By Topic

Model-based perceptual grouping and shape abstraction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Sala, P. ; Univ. of Toronto, Toronto, ON ; Dickinson, S.J.

Contour features are re-emerging in the categorization community as it moves from appearance back to shape. However, the classical assumption of one-to-one correspondence between an extracted image contour and a model contour constrains category models to be highly brittle, offering little abstraction between image and model. Moreover, todaypsilas contour-based models are category-specific, offering no mechanism for contour grouping and abstraction in the absence of an object prior. We present a novel framework for recovering a set of abstract parts from a multi-scale contour image. Given a user-specified part vocabulary and an image to be analyzed, the system covers the image with abstract part models drawn from the vocabulary. More importantly, correspondence between image contours and part contours is many-to-one, yielding a powerful shape abstraction mechanism. We illustrate the strengths and weaknesses of this work in progress on a set of anecdotal scenes.

Published in:

Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. IEEE Computer Society Conference on

Date of Conference:

23-28 June 2008