Learning object boundary detection from motion data
Author(s)
Ross, Michael G.
Kaelbling, Leslie P.
Date Issued
January 2003
Series/Report no.
Computer Science (CS);
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.
Subjects
machine learning
boundary detection
motion segmentation
loopy belief propagation
motion tracking algorithm
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