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A Hybrid Framework for Image Segmentation Using Probabilistic Integration of Heterogeneous Constraints

  • Rui Huang
  • Vladimir Pavlovic
  • Dimitris N. Metaxas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3765)

Abstract

In this paper we present a new framework for image segmentation using probabilistic multinets. We apply this framework to integration of region-based and contour-based segmentation constraints. A graphical model is constructed to represent the relationship of the observed image pixels, the region labels and the underlying object contour. We then formulate the problem of image segmentation as the one of joint region-contour inference and learning in the graphical model. The joint inference problem is solved approximately in a band area around the estimated contour. Parameters of the model are learned on-line. The fully probabilistic nature of the model allows us to study the utility of different inference methods and schedules. Experimental results show that our new hybrid method outperforms methods that use homogeneous constraints.

Keywords

Image Segmentation Inference Method Deformable Model Active Contour Model Region Label 
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 2005

Authors and Affiliations

  • Rui Huang
    • 1
  • Vladimir Pavlovic
    • 1
  • Dimitris N. Metaxas
    • 1
  1. 1.Deptartment of Computer ScienceRutgers UniversityPiscatawayUSA

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