1 September 2010 Textured image segmentation based on modulation models
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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.
© (2010) Society of Photo-Optical Instrumentation Engineers (SPIE)
Qingqing Zheng, Nong Sang, Leyuan Liu, Changxin Gao, "Textured image segmentation based on modulation models," Optical Engineering 49(9), 097009 (1 September 2010). https://doi.org/10.1117/1.3487747 . Submission:
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