NDLI: Learning Object Shape: From Drawings to Images

Content Provider	IEEE Xplore Digital Library
Author	Elidan, G. Heitz, G. Koller, D.
Copyright Year	2006
Description	Author affiliation: Stanford University (Elidan, G.)
Abstract	We consider the important challenge of recognizing a variety of deformable object classes in images. Of fundamental importance and particular difficulty in this setting is the problem of "outlining" an object, rather than simply deciding on its presence or absence. A major obstacle in learning a model that will allow us to address this task is the need for hand-segmented training images. In this paper we present a novel landmark-based, piecewise-linear model of the shape of an object class. We then formulate a learning approach that allows us to learn this model with minimal user supervision. We circumvent the need for hand-segmentation by transferring the shape "essence" of an object from drawings to complex images. We show that our method is able to automatically and effectively learn and localize a variety of object classes.
Starting Page	2064
Ending Page	2071
File Size	786299
Page Count	8
File Format	PDF
ISBN	0769525970
ISSN	10636919
DOI	10.1109/CVPR.2006.171
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image recognition Computer science Computer vision Shape measurement Engineering drawings Piecewise linear techniques Performance evaluation Layout Deformable models Markov random fields
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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