NDLI: Computational strategies for object recognition

Content Provider	ACM Digital Library
Author	Suetens, Paul Fua, Pascal Hanson, Andrew J.
Copyright Year	1992
Abstract	This article reviews the available methods for automated identification of objects in digital images. The techniques are classified into groups according to the nature of the computational strategy used. Four classes are proposed: (1) the simplest strategies, which work on data appropriate for feature vector classification, (2) methods that match models to symbolic data structures for situations involving reliable data and complex models, (3) approaches that fit models to the photometry and are appropriate for noisy data and simple models, and (4) combinations of these strategies, which must be adopted in complex situations. Representative examples of various methods are summarized, and the classes of strategies with respect to their appropriateness for particular applications.
Starting Page	5
Ending Page	62
Page Count	58
File Format	PDF
ISSN	03600300
e-ISSN	15577341
DOI	10.1145/128762.128763
Journal	ACM Computing Surveys (CSUR)
Volume Number	24
Issue Number	1
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1992-03-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Image understanding Model-based vision Object recognition
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computer Science

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Computational strategies for object recognition