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• W1552436272 abstract "A significant barrier to applying the techniques of machine learning to the domain of object boundary detection is the need to obtain a large database of correctly labeled examples. Inspired by developmental psychology, this paper proposes that boundary detection can be learned from the output of a motion tracking algorithm that separates moving objects from their static surroundings. Motion segmentation solves the database problem by providing cheap, unlimited, labeled training data. A probabilistic model of the textural and shape properties of object boundaries can be trained from this data and then used to efficiently detect boundaries in novel images via loopy belief propagation." @default.
• W1552436272 created "2016-06-24" @default.
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• W1552436272 date "2003-01-01" @default.
• W1552436272 modified "2023-09-26" @default.
• W1552436272 title "Learning object boundary detection from motion data" @default.
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• W1552436272 hasPublicationYear "2003" @default.
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