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Issue No.05 - May (2012 vol.34)
pp: 918-929
J. van de Weijer , Comput. Vision Center (CVC), Univ. Autonoma de Barcelona, Cerdanyola, Spain
ABSTRACT
Edge-based color constancy methods make use of image derivatives to estimate the illuminant. However, different edge types exist in real-world images, such as material, shadow, and highlight edges. These different edge types may have a distinctive influence on the performance of the illuminant estimation. Therefore, in this paper, an extensive analysis is provided of different edge types on the performance of edge-based color constancy methods. First, an edge-based taxonomy is presented classifying edge types based on their photometric properties (e.g., material, shadow-geometry, and highlights). Then, a performance evaluation of edge-based color constancy is provided using these different edge types. From this performance evaluation, it is derived that specular and shadow edge types are more valuable than material edges for the estimation of the illuminant. To this end, the (iterative) weighted Gray-Edge algorithm is proposed in which these edge types are more emphasized for the estimation of the illuminant. Images that are recorded under controlled circumstances demonstrate that the proposed iterative weighted Gray-Edge algorithm based on highlights reduces the median angular error with approximately 25 percent. In an uncontrolled environment, improvements in angular error up to 11 percent are obtained with respect to regular edge-based color constancy.
INDEX TERMS
lighting, edge detection, Gray codes, image colour analysis, iterative methods, median angular error, photometric edge weighting, edge-based color constancy methods, image derivatives, illuminant estimation, edge-based taxonomy, performance evaluation, shadow edge, specular edge, iterative weighted Gray-Edge algorithm, Lighting, Color, Image edge detection, edge classification., Color constancy, illuminant estimation, Gray Edge
CITATION
J. van de Weijer, "Improving Color Constancy by Photometric Edge Weighting", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.34, no. 5, pp. 918-929, May 2012, doi:10.1109/TPAMI.2011.197
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