Visual object recognition

The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and i...

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Bibliographic Details
Author / Creator:Grauman, Kristen Lorraine, 1979-
Imprint:San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, c2011.
Description:1 electronic text (xvii, 163 p.) : ill., digital file.
Language:English
Series:Synthesis lectures on artificial intelligence and machine learning, 1939-4616 ; # 11
Synthesis digital library of engineering and computer science.
Synthesis lectures on artificial intelligence and machine learning, # 11.
Subject:Computer vision.
Pattern recognition systems.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/10511006
Hidden Bibliographic Details
Other authors / contributors:Leibe, Bastian.
ISBN:9781598299694 (electronic bk.)
9781598299687 (pbk.)
Notes:Part of: Synthesis digital library of engineering and computer science.
Series from website.
Includes bibliographical references (p. 133-162).
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
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Also available in print.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
Summary:The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization.
Standard no.:10.2200/S00332ED1V01Y201103AIM011