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Proceedings Paper

Unsupervised image segmentation by automatic gradient thresholding for dynamic region growth in the CIE L*a*b* color space
Author(s): Sreenath Rao Vantaram; Eli Saber; Vincent Amuso; Mark Shaw; Ranjit Bhaskar
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Paper Abstract

In this paper, we propose a novel unsupervised color image segmentation algorithm named GSEG. This Gradient-based SEGmentation method is initialized by a vector gradient calculation in the CIE L*a*b* color space. The obtained gradient map is utilized for initially clustering low gradient content, as well as automatically generating thresholds for a computationally efficient dynamic region growth procedure, to segment regions of subsequent higher gradient densities in the image. The resultant segmentation is combined with an entropy-based texture model in a statistical merging procedure to obtain the final result. Qualitative and quantitative evaluation of our results on several hundred images, utilizing a recently proposed evaluation metric called the Normalized Probabilistic Rand index shows that the GSEG algorithm is robust to various image scenarios and performs favorably against published segmentation techniques.

Paper Details

Date Published: 10 February 2009
PDF: 11 pages
Proc. SPIE 7240, Human Vision and Electronic Imaging XIV, 724019 (10 February 2009); doi: 10.1117/12.805416
Show Author Affiliations
Sreenath Rao Vantaram, Rochester Institute of Technology (United States)
Eli Saber, Rochester Institute of Technology (United States)
Vincent Amuso, Rochester Institute of Technology (United States)
Mark Shaw, Hewlett-Packard Co. (United States)
Ranjit Bhaskar, Hewlett-Packard Co. (United States)

Published in SPIE Proceedings Vol. 7240:
Human Vision and Electronic Imaging XIV
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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