Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/405852
Title: Clustering appearances of objects under varying illumination conditions
Authors: Ho, Jeffrey;Yang, Ming-Hsuan;Lim, Jongwoo;Lee, Kuang-Chih;Kriegman, David
subject: Science & Technology
Year: 2008
Abstract: We introduce two appearance-based methods for clustering a set of images of 3D (three-dimensional) objects, acquired under varying illumination conditions, into disjoint subsets corresponding to individual objects. The first algorithm is based on the concept of illumination cones. According to the theory, the clustering problem is equivalent to finding convex polyhedral cones in the high-dimensional image space. To efficiently determine the conic structures hidden in the image data, we introduce the concept of conic affinity, which measures the likelihood of a pair of images belonging to the same underlying polyhedral cone. For the second method, we introduce another affinity measure based on image gradient comparisons. The algorithm operates directly on the image gradients by comparing the magnitudes and orientations of the image gradient at each pixel. Both methods have clear geometric motivations, and they operate directly on the images without the need for feature extraction or computation of pixel statistics. We demonstrate experimentally that both algorithms are surprisingly effective in clustering images acquired under varying illumination conditions with two large, well-known image data sets.
Description: 
URI: http://localhost/handle/Hannan/376551
http://localhost/handle/Hannan/405852
ISSN: 0-7695-1900-8
Appears in Collections:2002-2008

Files in This Item:
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AL505322.pdf1.56 MBAdobe PDF
Title: Clustering appearances of objects under varying illumination conditions
Authors: Ho, Jeffrey;Yang, Ming-Hsuan;Lim, Jongwoo;Lee, Kuang-Chih;Kriegman, David
subject: Science & Technology
Year: 2008
Abstract: We introduce two appearance-based methods for clustering a set of images of 3D (three-dimensional) objects, acquired under varying illumination conditions, into disjoint subsets corresponding to individual objects. The first algorithm is based on the concept of illumination cones. According to the theory, the clustering problem is equivalent to finding convex polyhedral cones in the high-dimensional image space. To efficiently determine the conic structures hidden in the image data, we introduce the concept of conic affinity, which measures the likelihood of a pair of images belonging to the same underlying polyhedral cone. For the second method, we introduce another affinity measure based on image gradient comparisons. The algorithm operates directly on the image gradients by comparing the magnitudes and orientations of the image gradient at each pixel. Both methods have clear geometric motivations, and they operate directly on the images without the need for feature extraction or computation of pixel statistics. We demonstrate experimentally that both algorithms are surprisingly effective in clustering images acquired under varying illumination conditions with two large, well-known image data sets.
Description: 
URI: http://localhost/handle/Hannan/376551
http://localhost/handle/Hannan/405852
ISSN: 0-7695-1900-8
Appears in Collections:2002-2008

Files in This Item:
File SizeFormat 
AL505322.pdf1.56 MBAdobe PDF
Title: Clustering appearances of objects under varying illumination conditions
Authors: Ho, Jeffrey;Yang, Ming-Hsuan;Lim, Jongwoo;Lee, Kuang-Chih;Kriegman, David
subject: Science & Technology
Year: 2008
Abstract: We introduce two appearance-based methods for clustering a set of images of 3D (three-dimensional) objects, acquired under varying illumination conditions, into disjoint subsets corresponding to individual objects. The first algorithm is based on the concept of illumination cones. According to the theory, the clustering problem is equivalent to finding convex polyhedral cones in the high-dimensional image space. To efficiently determine the conic structures hidden in the image data, we introduce the concept of conic affinity, which measures the likelihood of a pair of images belonging to the same underlying polyhedral cone. For the second method, we introduce another affinity measure based on image gradient comparisons. The algorithm operates directly on the image gradients by comparing the magnitudes and orientations of the image gradient at each pixel. Both methods have clear geometric motivations, and they operate directly on the images without the need for feature extraction or computation of pixel statistics. We demonstrate experimentally that both algorithms are surprisingly effective in clustering images acquired under varying illumination conditions with two large, well-known image data sets.
Description: 
URI: http://localhost/handle/Hannan/376551
http://localhost/handle/Hannan/405852
ISSN: 0-7695-1900-8
Appears in Collections:2002-2008

Files in This Item:
File SizeFormat 
AL505322.pdf1.56 MBAdobe PDF