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Author |
Maria Vanrell; Jordi Vitria |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Optimal 3x3 decomposable disks for morphological transformations |
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1997 |
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Image and Vision Computing |
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15 |
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11 |
Pages ![sorted by First Page field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
845-854 |
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OR;CIC;MV |
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BCNPCL @ bcnpcl @ VaV1997c |
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543 |
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Author |
Eduard Vazquez; Ramon Baldrich; Joost Van de Weijer; Maria Vanrell |
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![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Describing Reflectances for Colour Segmentation Robust to Shadows, Highlights and Textures |
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Journal Article |
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2011 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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33 |
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5 |
Pages ![sorted by First Page field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
917-930 |
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The segmentation of a single material reflectance is a challenging problem due to the considerable variation in image measurements caused by the geometry of the object, shadows, and specularities. The combination of these effects has been modeled by the dichromatic reflection model. However, the application of the model to real-world images is limited due to unknown acquisition parameters and compression artifacts. In this paper, we present a robust model for the shape of a single material reflectance in histogram space. The method is based on a multilocal creaseness analysis of the histogram which results in a set of ridges representing the material reflectances. The segmentation method derived from these ridges is robust to both shadow, shading and specularities, and texture in real-world images. We further complete the method by incorporating prior knowledge from image statistics, and incorporate spatial coherence by using multiscale color contrast information. Results obtained show that our method clearly outperforms state-of-the-art segmentation methods on a widely used segmentation benchmark, having as a main characteristic its excellent performance in the presence of shadows and highlights at low computational cost. |
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Los Alamitos; CA; USA; |
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IEEE Computer Society |
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0162-8828 |
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Admin @ si @ VBW2011 |
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1715 |
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Author |
Arjan Gijsenij; Theo Gevers; Joost Van de Weijer |
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Title |
Improving Color Constancy by Photometric Edge Weighting |
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Journal Article |
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2012 |
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IEEE Transaction on Pattern Analysis and Machine Intelligence |
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TPAMI |
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34 |
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5 |
Pages ![sorted by First Page field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
918-929 |
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: 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 Grey-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 Grey-Edge algorithm based on highlights reduces the median angular error with approximately $25\%$. In an uncontrolled environment, improvements in angular error up to $11\%$ are obtained with respect to regular edge-based color constancy. |
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Los Alamitos; CA; USA; |
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0162-8828 |
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CIC;ISE |
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Admin @ si @ GGW2012 |
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1850 |
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Author |
Daniel Ponsa; Robert Benavente; Felipe Lumbreras; Judit Martinez; Xavier Roca |
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Title |
Quality control of safety belts by machine vision inspection for real-time production |
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2003 |
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Optical Engineering (IF: 0.877) |
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42 |
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4 |
Pages ![sorted by First Page field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
1114-1120 |
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SPIE |
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ADAS;ISE;CIC |
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ADAS @ adas @ PRL2003 |
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399 |
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Author |
Graham D. Finlayson; Javier Vazquez; Sabine Süsstrunk; Maria Vanrell |
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Title |
Spectral sharpening by spherical sampling |
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Journal Article |
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Year |
2012 |
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Journal of the Optical Society of America A |
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JOSA A |
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29 |
Issue |
7 |
Pages ![sorted by First Page field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
1199-1210 |
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There are many works in color that assume illumination change can be modeled by multiplying sensor responses by individual scaling factors. The early research in this area is sometimes grouped under the heading “von Kries adaptation”: the scaling factors are applied to the cone responses. In more recent studies, both in psychophysics and in computational analysis, it has been proposed that scaling factors should be applied to linear combinations of the cones that have narrower support: they should be applied to the so-called “sharp sensors.” In this paper, we generalize the computational approach to spectral sharpening in three important ways. First, we introduce spherical sampling as a tool that allows us to enumerate in a principled way all linear combinations of the cones. This allows us to, second, find the optimal sharp sensors that minimize a variety of error measures including CIE Delta E (previous work on spectral sharpening minimized RMS) and color ratio stability. Lastly, we extend the spherical sampling paradigm to the multispectral case. Here the objective is to model the interaction of light and surface in terms of color signal spectra. Spherical sampling is shown to improve on the state of the art. |
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1084-7529 |
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Admin @ si @ FVS2012 |
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2000 |
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