in transfer estimation is how to set the weighting coefficients when designing the transfer
estimators. In this paper, we will propose a new algorithm to solve this problem. First, we will propose a common rule.
Following the proposed rule, we can formulate the weighting coefficient setting problem as a constrained optimization
problem. We will introduce an alternating optimized method and get a new algorithm of transfer estimation. The
estimation of evolving class priors in data stream classification is a typical and important small sample estimation
problem. In this paper, we will apply the proposed transfer estimation algorithm to the class prior estimation.
Experiments on benchmark data sets will be performed, which show that the proposed algorithm can improve the
performance on both class prior estimation and the final data stream classification.
A fast randomized clustering method based on a hypothetical potential field
Yonggang Lu,
Li Liao,
Ruhai Wang
Show abstract
A novel randomized clustering method is proposed to overcome some of the drawbacks of Mean Shift method. A
hypothetical potential field is constructed from all the data points. Different from Mean Shift which moves the kernel
window towards high-density region, our method moves the kernel window towards low-potential region. The proposed
method is evaluated by comparing with both Mean Shift and K-means++ on three synthetic data sets which represent the
clusters of different sizes, different shapes and different distributions. The experiments show that our method can
produce more accurate results than both Mean Shift and K-means++.
A weak component approach of subspace analysis
Show abstract
In the linear discriminative analysis, especially in the high dimension case, it is insufficient to project the data onto a
one-dimensional subspace for the two-category classification problem. Therefor a weak component approach (WCA)
was proposed to project patterns to a low dimensional subspace with rich number of classification features. The role of
the weak component in pattern classification was discussed. And the abundance of discriminative information contained
in weak components was explored. Firstly, a definition of the weak component was given. Secondly, an improved
regularization method was proposed. The regularization is a biased estimate of the variance in the corresponding
dimension of the training data and the population data. Then a construction method of the feature subspace based on
weak component was given, which extracts the eigenvector of the scatter matrixes according to their discriminative
information. Finally, the proposed approach was validated in the experiments by comparing it with LDA. A better
classification accuracy of the presented method was achieved. As WCA extracts the dims on which the data distributes
closer, it is applicable to the high-dimensional data which distributes elliptically.
A fingerprint matching algorithm based on probabilistic graphical model and three-tree model
Xiang Fu,
Junjie Bian,
Chongjin Liu,
et al.
Show abstract
Fingerprint matching is the most important part in the field of fingerprint recognition. In this paper, a novel fingerprint
matching algorithm based on the probabilistic graphical model and 3-tree model is proposed. First, minutiae matching
problems are considered as a special point-set matching. Fingerprint minutiae are viewed as random variables. Each
minutia pairs have some probability to be matched. Second, an algorithm is proposed to generate the graphical model
and choose "signal points", which dynamically have corresponding points in other point set. We choose three base
minutiae pairs as signal pairs. Third, the model is converted into a Junction Tree. A 3-tree model is built and the
potentials of other minutiae pairs are calculated through Junction Tree (J.T.) algorithm. Then we translate the matching
problem into the best matching problem of a weighted bipartite graph. Finally, the number of common matching pairs
can be got through maximum flow algorithm. The similarity of two fingerprints is evaluated using the number of
common matching pairs and the maximal posteriori probability. In order to deal with part-matching problems, we use the
smallest convex hull which contains all the matched minutiae. Experiments evaluated on FVC 2004 show both
effectiveness and efficiency of our methods.
A novel algorithm for cut shot boundary detection
Peng Sun,
Yinan Na,
Jie Zhou
Show abstract
Cut is a common type of shot boundary. In previous literature, frame pair similarity (FPS) was usually used as building
block of cut detector. For a given frame, one needs to determine what frame pairs in its adjacency to select and how to
combine the FPS values. To do these, previous methods, including Cross Analysis, Full Analysis, Graph Cut, etc, resort
to human experience and thus lead to inferior performance. Besides, they are susceptible to noise caused by flashlight or
popup subtitle such that additional procedure is needed to suppress such noise. In this paper we propose a novel
framework to address these problems. Both the frame pair selection and the similarity values combination are done via
machine learning. Features insensitive to flashlight or popup subtitle are extracted by exploiting the color histogram
based FPS. Experimental results on TRECVID2003~2005 datasets demonstrate the effectiveness of the proposed
algorithm.
Multi-view image matching algorithm based on Chang'e-1 lunar image
He Yu,
Xu Qing,
Xing Shuai
Show abstract
This paper studied an algorithm on image matching based on Chang'e-1 CCD image, the model used the three-line-array
image to do image matching and the constraint condition of the quasi-epipolar line on the course of matching. The
experiment proved the model is very availably and can get better effect.
Personalized tag prediction via social influence in social networks
Show abstract
Currently, social tagging systems have been adopted by many social websites. As tags help users to
browse social content effectively, personalized tag prediction problem becomes important in social
networks. In this paper, we present a new generative probabilistic model to solve personalized tag
prediction problem. Differently with previous methods, we consider social influence between users
and friends into this model. We bring two major contributions: 1) We propose a new probabilistic
model which considers in social influence to describe users' actual tagging activities; 2) Based on
this model, we propose a new approach to perform personalized tag prediction task. Experimental
results on a real-world dataset crawled from Last.fm show that our method outperforms other
methods.
Robust mean shift tracking with improved background-weighted histogram
Show abstract
Tracking objects in videos using mean shift technique has brought to public attention. In this paper, we developed an
improved tracking algorithm based on the mean shift framework. To represent the object model more accurately, the
motion direction of the object which was estimated by the local motion filters was employed to weight the histogram.
Besides, a wise object template updating strategy was proposed to adapt to the change of the object appearance caused
by noise, deformation or occlusion. The experimental results on several real world scenarios shows that our approach has
an excellent tracking performance comparing with the background weighted histogram mean shift tracking approach and
traditional mean shift tracking method.
Multiplicative multifractal modeling method for HF sea clutter
Show abstract
Accurate modeling of sea clutter and robust detection of low observable targets within sea clutter are the important
problems in the field of remote sensing and radar signal processing. In this paper, a multiplicative multifractal process is
introduced for the modeling sea clutter amplitude time series; a double parameter model which outperforms the double
exponential model and Gaussian model is proposed and applicable for distribution fitting for multiplier of multiplicative
multifractal modeling of real sea clutter. In addition, a short time generalized dimension spectrum based measurements
of matching goodness is proposed and analyzed carefully, new model for HF sea clutter is built and reasonability of
STGDs based measurements has been proven by simulation analysis. This modeling is computable rapidly and
applicable in the research of target detection in HF sea-clutter background.
Image super-resolution enhancement based on online learning and blind sparse decomposition
Show abstract
This paper presents a different learning-based image super-resolution enhancement method based on blind sparse
decomposition, in order to improve its resolution of a degraded one. Firstly, sparse decomposition based image super-resolution
enhancement model is put forward according to the geometrical invariability of local image structures under
different conditions of resolution. Secondly, for reducing the complexity of dictionary learning and enhancing adaptive
representation ability of dictionary atoms, the over-complete dictionary is constructed using online learning fashion of
the given low resolution image. Thirdly, since the fixed sparsity of the conventional matching pursuit algorithms for
sparse decomposition can not fit all types of patches, the approach to sparse decomposition with blind sparsity can
achieve relatively higher accurate sparse representation of an image patch. Lastly, atoms of high resolution dictionary
and coefficients of representation of the given low-resolution image are synthesized to the desired SR image.
Experimental results of the synthetic and real data demonstrate that the suggested framework can eliminate blurring
degradation and annoying edge artifacts in the resulting images. The proposed method can be effectively applied to
resolution enhancement of the single-frame low-resolution image.
A fast template matching method based on context prediction
Show abstract
This paper presents a novel fast template matching algorithm based on context prediction. The predicted regions are
those windows that contain the current entire sub-window. Comparison skipping or comparison terminating is executed
when a low bound of distance which has been calculated between the template and the window exceeds the threshold.
Experimental results and theory analyses prove the proposed method is faster than the conventional fast template
matching method, strictly guaranteeing the same accuracy and up to maximal twenty times faster than the SSDA.
Reference image generation based on three-dimensional realistic scene modeling for contour-based infrared ATR algorithm
Show abstract
A reference image creating approach is proposed in this paper for contour-based infrared ATR algorithm. The approach
based on three-dimensional realistic scene modeling consists of five parts: three-dimensional scene model construction,
view plane simulation, coordinates transformation and perspective projection, visible-surface identification and three-dimensional
rendering algorithm, contour extraction algorithm. By using this approach, a distinctively reference image is
rapidly generated at multiple objective views and different scales. Numerical experiments are chosen to test proposed
method from velocity performance. The experimental results indicate that the performance of the proposed technique is
better than that of other methods.
Improved SIFT algorithm with camera orientation
ShaoHua Zhang,
ZhiYuan Qin,
DongJuan Fang
Show abstract
Image matching is one of the key technologies in image processing applications,and the problem about the matching
between two images which have large tile is also a difficult point. Aim at this problem,the SIFT and ASIFT algorithm are
studied, besides, the characteristic of aerial images is considered,revise tile angle and translation variable of the Imaging
Model in ASIFT algorithm. Partial solution to the problem of automatic matching of aerial images which have large tile,
and bring out the experimental results.
Extending the depth-of-field of incoherent imaging system through wave-front coding by using composite phase masks
Hui Zhao,
Hongwei Yi,
Yingcai Li
Show abstract
Since 1995 when wave-front coding was first proposed by Dowski and Cathey, this technique has become a powerful
tool to extend the DOF (depth of field) of incoherent imaging systems. By introducing a phase mask to the aperture plane,
the optical transfer function (OTF) can be made insensitive to defocus and thus the key to this technique lies in the
design of suitable phase masks. So far, many phase masks have been proposed to achieve the goal of DOF extension. In
this paper, a new kind of phase mask, called composite phase mask, is designed by combining existing phase masks.
With the optimized parameters, the composite phase mask can be made even less sensitive to defocus than any single
mask. At the same time, as the number of phase masks used to generate one composite phase mask increases, the DOF
extension effect can also be changed. Both the simulations and experiments demonstrate the effectiveness of the
composite phase masks and the work reported also enriches the phase mask family used to realize the large DOF effect.
Image matching by affine speed-up robust features
Show abstract
Affine invariant image comparison is always consequential in computer vision. In this paper, affine-SURF (ASURF) is
introduced. Through a series of affine transformations and feature extraction, the matching algorithm becomes more
robust with the view and scale change. A kd-tree structure is build to store the feature sets and BBF search algorithm is
used in feature matching, then duplicates are removed by the conditional of Euclidean distance ratio. Experiments show
it has a good result, comparisons with SIFT and SURF is made to prove its performance.
Robust nonlinear dimensionality reduction by topologically constrained semi-isometric embedding
Show abstract
Real world data is usually high dimensional, and dimensionality reduction can significantly improve the efficiency of
data processing and analysis. Existing approaches relying on distances between neighboring features typically suffer
from the unreliable estimation of the true distance on a feature manifold due to its non-convexity. An approach is
proposed to solve the problem by discarding long geodesics poisoned by boundary points indiscriminately. However,
despite the improved performance, there are two major shortcomings with the approach. First, many long geodesics
poisoned by few boundary points, which contribute little to the distortion of a manifold, are thrown away, as may
decrease the robustness without improving the distortion of the manifold. Second, since short geodesics are sensitive to
noise, retaining the whole effect of them may result in the bad robustness. This paper presents a regularization
framework for nonlinear dimensionality reduction that incorporates long geodesics poisoned by few boundary points and
reduces the effect of short geodesics, to realize isometry largely. In addition, the approach is sensitive to non-uniform
sampling. To cope with the issue, we describe an improved robust boundary detection method. Experimental results are
presented to illustrate the better performance of the proposed algorithm on two standard data sets.
A novel radiometric projector compensation algorithm based on Lambertian reflection model
Show abstract
In this paper, a novel radiometric compensation algorithm based on Lambertian reflection model is proposed to
neutralize the visual perception of colors and textures which are intrinsic to the projection display surface. The algorithm
uses a calibration method of advanced coded structure light to determine the geometric mapping between corresponding
points in the projector-camera system. Concrete analysis of the dynamic range of projectors is applied to obtain the
intensity limits of the input image. Consequently, the Lambertian reflection model is constructed for each point to
compute the radiometric compensation function between displayed images and camera captured images. Experimental
results show that this algorithm can effectively correct color inaccuracy of projection on arbitrary textured surfaces, and
observers almost hard to notice visible artifacts of projected images.
Adaptive gesture recognition combining HMM models and geometrical features
Pu Cheng,
Jie Zhou
Show abstract
Hand gesture recognition is receiving more and more attentions due to its potential use in many applications. In this
paper, we propose a novel gesture spotting and recognition method, which combines the information of hand motion
parameter, the matching result of HMM models and the recognition result based on geometrical features of hand
trajectory to spot and recognize the gesture. Besides, we also study the method of adjusting classifiers to make the
gesture recognition system adapt to specific users. Experimental results have proved the effectiveness of the proposed
method.
Low-resolution facial image restoration based on sparse representation
Show abstract
In this paper, a strategy of reconstructing high resolution facial image based on that of low resolution is put
forward. Rather than only relying on low resolution input image, we construct a face representation dictionary
based on training high resolution facial images to compensate for the information difference between low and
high resolution images. This restoration is realized through enrolling a low resolution facial image dictionary
which is acquired through directly downsampling the learned high resolution dictionary. After the representation
coefficient vector of a low resolution input image on low resolution dictionary is obtained through ℓ1-optimization
algorithm, this coefficient can be transplanted into high resolution dictionary directly to restore the high resolution
image corresponding to input face. This approach was validated on the Extended Yale database.
Single image super-resolution using sparse prior
Show abstract
Obtaining high-resolution images from low-resolution ones has been an important topic in computer vision field. This is
a very hard problem since low-resolution images will always lose some information when down sampled from high-resolution
ones. In this article, we proposed a novel image super-resolution method based on the sparse assumption.
Compared to many existing example-based image super-resolution methods, our method is based on single original low-resolution
image, i.e. our method does not need any training examples. Compared to other interpolation based approach,
like nearest neighbor, bilinear or bicubic, our method takes advantage of the inner properties of high-resolution images,
thus obtains a better result. The main approach for our method is based on the recently developed theory called sparse
representation and compress sensing. Many experiments show our method can lead to competitive or even superior
results in quality to images produced by other super-resolution methods, while our method need much fewer additional
information.
Improved Fourier descriptors in model-based pose estimation
Show abstract
We use Fourier descriptors (FD's) to represent shape in model-based pose estimation. Specific invariance and
normalization requirements for shape descriptors concerning to pose estimation are proposed. FD's are improved to meet
such requirements. Common issues and techniques for such application are focused on. Starting point of the shape
contour is fixed to the upper left corner point. Distance pairs instead of edge coordinate pairs recast the shape sequence.
Moving average filtering is proceeded to remove the noises of the shape sequence. Shape sequence is re-sampled to
make it definite length. FD's amplitude is normalized to the range of 0 to 1. Variance of sequences between observed
and library FD's is defined as the shape matching objective function. For simulation, we use a missile model of
Milkshape3d format. Results show that by adopting the improved FD's, we can arrive at a pose estimation practically by
randomly optimal search of library FD's.
Detecting skin colors under varying illumination
Leyuan Liu,
Rui Huang,
Saiyong Yang,
et al.
Show abstract
Skin color has been used as an important cue for various human related computer vision applications. However,
detecting skin colors under varying illumination is a challenging task, as the appearance of skin in an image highly
depends on the illumination under which the image was taken. To this end, a method for detecting skin colors under
varying illumination is proposed in this paper. First, spatial illumination variation is identified and the images are
segmented into different regions with different illumination. Each illumination region of color images are corrected base
on the illuminant estimated by a local edge-based color constancy algorithm. Then, the corrected images are transformed
into a color-space, where statistical results on a skin dataset show that the skin color cluster and non-skin color clusters
are separated. Finally, the skin colors are modeled under Bayesian decision framework and classified from non-skin
colors. The experimental results show that the proposed method is robust to illumination variations.
A method of frost observation based on intensity changing regularity simulation and texture analysis
Show abstract
Frost is a kind of ground coagulation phenomena, and if the temperature of dew point is below 0Co , the water vapor
condenses as solid, which is called frost. The frost phenomena observing is an important step in daily ground observation
work, and the results is one of 36 critical data in meteorological observation field. This work is usually accomplished by
manual. In this paper, we propose an effective method for frost observation based on image processing. The changing of
frost formation process is well simulated by using the curve fitting of gray correlation coefficient between certain lengths
of frames, while the characteristic of frost surface texture is also well described by texture analysis based on texture
descriptor. The experiment results show that our method can get high detection accuracy in the different kinds of
continuous changing environment.
An automatic DSM and remote sensing images registration scheme using template matching technique
Show abstract
In this paper, a novel automatic DSM and remote sensing images registration scheme using template matching technique
is developed. Due to the heterogeneity of DSM and remote sensing images, the emphases of our scheme are to describe
the common feature between DSM and remote sensing images, and to generate a suitable template for template
matching. Based on the sparse representation theory, we present a new feature descriptor, which can highlight the
similarities of DSM and remote sensing images, and can be used to form a new kind of feature image. Meanwhile we
present a criterion to choose the proper region from the feature image as the template which will ensure perfect template
matching performance. The experiment results show that our scheme is efficient to fulfill the task of registration.
A matching algorithm for IR/visual images based on the invariant moment of histogram
Show abstract
The concept of invariant moment of gradient orientation histogram and a novel line matching algorithm based on the
invariant moment of histogram are proposed to resolve the problem of matching typical objects in the IR and visible images.
First, line segments are extracted. Second, the average gradient vector direction of each pixel on the line is adopt as the main
direction of the line. Third, the line is divided into non-overlapped sub-regions with the same size. The gradient vector
direction of each sub-region are constructed, and the weighted invariant moment of histogram in each sub-region are
calculated to build the line descriptor. Finally, the feature matching is realized via the NNDR(nearest/next ratio) method.
Experimental results show that the proposed algorithm can match the typical objects in the IR and visible images efficiently
and correctly.
User-oriented summary extraction for soccer video based on multimodal analysis
Show abstract
An advanced user-oriented summary extraction method for soccer video is proposed in this work. Firstly, an algorithm
of user-oriented summary extraction for soccer video is introduced. A novel approach that integrates multimodal
analysis, such as extraction and analysis of the stadium features, moving object features, audio features and text features
is introduced. By these features the semantic of the soccer video and the highlight mode are obtained. Then we can find
the highlight position and put them together by highlight degrees to obtain the video summary. The experimental results
for sports video of world cup soccer games indicate that multimodal analysis is effective for soccer video browsing and
retrieval.
An application of three-dimensional modeling in the cutting machine of intersecting line software
Jixiang Lu
Show abstract
This paper developed a software platform of intersecting line cutting machine. The software
platform consists of three parts. The first is the interface of parameter input and modify, the second
is the three-dimensional display of main tube and branch tube, and the last is the cutting simulation
and G code output. We can obtain intersection data by intersection algorithm, and we also make
three-dimensional model and dynamic simulation on the data of intersecting line cutting. By
changing the parameters and the assembly sequence of main tube and branch tube, you can see the
modified two-dimensional and three-dimensional graphics and corresponding G-code output file.
This method has been applied to practical cutting machine of intersecting line software.
Laser-beam-based calibration
Show abstract
A laser beam based calibration method is proposed in the paper. The laser beam we used can keep the cylinder shape in
the real world space. The cylinder shaped laser beam will form an elliptical area in the plane in which the defect we want
to measure also located. This elliptical laser area will form an ellipse in the image. Our purpose is to achieve calibration
only depending on the elliptical parameters of the ellipse in the image. We created the relationship model between the
elliptical parameters of the ellipse in the image and the calibration parameters of the video appliance, and we find a quick
and easy way to solve the relationship model. Using this calibration method we can achieve contactless video
measurement during the equipment checking in the nuclear power station.
A new method for fast circle detection in a complex background image
Show abstract
A new method for circle detection, Hough gradient clustering method, has been developed in this paper. By using
gradient direction angle to find the diameter of a circle, the new method can rapidly detect the circle in a complex
background. The crucial steps in this method are the feature extraction and the clustering of the points which have the
same gray direction angle and are collinear along the gradient direction. The application of the two-to-one space
mapping and 1-2Hough transform can further reduce the useless calculation in the process of circle detection. Comparing
with the Hough gradient method in OpenCV, the newly developed method shows a higher efficiency of circle detection
in a complex background image as well as a great improvement in the anti-noise ability.
Object recognition based on spatial active basis template
Show abstract
This article presents a method for the object classification that combines a generative template and a discriminative
classifier. The method is a variant of the support vector machine (SVM), which uses Multiple Kernel Learning (MKL). The
features are extracted from a generative template so called Active Basis template. Before using them for object
classification, we construct a visual vocabulary by clustering a set of training features according to their orientations. To
keep the spatial information, a "spatial pyramid" is used. The strength of this approach is that it combines the rich
information encoded in the generative template, the Active Basis, with the discriminative power of the SVM algorithm. We
show promising results of experiments for images from the LHI dataset.
A realization of semi-global matching stereo algorithm on GPU for real-time application
Bin Chen,
He-ping Chen
Show abstract
Real-time stereo vision systems have many applications such as automotive and robotics. According to the Middlebury
Stereo Database, Semi-Global Matching (SGM) is commonly regarded as the most efficient algorithm among the
top-performing stereo algorithms. Recently, most effective real-time implementations of this algorithm are based on
reconfigurable hardware (FPGA). However, with the development of General-Purpose computation on Graphics
Processing Unit, an effective real-time implementation on general purpose PCs can be expected.
In this paper, a real-time SGM realization on Graphics Processing Unit (GPU) is introduced. CUDA, a general purpose
parallel computing architecture introduced by NVIDIA in November 2006, has been used to realize the algorithm. Some
important optimizations according to CUDA and Fermi (the latest architecture of NVIDA GPUs) are also introduced in
this paper.
Hand-written numeral recognition based on spectrum clustering
Show abstract
In this paper, we First makes selection of the Zernike moment features of handwritten numerals based
on the principles that the distinction degree of inside-class features is small and the dividing of the
features between classes is huge; Then construct the similar matrix between handwritten numerals by
the similarity measure based on Grey relational analysis and make transitivity transformation to similar
matrix for better block symmetry after reformation; Finally make spectrum decomposition to the
Laplacian matrix which from the reformation similar matrices, and recognize the handwritten numerals
with the eigenvectors corresponding to the second minimal eigenvalues in Laplacian matrix as the
spectral features. The experimental result indicates that the robustness of the algorithm proposed in this
paper is great and the result is fine.
Fast residential area extraction from remote sensing image based on Log-Gabor filter
Show abstract
Monitoring urbanization may help government agencies and urban region planners in updating land maps and forming
long-term plans accordingly. In this paper, a novel method for fast extracting residential area from remote sensing
images based on log-Gabor filter was proposed. The method is divided in three steps. Firstly, we detect the edge-oriented
urban characteristics in a remote sensing image using log-Gabor filter. Secondly, with the filtering orientations
perpendicular to each other, we choose two log-Gabor filter response images to suppress the noise and acquire a smooth
spatial region. Thirdly, a set of smooth regions served as residential areas can be extracted using Otsu's method. We
tested it on diverse aerial and satellite images and encouraging results were acquired. The comparison of our method
with the classical texture analyzing method of co-occurrence matrix demonstrated its superiority.
Object tracking in visual surveillance using narrow band-based graph cuts
Show abstract
Tracking deforming objects involves estimating the global motion of the object and its local deformations as a function
of time. Tracking algorithms using Graph Cut have been proposed. However, because of its globally optimal nature, it is
prone to capture outlying areas similar to the object of interest. Constraint was introduced to confine the standard graph
cut technique in many methods. In this paper, narrow band is introduced to constrain the standard graph cut where it is
build on. With the narrow band constraint, segmentation is ensured to remain in it, which is useful when the result is not
robust using standard graph cut. Secondly, Graph Cut is built based on the band region, compared with traditionally
method, calculation cost is reduced. Thirdly, formulations are adjusted to distinguish between different parts of the
narrow band, which improve the segmentation quality.
A novel image distance based on Gabor feature and approximated manifold
Show abstract
Tangent distance method approximates nonlinear manifolds by their tangent hyperplanes and has been widely used in
image recognition. However tangent distance directly deals with original images while high-order statistic information
may be neglected. And the information of image transformation should be known a priori. We propose a new image
distance metric-Gabor feature-based approximated manifold distance (GFMD) to address these disadvantages. Firstly
Gabor wavelet transform are applied to calculate high-order statistical information of images. The intrinsic variables of
feature manifold are revealed by MVU. The feature manifold can be approximated by curve surfaces based on second-order
Taylor expansion. GFMD is defined as the minimum distance between the approximated curved surfaces and can
be directly combined with distance-based classifiers for image recognition. The experimental results of face recognition
demonstrate that GFMD not only has higher invariance of transformation but also has more stability of classification
than several state-of-the-art distance metrics.
A multifocus image fusion in nonsubsampled contourlet domain with variational fusion strategy
Show abstract
Based on the variational idea, we propose a new fusion strategy for nonsubsampled contourlet transform (NSCT). For
NSCT bandpass subband coefficients of input images, we take the main component of coefficients as the target and then
build an extremum problem for energy functional to find the closest to the target one as the fused coefficient. We apply
the gradient descent flow to minimize the functional and give the numerical scheme. The experimental results show that
the proposed strategy outperforms state-of-the-art image fusion strategies for NSCT in terms of both visual quality and
objective evaluation criteria.
A novel 3D convex surface reconstruction method based on visual hull
Show abstract
3D reconstruction is one of main techniques for computer vision. A novel 3D convex surface reconstruction
method is presented in this paper, which is based on visual hull principle. The real object is supposed to be surrounded
by a 3D grid bounding box filled with voxels. A series of images of the object are captured by a calibrated camera in
different locations. For each image, a series of virtual rays, each of which starts from an image silhouette point and drills
through the camera center, intersect the voxels in bounding box to obtain a number of potential object surface points.
Afterward, the potential surface points are projected onto the other images to eliminate the pseudo surface points which
must locate outside the object image area in at least one image. When all images are processed, the surface points of
whole object are obtained and then 3D surface is reconstructed. The experiment illuminates the feasibility and validity
of our approach.
Visualization and analysis for multidimensional gene expressions signature of cigarette smoking
Show abstract
Biologists often use gene chip to get massive experimental data in the field of bioscience and chemical sciences. Facing a
large amount of experimental data, researchers often need to find out a few interesting data or simple regulations. This
paper presents a set of methods to visualize and analyze the data for gene expression signatures of people who smoke.
We use the latest research data from National Center for Biotechnology Information. Totally, there are more than 400
thousand expressions data. Using these data, we can use parallel coordinates method to visualize the different gene
expressions between smokers and nonsmokers and we can distinguish non-smokers, former smokers and current smokers
by using the different colors. It can be easy to find out which gene is more important during the lung cancer angiogenesis
in the smoking people. In another way, we can use a hierarchical model to visualize the inner relation of different genes.
The location of the nodes shows different expression moment and the distance to the root shows the sequence of the
expression. We can use the ring layout to represent all the nodes, and connect the different nodes which are related with
color lines. Combined with the parallel coordinates method, the visualization result show the important genes and some
inner relation obviously, which is useful for examination and prevention of lung cancer.
A novel high-resolution image connected components labeling method
Show abstract
A novel labeling algorithm is proposed in this paper for high-resolution image connected components labeling. The
proposed method successfully solves the labeling problem in the case that the computer memory capacity is less than
required for the existing algorithms. Unlike the conventional algorithms that have to load the entire image into memory
for labeling, our algorithm can finish the labeling task with the memory requirement of only two image rows size.
Comparison and tests for high-resolution images are performed with the state-of-the-art algorithm. The proposed
algorithm achieves significant performance improvement for high-resolution images labeling.
Contour integration and segmentation with a new lateral connections model
Chao Cai
Show abstract
Automatically target contour detection from cluttered scenes is a very difficult task for computer vision. Humans,
however, have a much better background suppress ability. The preceding models could not implement such a task very
well. In this letter, an effective contour integration method based on human visual perception mechanism is proposed.
The algorithm combines the properties of primary visual cortex and psychology researching results to simulate the
contour perception of the V1 cortex. The new lateral connection based computational model have a better texture
suppress ability, while, target's contour is enhanced. Compared with traditional methods, experiments show that the new
method implement a more reasonable simulation of the V1 function structure, availably enhance the target's contour
while suppress the cluttered background, obtain a balance between over and lose detection, besides, it has better accuracy
with less computational complexity and time-consuming.
A fast image retrieval method based on SVM and imbalanced samples in filtering multimedia message spam
Zhang Chen,
Zhenming Peng
Show abstract
With the swift and violent development of the Multimedia Messaging Service (MMS), it becomes an urgent task to filter
the Multimedia Message (MM) spam effectively in real-time. For the fact that most MMs contain images or videos, a
method based on retrieving images is given in this paper for filtering MM spam. The detection method used in this paper
is a combination of skin-color detection, texture detection, and face detection, and the classifier for this imbalanced
problem is a very fast multi-classification combining Support vector machine (SVM) with unilateral binary decision tree.
The experiments on 3 test sets show that the proposed method is effective, with the interception rate up to 60% and the
average detection time for each image less than 1 second.
Face-sets based video shot retrieval using the text-search analogous approach
Show abstract
The objective of this work is to retrieve shots containing particular people in video material. Due to well known
problems such as illumination, pose and expression variations, it has been a challenging task. To this end, a robust and
scalable approach analogized with text search method is proposed in this paper, which mainly consists of four steps:
Obtaining sets of face exemplars by tracking, Local Facial Features Extraction and Fusion, Visual words established,
Document descriptor obtained and VSM established for shot retrieval. Experimental results demonstrate that the
proposed approach is not only efficient but also outperforms the competing methods.
A novel method using Gabor-based multiple feature and ensemble SVMs for ground-based cloud classification
Show abstract
Cloud recognition is the base of weather forecast and the recognition of cloud types is challenging because the texture of
the clouds is extremely variable under different atmospheric conditions. In this paper, we propose a novel method for
ground-based cloud classification. Firstly, the interest operator feature (IO) and the sorted spectral histogram (SSH)
feature are generated from Gabor-filtered images and then they are selected by using the principal component analysis
(PCA), which can reduce the feature's dimension. Secondly the new training set is selected using the supervised
clustering technology. Finally we send the two features to the multi-class SVM classifier, and a voting algorithm is used
to determine the category of each cloud. In practice, we find no single feature is best suited for recognizing all these
classes. The result shows that this method has higher classfication accuracy and lower space complexity than the other
methods.
The research and development of CCD-based slab continuous casting mold copper surface imaging system
Xingdong Wang,
Liugang Zhang,
Haihua Xie,
et al.
Show abstract
An imaging system for the continuous casting mold copper surface is researched and
developed, to replace the on-line manual measuring method, which is used to checking Copper
defects such as wearing, scratches and coating loss and other phenomena. Method: The imaging
system proposes a special optical loop formed by three Mirrors, selects light source, CCD camera
and lens type, designs mechanical transmission system and installation platform. Result: the optical
loop and light source can insure imaging large-format object in narrow space. The CCD camera and
lens determine the accuracy of horizontal scanning, and the mechanical transmission system ensures
accuracy of the vertical scan. The installation platform supplies base and platform for the system.
Conclusions: CCD-based copper surface imaging system effectively prevent defects such as missed
measuring and low efficiency, etc. It can automatically and accurately shoot copper surface images
on-line, and supply basis for image processing, defects identification and copper changing in the
late.
Handwritten digits recognition based on immune network
Yangyang Li,
Yunhui Wu,
Lc Jiao,
et al.
Show abstract
With the development of society, handwritten digits recognition technique has been widely applied to production and
daily life. It is a very difficult task to solve these problems in the field of pattern recognition. In this paper, a new method
is presented for handwritten digit recognition. The digit samples firstly are processed and features extraction. Based on
these features, a novel immune network classification algorithm is designed and implemented to the handwritten digits
recognition. The proposed algorithm is developed by Jerne's immune network model for feature selection and KNN
method for classification. Its characteristic is the novel network with parallel commutating and learning. The
performance of the proposed method is experimented to the handwritten number datasets MNIST and compared with
some other recognition algorithms-KNN, ANN and SVM algorithm. The result shows that the novel classification
algorithm based on immune network gives promising performance and stable behavior for handwritten digits recognition.
Research of SIFT matching algorithm in binocular vision
Shihua Liang,
Wei Wu,
Liting Lin,
et al.
Show abstract
According to that the SIFT algorithm contains contour property in the process of computation of scale-space, this paper
proposes an improved SIFT algorithm named Contour directed-SIFT to offset the flaws of real-time in SIFT algorithm.
In order to improve the accuracy of SIFT algorithm, the paper improve the Contour directed-SIFT algorithm using the
epipolar constraint. Taking the two aspects above, a new matching algorithm based on SIFT algorithm in binocular
vision is proposed. This algorithm can assure the real-time and accuracy in matching images, which can be applied in
matching of massive features of tridimensional vision.
Removing artifacts using gradient projection from a single image
Show abstract
Image artifacts caused by image deconvolution usually make people feel regretful. Especially for image deconvolution
algorithm which is used for restoring motion blurred image. The artifacts removing is challenging for conventional
image deconvolution approach. In this paper, we propose a novel method that removes artifacts from a deconvolved
image which caused by image deconvolution. Firstly, blur method is used to get a blurred version of the original artifacts
image. Secondly, gradient projection algorithm is used to adjust the gradient of the original image with the blurred image
for reference. Finally, the Poisson solution is adopted to reconstruct the final high quality and artifacts free image.
Moreover, the artifacts are also reduced at the Poisson interpolation stage. The experiments demonstrate that our
algorithms are efficient.
A simple and efficient saliency extraction method based on multi-scale horizon-directional filter for infrared dim small target detection
Show abstract
This paper presents a simple and computationally efficient saliency extraction method for detecting dim small target
from single frame in heterogeneous background. The proposed method is based on background subtraction (BS), which
identifies targets from the portion of a image that differs significantly from a background model. A set of horizon-directional
filters (HDF) with multi-scales are first implemented to effectively recover the background maps from the
input image. As a result, the foreground maps are extracted by computing the absolute difference between the input
image and the estimated background maps. Then the foreground maps are fused into the total saliency map using a
simple scheme. Finally, the experimental results of various cluttered background images show that the proposed method
is efficient and has an outstanding performance in dim small target detection just by thresholding the saliency map.
Design of 3D crafts manufacturing system based on stereo vision
Xiangyan Liu,
Shilin Wu
Show abstract
This paper proposed a design program of 3D crafts manufacturing system which is based on binocular stereo vision and
plug-in technology. First, this paper gave an overall introduction to the system, including system structure, function and
working flow. Second, this paper analyzed some key technologies in the system development. It proposed a 3D measure
method of structured light based binocular stereo system, and introduced the structure of the measurement system and
analyzed thoroughly the key technologies in 3D measure, including calibration of stereo system, stereo matching and so
on. Besides, it did some researches on how to use Geomagic Studio to carry on point cloud processing and the STL
reconstruction. Third, this paper analyzed how to develop the system software which mainly consists of 3D measure
Geomagic plug-in and RE Geomagic plug-in using VC++ programming language and plug-in technology. At last, this
paper carried on large numbers of experiments and simulation using the developed software and the platform built
according to the development program proposed in this paper and received satisfactory results which have proven that
the development program proposed in this paper is feasible and the developed software takes on usability and may be
used in the development of RE and RP system.
An ensemble learning algorithm based on generalized attribute value partitioning
Weidong Tian,
Fang Wu,
Jipeng Qiang,
et al.
Show abstract
The method of disturbing training data randomly to train individual classifiers has been widely applied in some ensemble
learning methods such as Bagging and Boosting to achieve strong generalization ability, however, it seems something
blind. In this paper, a new ensemble learning algorithm named GAVPEL is proposed. By using the hierarchy nature of
the data set, GAVPEL leverages the generalized attribute value partitioning method to form an ensemble tree, called a
generalized classifier hierarchy tree. While classifying, GAVPEL selects part of the individual classifiers based on
attribute value and ensembles them with majority voting. Experiment results show that GAVPEL can efficiently improve
generalization performance when compared with some popular ensemble learning algorithms.
Combining 1D and 2D linear discriminant analysis for palmprint recognition
Show abstract
In this paper, a novel feature extraction method for palmprint recognition termed as Two-dimensional Combined
Discriminant Analysis (2DCDA) is proposed. By connecting the adjacent rows of a image sequentially, the obtained new
covariance matrices contain the useful information among local geometry structures in the image, which is eliminated by
2DLDA. In this way, 2DCDA combines LDA and 2DLDA for a promising recognition accuracy, but the number of
coefficients of its projection matrix is lower than that of other two-dimensional methods. Experimental results on the
CASIA palmprint database demonstrate the effectiveness of the proposed method.
Online independent Lagrangian support vector machine
Yu Jin,
Hongbing Ji,
Lei Wang,
et al.
Show abstract
In this paper, a novel incremental learning method called online independent Lagrangian support vector machine
(OILSVM) is proposed. It achieves comparable classification accuracy with benchmark Lagrangian support vector
machine (LSVM), while still enjoying the time efficiency of online learning machines. As opposed to the newly
proposed OLSVM that utilizes the KKT conditions as data selection strategy, the size of the solution obtained by
OILSVM using a linear independence check is always bounded, which implies bounded memory requirements, training
and testing time. Experimental results demonstrate the effectiveness of the proposed OILSVM.
Terrain reconstruction algorithm based on epipolar line rectification and dense matching
Wenjuan Jiang,
Yuan Gao,
Jiayi Ma,
et al.
Show abstract
Terrain reconstruction aims at acquiring height maps by detecting corresponding feature points from two or more
down-looking remote sensing images. This paper proposes a terrain reconstruction algorithm based on epipolar line
rectification and dense matching. At first, it uses fundamental matrix to rectify stereo images to make their epipolar lines
parallel and remove the disparity in vertical direction. Then, dense matching based on grid method is applied, which can
provide sufficient matching points to estimate disparity of the rectified images. Finally, the heights of the matched points
can be calculated according to the obtained disparity and flight parameters. Experiments show that our algorithm can
generate precise and reliable height maps for well depicting terrain features.
A heuristic algorithm for extraction of straight lines
Yinwen Dong,
Bingcheng Yuan,
Hangyu Wang,
et al.
Show abstract
Straight line is an important intermediate symbol, and the straight line extraction is one of the key technologies in image
processing and computer vision. Aiming to the characteristics of digital image spatial domain, a straight line model of
image spatial domain is established, and a heuristic algorithm based on the model for straight line extraction is presented,
Which gets the edge amplitude image by edge detecting, and extracts the straight line which matches the straight line
model through employing heuristic search guided by the edge information. The simulation shows that the algorithm is
robust for image noise, and it not only can extract the low contrast straight line, but also can eliminate the short fractured
straight line effectively.
Error analysis of three-dimensional reconstruction for irregular objects
Li Zheng,
Yanjie Feng,
Yuejun Luo
Show abstract
The visual detection based on structured light is put forward to solve the problem of image matching for stereo vision in
recent years. It is paid much attention in the industrial environment, especially in the three-dimensional automatic
detection and object recognition. In this paper, it is discussed about the accuracy of visual inspection techniques based on
structured light. There are some factors of affecting measurement techniques based on structured light, which are object
material, edge detection algorithms, uneven lighting, and so on. The experimental results reveal that these three factors
have a great impact on the accuracy of the model measurement of irregular objects.
Laser spot tracking with sub-pixel precision based on subdivision mesh
Show abstract
As high brightness of laser spot, it significantly distinguish from the other goals on objects surface.
Therefore, in computer vision, laser spot often is applied as interaction tools, and an important
measurement tool etc. In building large-scale multi-projection display system, accurate measurement of
geometrical parameters is very important. Based on subdivision mesh, this paper presents laser spot
tracking algorithm with sub-pixel precision. The experimental data validates this algorithm sub-pixel
precision.
Background feature descriptor for offline handwritten numeral recognition
Show abstract
This paper puts forward an offline handwritten numeral recognition method based on background structural descriptor
(sixteen-value numerical background expression). Through encoding the background pixels in the image according to a
certain rule, 16 different eigenvalues were generated, which reflected the background condition of every digit, then
reflected the structural features of the digits. Through pattern language description of images by these features, automatic
segmentation of overlapping digits and numeral recognition can be realized. This method is characterized by great
deformation resistant ability, high recognition speed and easy realization. Finally, the experimental results and
conclusions are presented. The experimental results of recognizing datasets from various practical application fields
reflect that with this method, a good recognition effect can be achieved.