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020 _a9781447167143
_z978-1-4471-6714-3
024 7 _a10.1007/978-1-4471-6714-3
_2doi
050 4 _aQ337.5
050 4 _aTK7882.P3
072 7 _aUYQP
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYQP
_2thema006.4
_223
100 1 _aCheng, Hong.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aSparse Representation, Modeling and Learning in Visual Recognition :
_bTheory, Algorithms and Applications /
_cby Hong Cheng.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2015.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 0 _aAdvances in Computer Vision and Pattern Recognition,
_x2191-6586
505 0 _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.
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.
650 0 _aOptical pattern recognition.
650 0 _aComputer vision.
650 0 _aArtificial intelligence.
650 1 4 _aPattern Recognition.
_0http://scigraph.springernature.com/things/product-market-codes/I2203X
650 2 4 _aImage Processing and Computer Vision.
_0http://scigraph.springernature.com/things/product-market-codes/I22021
650 2 4 _aArtificial Intelligence.
_0http://scigraph.springernature.com/things/product-market-codes/I21000
710 2 _aSpringerLink (Online service)
856 4 0 _uhttps://doi.org/10.1007/978-1-4471-6714-3
_3Springer eBooks
_zOnline access link to the resource
912 _aZDB-2-SCS
999 _c200433846
_d52058
942 _2lcc
_cEBK
041 _aeng