Please use this identifier to cite or link to this item: http://idr.nitk.ac.in/jspui/handle/123456789/10378
Title: Contourlet based multiresolution texture segmentation using contextual hidden markov models
Authors: Raghavendra, B.S.
Subbanna, Bhat, P.
Issue Date: 2004
Citation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2004, Vol.3356, , pp.336-343
Abstract: In this paper, block based texture segmentation is proposed based on contourlets and the hidden Markov model (HMM). Hidden Markov model is combined with hidden Markov tree (HMT) to form HMM-HMT model that models global dependency between the blocks in addition to the local statistics within a block. The HMM-HMT model is modified to use the contourlet transform, a new extension to the wavelet transform that forms a true basis for image representations. The maximum likelihood multiresolution segmentation algorithm is used to handle several block sizes at once. Since the algorithm works on the contourlet transformed image data, it can directly segment images without the need for transforming into the space domain. The experimental results demonstrate the competitive performance of the algorithm on contourlets with that of the other methods and excellent visual performance at small block sizes. The performance is comparable with that of wavelets and is superior at small block sizes. Springer-Verlag 2004.
URI: http://idr.nitk.ac.in/jspui/handle/123456789/10378
Appears in Collections:1. Journal Articles

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