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Sparse Representation, Modeling and Learning in Visual Recognition

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Table of Contents

Part 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

About the Author

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.

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