Supervised Texture Classification using Multiscale Contourlet Based Hidden Markov Tree Models
نویسنده
چکیده
Contourlet domain Hidden Markov Models can provide a powerful approach for statis tical modeling and processing of contourlet coefficients of natural textural images. This multiscale model captures the statistical structure of smooth, texture and edge regions of an image. Contourlets have emerged as a new mathematical tool for image processing. They provide a compact and decorrelated image representation. In this paper, contourlet domain hidden Markov tree modeling of images and its application to texture classification/segmentation is presented. The performance of the texture segmentation algorithm is compared with that of wavelet HMT texture segmentation method. The performance is comparable with that of wavelets and is superior at small block sizes.
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