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Proceedings Paper

Combining edge and surface information for object recognition
Author(s): Neelima Shrikhande; Gongzhu Hu
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Paper Abstract

One of the central problems of computer vision is model based object recognition. A catalogue of model objects is described via a set of features which are matched with the images. Image features that are most important to object recognition can be categorized into two types: edges and regions. Edges and surface patches are obtained from intensity and range images via independent algorithms. In this paper, we describe an algorithm that sets up a correspondence between the edge and surface features in a multiobject scene. Each edge is labeled with a list of surfaces to which it belongs and each surface is labeled with a list of edges that are on its boundary. This information can then be passed to higher level routines to group into individual objects for scene analysis. Both synthetic (with Gaussian noise) and real images containing multiple object scenes have been tested. Results appear quite encouraging.

Paper Details

Date Published: 10 October 1994
PDF: 10 pages
Proc. SPIE 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, (10 October 1994); doi: 10.1117/12.188932
Show Author Affiliations
Neelima Shrikhande, Central Michigan Univ. (United States)
Gongzhu Hu, Central Michigan Univ. (United States)


Published in SPIE Proceedings Vol. 2353:
Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision
David P. Casasent, Editor(s)

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