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Optimal Contour Closure by Superpixel Grouping

  • Alex Levinshtein
  • Cristian Sminchisescu
  • Sven Dickinson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6312)

Abstract

Detecting contour closure, i.e., finding a cycle of disconnected contour fragments that separates an object from its background, is an important problem in perceptual grouping. Searching the entire space of possible groupings is intractable, and previous approaches have adopted powerful perceptual grouping heuristics, such as proximity and co-curvilinearity, to manage the search. We introduce a new formulation of the problem, by transforming the problem of finding cycles of contour fragments to finding subsets of superpixels whose collective boundary has strong edge support in the image. Our cost function, a ratio of a novel learned boundary gap measure to area, promotes spatially coherent sets of superpixels. Moreover, its properties support a global optimization procedure using parametric maxflow. We evaluate our framework by comparing it to two leading contour closure approaches, and find that it yields improved performance.

Keywords

Image Edge Perceptual Grouping Contour Closure Contour Image Ground Truth Segmentation 
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 2010

Authors and Affiliations

  • Alex Levinshtein
    • 1
  • Cristian Sminchisescu
    • 2
  • Sven Dickinson
    • 1
  1. 1.University of Toronto 
  2. 2.University of Bonn 

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