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International Journal of Computer Vision

, Volume 71, Issue 3, pp 337–359 | Cite as

Global Detection of Salient Convex Boundaries

  • Song WangEmail author
  • Joachim S. Stahl
  • Adam Bailey
  • Michael Dropps
Article

Abstract

As an important geometric property of many structures or structural components, convexity plays an important role in computer vision and image understanding. In this paper, we describe a general approach that can force various edge-grouping algorithms to detect only convex structures from a set of boundary fragments. The basic idea is to remove some fragments and fragment connections so that, on the remaining ones, a prototype edge-grouping algorithm that detects closed boundaries without the convexity constraint can only produce convex closed boundaries. We show that this approach takes polynomial time and preserves the grouping optimality by not excluding any valid convex boundary from the search space. Choosing the recently developed ratio-contour algorithm as the prototype grouping algorithm, we develop a new convex-grouping algorithm, which can detect convex salient boundaries with good continuity and proximity in a globally optimal fashion. To facilitate the application of this convex-grouping algorithm, we develop a new fragment-connection method based on four-point Bezier curves. We demonstrate the performance of this convex-grouping algorithm by conducting experiments on both synthetic and real images. In addition, we provide a comparison with some prior edge-grouping algorithms. Finally, we show that the proposed convex-grouping algorithm can be further extended to detect convex open boundaries, derive region-based image hierarchies, and incorporate some simple human-computer interactions.

Keywords

boundary detection convexity edge detection edge grouping graph models perceptual organization 

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Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • Song Wang
    • 1
    Email author
  • Joachim S. Stahl
    • 1
  • Adam Bailey
    • 1
  • Michael Dropps
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of South CarolinaColumbia

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