23 December 1999 Feature localization and search by object model under illumination change
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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.
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Mark S. Drew, Zinovi Tauber, 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); doi: 10.1117/12.373572; https://doi.org/10.1117/12.373572
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