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A. Gijsenij, Theo Gevers, & Joost Van de Weijer. (2010). Generalized Gamut Mapping using Image Derivative Structures for Color Constancy. IJCV - International Journal of Computer Vision, 86(2-3), 127–139.
Abstract: The gamut mapping algorithm is one of the most promising methods to achieve computational color constancy. However, so far, gamut mapping algorithms are restricted to the use of pixel values to estimate the illuminant. Therefore, in this paper, gamut mapping is extended to incorporate the statistical nature of images. It is analytically shown that the proposed gamut mapping framework is able to include any linear filter output. The main focus is on the local n-jet describing the derivative structure of an image. It is shown that derivatives have the advantage over pixel values to be invariant to disturbing effects (i.e. deviations of the diagonal model) such as saturated colors and diffuse light. Further, as the n-jet based gamut mapping has the ability to use more information than pixel values alone, the combination of these algorithms are more stable than the regular gamut mapping algorithm. Different methods of combining are proposed. Based on theoretical and experimental results conducted on large scale data sets of hyperspectral, laboratory and realworld scenes, it can be derived that (1) in case of deviations of the diagonal model, the derivative-based approach outperforms the pixel-based gamut mapping, (2) state-of-the-art algorithms are outperformed by the n-jet based gamut mapping, (3) the combination of the different n-jet based gamut
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A. Gijsenij, Theo Gevers, & Joost Van de Weijer. (2009). Physics-based Edge Evaluation for Improved Color Constancy. In 22nd IEEE Conference on Computer Vision and Pattern Recognition (581 – 588).
Abstract: Edge-based color constancy makes use of image derivatives to estimate the illuminant. However, different edge types exist in real-world images such as shadow, geometry, material and highlight edges. These different edge types may have a distinctive influence on the performance of the illuminant estimation.
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A. Gijsenij, Theo Gevers, & Joost Van de Weijer. (2008). Edge Classification for Color Constancy. In 4th European Conference on Colour in Graphics, Imaging and Vision Proceedings (231–234).
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Abstract: International Symposium on Wood Structure in Plant Biology and Ecology
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A. Martinez, J.R. Serra, Jordi Vitria, & J.B. Subirana. (1997). An Iconic Representation for Image Retrieval.
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A. Martinez, & Jordi Vitria. (2001). Clustering in Image Space for Place Recognition and Visiual Annotations for Human-Robot Interaction. IEEE Trans. on Systems, Man, and Cybernatics–Part B: Cybernetics, 31(5):669–682 (IF: 0.789).
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A. Martinez, & Jordi Vitria. (2000). Learning mixture models using a genetic version of the EM algorithm. PRL - Pattern Recognition Letters, 21(8), 759–769.
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A. Martinez, & Jordi Vitria. (1998). Learning mixture models with the EM algorithm and genetic algorithms.
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A. Martinez, & Jordi Vitria. (1998). Visual annotation for mobile robot navigation.
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A. Martinez, & Jordi Vitria. (1997). From Visual Scanning to Object Recognition. In (SNRFAI’97) 7th Spanish National Symposium on Pattern Recognition and Image Analysis..
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