The Experts below are selected from a list of 10944 Experts worldwide ranked by ideXlab platform

Peter Sturm - One of the best experts on this subject based on the ideXlab platform.

  • Editorial for the Special Issue on Photometric Analysis for Computer Vision
    International Journal of Computer Vision, 2017
    Co-Authors: Peter Belhumeur, Emmanuel Prados, Katsushi Ikeuchi, Stefano Soatto, Peter Sturm
    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.

Peter Belhumeur - One of the best experts on this subject based on the ideXlab platform.

  • Editorial for the Special Issue on Photometric Analysis for Computer Vision
    International Journal of Computer Vision, 2017
    Co-Authors: Peter Belhumeur, Emmanuel Prados, Katsushi Ikeuchi, Stefano Soatto, Peter Sturm
    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.

Hua Man - One of the best experts on this subject based on the ideXlab platform.

  • Semantic-based Scene Annotation Method of Sport Video
    Computer Engineering, 2010
    Co-Authors: Hua Man
    Abstract:

    This paper presents techniques and results on semantic annotation of sport video with active learning.It takes tennis video as examples and defines 6 scenes containing semantic concept.The mid-level features such as court line,court color percentage,domain motion,Camera Move pattern etc are extracted through analyzing the visual difference of semantic scenes.The Support Vector Machine(SVM) is used to classify the shots.The proposed method employs a clustering based active learning scheme.It performs most representative samples selection as well as to avoid repeatedly labeling samples in the same cluster.Experimental results show encouraging result compared with the traditional methods.

Emmanuel Prados - One of the best experts on this subject based on the ideXlab platform.

  • Editorial for the Special Issue on Photometric Analysis for Computer Vision
    International Journal of Computer Vision, 2017
    Co-Authors: Peter Belhumeur, Emmanuel Prados, Katsushi Ikeuchi, Stefano Soatto, Peter Sturm
    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.

Katsushi Ikeuchi - One of the best experts on this subject based on the ideXlab platform.

  • Editorial for the Special Issue on Photometric Analysis for Computer Vision
    International Journal of Computer Vision, 2017
    Co-Authors: Peter Belhumeur, Emmanuel Prados, Katsushi Ikeuchi, Stefano Soatto, Peter Sturm
    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.