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020 a| 9781598291001;

020 a| 1598291009 (Perfect)c| USD 40.00 Retail Price (American International Distribution Corporation)9| Active Record;

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035 a| (WaSeSS)ssj0000328599;

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100 1 a| Mordohai, Philippose| Author?| UNAUTHORIZED;

245 10 a| Tensor Votingh| [electronic resource]:b| A Perceptual Organization Approach to Computer Vision and Machine Learning;

260 a| San Rafael : b| Morgan & Claypool Publishersa| Williston : b| American International Distribution Corporation [Distributor];

300 a| 136 p.b| illc| 09.250 x 07.500 in.;

440 0 a| Synthesis Lectures on Image, Video, and Multimedia Processing Ser.;

506 a| Available only to authorized users.;

520 8 a| Annotationb| This lecture presents research on a general framework for perceptual organization that was contacted mainly at the Institute for Robotics and Intelligent Systems of the University of Southern California. It is not written as a historical recount of the work, since the sequence of the presentation is not in chronological order. It aims at presenting an approach to a wide range of problems in computer vision and machine learning that is data-driven, local and requires a minimal number of assumptions. The tensor voting framework combines these properties and provides a unified perceptual organization methodology applicable in situations that may seem heterogeneous initially. Authors Philippos Mordohai and Gerard Medioni show how several problems can be posed as the organization of the inputs into salient perceptual structures, which are inferred via tensor voting. The book extends the original tensor voting framework with the addition of boundary inference capabilities, a novel re-formulation of the framework applicable to high-dimensional spaces and the development of algorithms for computer vision and machine learning problems. The authors provide complete analysis for some problems and briefly outline the approach for other applications and provide references to relevant sources.;

538 a| Mode of access: World Wide Web;

650 4 a| Computer Vision;

650 4 a| Machine Learning;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Medioni, Gerarde| Author?| UNAUTHORIZED;

700 1 a| Bovik, Alan C.e| Contribution by4| oth?| UNAUTHORIZED;

856 40 z| Full text available from Computer & Information Science Collection Oneu| https://go.openathens.net/redirector/ecu.edu?url=http%3A%2F%2Fdx.doi.org%2F10.2200%2FS00049ED1V01Y200609IVM008;

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Tensor Voting A Perceptual Organization Approach to Computer Vision and Machine Learning

Author/creator	Mordohai, Philippos Author
Other author	Medioni, Gerard Author
Other author	Bovik, Alan C. Contribution by
Format	Electronic
Publication Info	San Rafael : Morgan & Claypool Publishers Williston : American International Distribution Corporation [Distributor]
Description	136 p. ill 09.250 x 07.500 in.
Supplemental Content	Full text available from Computer & Information Science Collection One
Subjects	Computer Vision Machine Learning

Series	Synthesis Lectures on Image, Video, and Multimedia Processing Ser.
Summary	Annotation This lecture presents research on a general framework for perceptual organization that was contacted mainly at the Institute for Robotics and Intelligent Systems of the University of Southern California. It is not written as a historical recount of the work, since the sequence of the presentation is not in chronological order. It aims at presenting an approach to a wide range of problems in computer vision and machine learning that is data-driven, local and requires a minimal number of assumptions. The tensor voting framework combines these properties and provides a unified perceptual organization methodology applicable in situations that may seem heterogeneous initially. Authors Philippos Mordohai and Gerard Medioni show how several problems can be posed as the organization of the inputs into salient perceptual structures, which are inferred via tensor voting. The book extends the original tensor voting framework with the addition of boundary inference capabilities, a novel re-formulation of the framework applicable to high-dimensional spaces and the development of algorithms for computer vision and machine learning problems. The authors provide complete analysis for some problems and briefly outline the approach for other applications and provide references to relevant sources.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
ISBN	9781598291001
ISBN	1598291009 (Perfect) Active Record
Standard identifier#	9781598291001
Stock number	01307586

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Tensor Voting A Perceptual Organization Approach to Computer Vision and Machine Learning

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