Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/10378
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRaghavendra, B.S.-
dc.contributor.authorSubbanna, Bhat, P.-
dc.date.accessioned2020-03-31T08:19:02Z-
dc.date.available2020-03-31T08:19:02Z-
dc.date.issued2004-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2004, Vol.3356, , pp.336-343en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/10378-
dc.description.abstractIn 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.en_US
dc.titleContourlet based multiresolution texture segmentation using contextual hidden markov modelsen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

Files in This Item:
File Description SizeFormat 
10378.pdf88.84 kBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.