http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Learning Approaches in Signal Processing
Coupled with machine learning, the use of signal processing techniques for big data analysis, Internet of things, smart cities, security, and bio-informatics applications has witnessed explosive growth. This has been made possible via fast algorithms on data, speech, image, and video processing with advanced GPU technology. This book presents an up-to-date tutorial and overview on learning technologies such as random forests, sparsity, and low-rank matrix estimation andcutting-edge visual/signal processing techniques, including face recognition, Kalman filtering, and multirate DSP. It discusses the applications that make use of deep learning, convolutional neural networks, random forests, etc. The applications include super-resolution imaging, fringe projection profilometry, human activities detection/capture, gesture recognition, spoken language processing, cooperative networks, bioinformatics, DNA, and healthcare.
Cover; Half Title; Title Page; Copyright Page; Table of Contents; Preface; PART I: TUTORIAL AND OVERVIEW OF LEARNING APPROACHES; 1: Introduction to Random Tree and Random Forests for Fast Signal Processing and Object Classification; 1.1 Introduction; 1.1.1 Split Functions with Binary Tests; 1.1.2 Definition and Structure of Random Trees; 1.1.3 Random Forests; 1.2 Object Classification; 1.2.1 Training Stage; 1.2.2 Testing Stage; 1.3 Random Tree for Shadow Detection; 1.3.1 Training Stage; 1.3.2 Testing Stage and Experimental Results; 1.4 Random Forests for Light Rail Vehicle Detection
1.4.1 Training Stage1.4.1.1 Prediction Model; 1.4.2 Further Experimental Results; 1.5 Review of Some RelatedWorks; 1.6 Conclusion; 2: Sparsity Based Dictionary Learning Techniques; 2.1 Introduction; 2.2 Brief History of Dictionary-Based Signal Processing; 2.2.1 K-means and KLine; 2.2.2 Method of Optimal Directions; 2.2.3 Union of Orthobases; 2.2.4 Generalized Principal Component Analysis; 2.2.5 K Times Singular Value Decomposition; 2.2.6 Efficient Implementation of the K Times Singular Value Decomposition Algorithm; 2.2.7 Sparse K Times Singular Value Decomposition; 2.2.8 Challenges
2.3 Regularized K Times Sum of OptimallyWeighted Vectors2.4 Dictionary Atom with Smoothness Constraint; 2.5 Gray Image De-Noising; 2.6 Discriminative K Times Sum of OptimallyWeighted Vectors; 2.7 Fast RKSOWV; 2.8 Conclusion; 3: A Comprehensive Survey of Persistent Homology for Pattern Recognition; 3.1 Introduction; 3.1.1 Motivation; 3.1.2 Persistent Homology; 3.1.3 Previous Work; 3.2 Background; 3.2.1 Preliminary of Group Theory; 3.2.2 Simplicial Complex; 3.2.3 Homology Groups of Simplicial Complexes; 3.2.4 Topological Invariant; 3.2.5 Persistent Homology; 3.2.6 Barcode and Diagram
3.3 Persistent Homology in Pattern Recognition3.3.1 Taxonomy; 3.3.2 Complex Modeling; 3.3.3 Function Construction; 3.3.4 Sample Assumption; 3.4 Perspectives and Open Directions; 3.4.1 Multi-Dimensional Persistent Homology; 3.4.2 Persistent Homology and Machine Learning; 3.5 Conclusion; 4: Low-Rank Matrix Estimation and Its Applications in Signal Processing and Machine Learning; 4.1 Low-Rank Matrix Estimation Models; 4.2 Applications of Low-Rank Models in Signal Processing and Machine Learning; 4.2.1 Infinite Impulse Response Digital Filter Design; 4.2.1.1 Design model; 4.2.1.2 Design example
4.2.2 Time-Difference-of-Arrival Estimation4.2.2.1 TDOA estimation model; 4.2.2.2 ADMM for TDOA estimation; 4.2.2.3 Experimental results; 4.2.3 Sparse Common Spatial Pattern for Feature Extraction in Brain-Computer Interface; 4.2.3.1 Traditional CSP; 4.2.3.2 Sparse CSP; 4.2.3.3 Real data experiment; 4.3 Summary; 5: Introduction to Face Recognition and Recent Work; 5.1 Introduction; 5.2 Face Detection and Face Alignment; 5.2.1 Face Detection; 5.2.2 Face Alignment; 5.3 Face Recognition; 5.3.1 The Eigenface Approach; 5.3.1.1 Main idea behind eigenfaces
Pan Stanford series on Digital signal processing ; vol. 2