Abstract
A novel method of unsupervised imagesegmentation using contourlet domain hidden markov trees model is presented. Fuzzy C-mean clustering algorithm is used to capture the likelihood disparity of different texture features. A new context based fusion model is given for preserve more interscale information in contourlet domain. The simulation results of synthetic mosaics and real images show that the proposed unsupervised segmentation algorithm represents a better performance in edge detection and protection and its error probability of the synthetic mosaics is lower than wavelet domain HMT based method.
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Sha, Y., Cong, L., Sun, Q., Jiao, L. (2005). Unsupervised Image Segmentation Using Contourlet Domain Hidden Markov Trees Model. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2005. Lecture Notes in Computer Science, vol 3656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559573_5
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DOI: https://doi.org/10.1007/11559573_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29069-8
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