A Fuzzy Segmentation Method for Images of Heat-Emitting Objects
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Texture classification using refined histogram
IEEE Transactions on Image Processing
Optical Memory and Neural Networks
Multimedia Tools and Applications
Image segmentation using local spectral histograms and linear regression
Pattern Recognition Letters
Face segmentation using projection pursuit for texture classification
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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We present a method for segmenting images consisting of texture and nontexture regions based on local spectral histograms. Defined as a vector consisting of marginal distributions of chosen filter responses, local spectral histograms provide a feature statistic for both types of regions. Using local spectral histograms of homogeneous regions, we decompose the segmentation process into three stages. The first is the initial classification stage, where probability models for homogeneous texture and nontexture regions are derived and an initial segmentation result is obtained by classifying local windows. In the second stage, we give an algorithm that iteratively updates the segmentation using the derived probability models. The third is the boundary localization stage, where region boundaries are localized by building refined probability models that are sensitive to spatial patterns in segmented regions. We present segmentation results on texture as well as nontexture images. Our comparison with other methods shows that the proposed method produces more accurate segmentation results