Deformable Object Shape Refinement and Tracking Using Graph Cuts and Support Vector Machines

  • Mehmet Kemal Kocamaz
  • Yan Lu
  • Christopher Rasmussen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6939)


This paper describes several approaches to the problem of obtaining a refined segmentation of an object given a coarse initial segmentation of it. One line of investigation modifies the standard graph cut method by incorporating color and shape distance terms, adaptively weighted at run time to try to favor the most informative cue given visual conditions. We also discuss a machine learning approach based on support vector machines which uses color and spatial features as well. Furthermore, we extend these single-frame refinement methods to serve as the basis of trackers which work for a variety of object types with complex, deformable shapes. Comparative results are presented for several diverse datasets including objects such as trail regions used for robot navigation, hands, and faces.


Support Vector Machine Color Space Training Image Object Segmentation Object Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mehmet Kemal Kocamaz
    • 1
  • Yan Lu
    • 1
  • Christopher Rasmussen
    • 1
  1. 1.Department of Computer and Information SciencesUniversity of DelawareNewarkU.S.A.

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