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Proceedings Paper

Using a computational model of human color vision to perform object segmentation in natural images
Author(s): John Arthur Black Jr.; Karthikeyan Vaithianathan; Sethuraman Panchanathan
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Paper Abstract

Most recent attempts at object segmentation have been based on object motion in video sequences. However, object segmentation in still images is more difficult. Without motion cues, other cues must be found. Edge detection algorithms are able to extract object contours from images, and were once thought to hold promise for object segmentation in still images. However, additional processing is needed to distinguish between object contours and other edges, such as those produced by textures. The alternative method of region growing (based on luminance or color) has also proven rather ineffective for object segmentation in natural images. In contrast, humans are very successful at object segmentation in still images, suggesting that a model of the early human visual system (HVS) might reveal useful methods for more robust object segmentation in still images. The research results presented in this paper are derived from an HVS model that includes models of Type 1 and Type 2 color contrast cells, and double opponent color contrast cells. By combining the outputs of these cells with edge detected images, object contours can be better distinguished from other contours (such as texture contours and shadow contours) thus providing enhanced object segmentation in cluttered images.

Paper Details

Date Published: 30 May 2002
PDF: 12 pages
Proc. SPIE 4662, Human Vision and Electronic Imaging VII, (30 May 2002); doi: 10.1117/12.469522
Show Author Affiliations
John Arthur Black Jr., Arizona State Univ. (United States)
Karthikeyan Vaithianathan, Arizona State Univ. (United States)
Sethuraman Panchanathan, Arizona State Univ. (United States)

Published in SPIE Proceedings Vol. 4662:
Human Vision and Electronic Imaging VII
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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