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25 January 2011 Object classification by color normalization or calibration?
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Proceedings Volume 7866, Color Imaging XVI: Displaying, Processing, Hardcopy, and Applications; 78660K (2011)
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States
Model-based approaches to object recognition rely on shape and contours while appearance-based approaches use information provided by the object intensity or color. Color histograms as an object characteristics are commonly used to solve this task. TheRGBcolor values formed by a camera depend heavily on the image formation process - especially the illumination involved. Mainly for this reason color normalization algorithms are applied to estimate the impact of position and color of the illumination and eliminate or at least minimize their influence to the image appearance. Providing information about the image acquisition settings another color normalization is applicable: color calibration. We compare several color normalization procedures to a colorimetric calibration method proposed by Raymond L. Lee, Jr. By estimating the spectral reflectance of object surfaces one obtain a colorimetrically correct image representation. The impact of color normalization on the recognition rates is explored and is set in contrast to a calibration approach. Additionally our experiments test several histogram distance measures for histogram based object recognition. We vary the number of bins, the order of two processing steps, and the dimensionality of color histograms to determine a most suitable parameter setting for object recognition.
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Wolfram Hans and Dietrich Paulus "Object classification by color normalization or calibration?", Proc. SPIE 7866, Color Imaging XVI: Displaying, Processing, Hardcopy, and Applications, 78660K (25 January 2011);

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