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• W2110494098 abstract "The goal of this work is to discriminatively learn contour fragment descriptors for the task of object detection. Unlike previous methods that incorporate learning techniques only for object model generation or for verification after detection, we present a holistic object detection system using solely shape as underlying cue. In the learning phase, we interrelate local shape descriptions (fragments) of the object contour with the corresponding spatial location of the object centroid. We introduce a novel shape fragment descriptor that abstracts spatially connected edge points into a matrix consisting of angular relations between the points. Our proposed descriptor fulfills important properties like distinctiveness, robustness and insensitivity to clutter. During detection, we hypothesize object locations in a generalized Hough voting scheme. The back-projected votes from the fragments allow to approximately delineate the object contour. We evaluate our method e.g. on the well-known ETHZ shape data base, where we achieve an average detection score of 87:5% at 1:0 FPPI only from Hough voting, outperforming the highest scoring Hough voting approaches by almost 8%." @default.
• W2110494098 created "2016-06-24" @default.
• W2110494098 creator A5011359067 @default.
• W2110494098 creator A5011444017 @default.
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• W2110494098 date "2011-01-01" @default.
• W2110494098 modified "2023-09-28" @default.
• W2110494098 title "Discriminative Learning of Contour Fragments for Object Detection" @default.
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• W2110494098 doi "https://doi.org/10.5244/c.25.4" @default.
• W2110494098 hasPublicationYear "2011" @default.
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