Skeletal shape abstraction from examples.

Full Abstract

Learning a class prototype from a set of exemplars is an important challenge facing researchers in object categorization. Although the problem is receiving growing interest, most approaches assume a one-to-one correspondence among local features, restricting their ability to learn true abstractions of a shape. In this paper, we present a new technique for learning an abstract shape prototype from a set of exemplars whose features are in many-to-many correspondence. Focusing on the domain of 2D shape, we represent a silhouette as a medial axis graph whose nodes correspond to "parts" defined by medial branches and whose edges connect adjacent parts. Given a pair of medial axis graphs, we establish a many-to-many correspondence between their nodes to find correspondences among articulating parts. Based on these correspondences, we recover the abstracted medial axis graph along with the positional and radial attributes associated with its nodes. We evaluate the abstracted prototypes in the context of a recognition task.

Author information

Author/s: Fatih Demirci, M (M); Shokoufandeh, Ali (A); Dickinson, Sven J (SJ);

Affiliation: Department of Computer Engineering, TOBB University of Economics and Technology, Sogutozu Cad. No.: 43, Ankara 06560, Turkey. mfdemirci(-atsign-)etu.edu.tr

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: IEEE transactions on pattern analysis and machine intelligence (IEEE Trans Pattern Anal Mach Intell), published in United States. (Language: eng)

Reference: 2009-May; vol 31 (issue 5) : pp 944-52

Dates: Created 2009/03/20; Completed 2009/06/10;

PMID: 19299866, status: MEDLINE (last retrieval date: 6/10/2009, IMS Date: 10 Jun 2009 00:00:00)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Artificial Intelligence
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods

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