Contour segment matching by integrating intra and inter shape cues of objects

Ishani Chakraborty, Ahmed Elgammal

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

1 Scopus citations

Abstract

In this paper we propose an algorithm for contour-based object detection in cluttered images. Contour of an object shape is approximated as a set of line segments and object detection is framed as matching contour segments of an image (i.e., an edge image) to a boundary model of an object (i.e., a line drawing). Local shape is abstracted as a group of k-adjacent segments. We use a multi-level shape description (with different k's) to capture complexity variations in local shape. Between images, shape descriptors are matched to give inter-shape correspondences and within images the underlying segment grouping enforces intra-shape contextual constraints. We use an efficient relaxation labeling approach that integrates these shape cues to qualify a contour match. To this end, we propose a novel framework that solves the problem of object detection as a contour segments correspondence problem. We then demonstrate the efficacy of the method for detecting various objects in cluttered images by comparing them to simple line drawings.

Original languageEnglish (US)
Title of host publicationBritish Machine Vision Conference, BMVC 2009 - Proceedings
PublisherBritish Machine Vision Association, BMVA
ISBN (Print)1901725391, 9781901725391
DOIs
StatePublished - 2009
Event2009 20th British Machine Vision Conference, BMVC 2009 - London, United Kingdom
Duration: Sep 7 2009Sep 10 2009

Publication series

NameBritish Machine Vision Conference, BMVC 2009 - Proceedings

Other

Other2009 20th British Machine Vision Conference, BMVC 2009
Country/TerritoryUnited Kingdom
CityLondon
Period9/7/099/10/09

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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