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  • 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)
    Liew, Alan Wee-Chung
    Year published
    2014
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    Abstract
    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
    http://www.icmlc.com/ICMLC/formerICMLC_2014.html
    DOI
    https://doi.org/10.1007/978-3-662-45652-1_37
    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
    Publication URI
    http://hdl.handle.net/10072/66986
    Collection
    • Conference outputs

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