Image Segmentation based on Graph-Cut Models and Probabilistic Graphical Models: a Comparative Study
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Accepted Manuscript (AM)
Author(s)
Beheshti, M
Liew, AWC
Griffith University Author(s)
Year published
2014
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Show full item recordAbstract
Image Segmentation has been one of the most important unsolved problems in computer vision for many years. Recently, there have been great efforts in producing better segmentation algorithms. The purpose of this paper is to introduce two recently proposed graph based segmentation methods, namely, graph-cut models (deterministic) and unified graphical model (probabilistic). We present some foreground/background segmentation results to illustrate their performance on images with complex background scene.Image Segmentation has been one of the most important unsolved problems in computer vision for many years. Recently, there have been great efforts in producing better segmentation algorithms. The purpose of this paper is to introduce two recently proposed graph based segmentation methods, namely, graph-cut models (deterministic) and unified graphical model (probabilistic). We present some foreground/background segmentation results to illustrate their performance on images with complex background scene.
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Conference Title
Communications in Computer and Information Science
Volume
481
Publisher URI
Copyright Statement
© 2014 Springer Berlin/Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.c
Subject
Computer vision