A continuous shape descriptor by orientation diffusion

Hsing Kuo Pao, Davi Geiger

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a continuous description for 2-D shapes that calculates convexity, symmetry and is able to account for size. Convexity and size are known to be critical in deciding figure/ground (F/G) separation, with the study initiated by the Gestalt school [9] [11]. However, few quantitative discussions were made before. Thus, we emphasize the convexity/size measurement for the purpose of F/G prediction. A Kullback-Leibler measure is introduced. In addition, the symmetry information is studied through the same platform. All these shape properties are collected for shape representations. Overall, our representations are given in a continuous manner. For convexity measurement, unlike the 1/0 mathematical definition where shapes are categorized as convex or concave, we give a measure describing shapes as “more” or “less” convex than others. In symmetry information (skeleton) retrieval, a 2-D intensity map is provided with the intensity value specifying “strength” of the skeleton. The proposed representations are robust in the sense that small fine-scale perturbations on shape boundaries will cause minor effects on the final representations. All these shape properties are intergrated into one description. To apply to the F/G separation, the shape measure can be flexibly chosen between a size-invariant convexity measure or a convexity measure with the small size preference. The model is established on an orientation diffusion framework, where the local features, served as inputs, are intensity edge locations and their orientations. The approach is a variational one, rooted in a Markov random field (MRF) formulation. A quadratic form is used to assure simplicity and the existence of solution.

Original languageEnglish (US)
Title of host publicationEnergy Minimization Methods in Computer Vision and Pattern Recognition - 3rd International Workshop, EMMCVPR 2001, Proceedings
EditorsAnil K. Jain, Mario Figueiredo, Josiane Zerubia
PublisherSpringer Verlag
Pages544-559
Number of pages16
ISBN (Print)3540425233, 9783540425236
DOIs
StatePublished - 2001
Event3rd International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2001 - Sophia Antipolis, France
Duration: Sep 3 2001Sep 5 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2134
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2001
CountryFrance
CitySophia Antipolis
Period9/3/019/5/01

Keywords

  • Convexity
  • Early vision
  • Orientation diffusion
  • Shape analysis
  • Symmetry

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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