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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/10054/233

Title: Joint segmentation and image interpretation using hidden Markov models
Authors: KAMATH, N
SUNIL KUMAR, K
DESAI, UB
DUGUD, R
Keywords: computer vision
hidden markov models
image segmentation
knowledge representation
probability
Issue Date: 1998
Publisher: IEEE
Citation: Proceedings of the Fourteenth International Conference on Pattern Recognition (V 2), Brisbane, Australia, 16-20 August 1998, 1840-1842
Abstract: Image interpretation consists of interleaving the low-level task of image segmentation and the high-level task of interpretation. The idea being that the interpretation block guides the segmentation block which in turn helps the interpretation block in better interpretation. In this paper we develop a joint segmentation and image interpretation scheme using the notion of joint hidden Markov model (HMM) for probabilistic modeling of spatial relationship. We find the optimal interpretation labels, which are nothing but the optimal state sequence of the HMM.
URI: 10.1109/ICPR.1998.712088
http://hdl.handle.net/10054/233
http://dspace.library.iitb.ac.in/xmlui/handle/10054/233
ISBN: 0-8186-8512-3
Appears in Collections:Proceedings papers

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