Editorial for the Special Issue on Photometric Analysis for Computer Vision

Abstract : This special issue arises from papers presented at the Workshop on Photometric Analysis For Computer Vision, held on October 14, 2007 in conjunction with the 11th International Conference on Computer Vision Conference in Rio de Janeiro, Brazil. Photometric analysis is a central aspect of computer vision theory and practice. The way an image looks depends on many factors, including geometry, illumination and reflectance properties of the objects. For transparent or translucent objects, or for objects composed by multiple coatings, the factors are even more numerous (refraction, subsurface scattering, ...). The laws combining these components are diverse and complex. This complexity makes computer vision tasks even more difficult and typically causes the failure of methods based on simple models. A typical example could be problems caused by specularities in the stereo-vision problem; proposed methods usually assume that the scene is perfectly diffuse. Feature tracking and matching is another example since the photometric appearance of objects can change when they or the camera move. On the one hand, from a theoretical as well as from a computational point of view, a better understanding and handling of these factors should improve robustness to photometric effects. On the other hand, this allows not only to circumvent problems but also to gather valuable information which can be practically exploited in computer vision tasks. One example is the information provided by shading and shadows.
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Article dans une revue
International Journal of Computer Vision, Springer Verlag, 2010, 86 (2-3), pp.125-126. 〈http://springerlink.metapress.com/content/gr565g8821h22226/〉. 〈10.1007/s11263-009-0292-3〉
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Peter Belhumeur, Katsushi Ikeuchi, Emmanuel Prados, Stefano Soatto, Peter Sturm. Editorial for the Special Issue on Photometric Analysis for Computer Vision. International Journal of Computer Vision, Springer Verlag, 2010, 86 (2-3), pp.125-126. 〈http://springerlink.metapress.com/content/gr565g8821h22226/〉. 〈10.1007/s11263-009-0292-3〉. 〈inria-00511432〉

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