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000727294 020__ $$a9781447167143$$qelectronic book
000727294 020__ $$a1447167147$$qelectronic book
000727294 020__ $$z9781447167136
000727294 0247_ $$a10.1007/978-1-4471-6714-3$$2doi
000727294 035__ $$aSP(OCoLC)ocn910159355
000727294 035__ $$aSP(OCoLC)910159355
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000727294 050_4 $$aTA1634$$b.C44 2015eb
000727294 08204 $$a006.4/2$$223
000727294 1001_ $$aCheng, Hong,$$eauthor.
000727294 24510 $$aSparse representation, modeling and learning in visual recognition$$h[electronic resource] :$$btheory, algorithms and applications /$$cHong Cheng.
000727294 264_1 $$aLondon :$$bSpringer,$$c[2015]
000727294 264_4 $$c©2015
000727294 300__ $$a1 online resource.
000727294 336__ $$atext$$btxt$$2rdacontent
000727294 337__ $$acomputer$$bc$$2rdamedia
000727294 338__ $$aonline resource$$bcr$$2rdacarrier
000727294 4901_ $$aAdvances in computer vision and pattern recognition
000727294 504__ $$aIncludes bibliographical references and index.
000727294 5050_ $$aPart I: Introduction and Fundamentals -- Introduction -- The Fundamentals of Compressed Sensing -- Part II: Sparse Representation, Modeling and Learning -- Sparse Recovery Approaches -- Robust Sparse Representation, Modeling and Learning -- Efficient Sparse Representation and Modeling -- Part III: Visual Recognition Applications -- Feature Representation and Learning -- Sparsity Induced Similarity -- Sparse Representation and Learning Based Classifiers -- Part IV: Advanced Topics -- Beyond Sparsity -- Appendix A: Mathematics -- Appendix B: Computer Programming Resources for Sparse Recovery Approaches -- Appendix C: The source Code of Sparsity Induced Similarity -- Appendix D: Derivations.
000727294 506__ $$aAccess limited to authorized users.
000727294 520__ $$aThis unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: Provides a thorough introduction to the fundamentals of sparse representation, modeling and learning, and the application of these techniques in visual recognition Describes sparse recovery approaches, robust and efficient sparse representation, and large-scale visual recognition Covers feature representation and learning, sparsity induced similarity, and sparse representation and learning-based classifiers Discusses low-rank matrix approximation, graphical models in compressed sensing, collaborative representation-based classification, and high-dimensional nonlinear learning Includes appendices outlining additional computer programming resources, and explaining the essential mathematics required to understand the book Researchers and graduate students interested in computer vision, pattern recognition and robotics will find this work to be an invaluable introduction to techniques of sparse representations and compressive sensing. Dr. Hong Cheng is Professor in the School of Automation Engineering, and Deputy Executive Director of the Center for Robotics at the University of Electronic Science and Technology of China. His other publications include the Springer book Autonomous Intelligent Vehicles.
000727294 588__ $$aOnline resource; title from PDF title page (viewed May 28, 2015).
000727294 650_0 $$aComputer vision.
000727294 650_0 $$aVisual perception.
000727294 650_0 $$aImage processing.
000727294 650_0 $$aSignal processing$$xDigital techniques$$xMathematics.
000727294 77608 $$iPrint version:$$z9781447167136
000727294 830_0 $$aAdvances in computer vision and pattern recognition.
000727294 852__ $$bebk
000727294 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-1-4471-6714-3$$zOnline Access$$91397441.1
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000727294 980__ $$aEBOOK
000727294 980__ $$aBIB
000727294 982__ $$aEbook
000727294 983__ $$aOnline
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