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23 December 1999 Feature localization and search by object model under illumination change
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Proceedings Volume 3972, Storage and Retrieval for Media Databases 2000; (1999) https://doi.org/10.1117/12.373572
Event: Electronic Imaging, 2000, San Jose, CA, United States
Abstract
Color objects recognition methods that are based on image retrieval algorithms can handle changes of illumination via image normalization, e.g. simple color-channel-normalization or by forming a doubly-stochastic image matrix. However these methods fail if the object sought is surrounded by clutter. Rather than directly trying to find the target, a viable approach is to grow a small number of feature regions called locales. These are defined as a non-disjoint coarse localization based on image tiles. In this paper, locales are grown based on chromaticity, which is more insensitive to illumination change than is color. Using a diagonal model of illumination changes, a least-squares optimization on chromaticity recovers the best set of diagonal coefficients for candidate assignments from model to test locales sorted in a database. If locale centroids are also sorted then, adapting a displacement model to include model locale weights, transformed pose and scale can be recovered. Tests on databases of real images show promising results for objects query.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mark S. Drew, Zinovi Tauber, and Ze-Nian Li "Feature localization and search by object model under illumination change", Proc. SPIE 3972, Storage and Retrieval for Media Databases 2000, (23 December 1999); https://doi.org/10.1117/12.373572
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