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Optical Engineering

Textured image segmentation based on modulation models
Author(s): Qingqing Zheng; Nong Sang; Leyuan Liu; Changxin Gao
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Paper Abstract

We propose an approach for textured image segmentation based on amplitude-modulation frequency-modulation models. An image is modeled as a set of 2-D nonstationary sinusoids with spatially varying amplitudes and spatially varying frequency vectors. First, the demodulation procedure for the models furnishes a high-dimensional output at each pixel. Then, features including texture contrast, scale, and brightness are elaborately selected based on the high-dimensional output and the image itself. Next, a normalization and weighting scheme for feature combination is presented. Finally, simple K-means clustering is utilized for segmentation. The main characteristic of this work provides a feature vector that strengthens useful information and has fewer dimensionalities simultaneously. The proposed approach is compared with the dominant component analysis (DCA)+K-means algorithm and the DCA+ weighted curve evolution algorithm on three different datasets. The experimental results demonstrate that the proposed approach outperforms the others.

Paper Details

Date Published: 1 September 2010
PDF: 9 pages
Opt. Eng. 49(9) 097009 doi: 10.1117/1.3487747
Published in: Optical Engineering Volume 49, Issue 9
Show Author Affiliations
Qingqing Zheng, Huazhong Univ. of Science and Technology (China)
Nong Sang, Huazhong Univ. of Science and Technology (China)
Leyuan Liu, Huazhong Univ. of Science and Technology (China)
Changxin Gao, Huazhong Univ. of Science and Technology (China)

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