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Intro -- CONTENTS -- Dedication -- Preface -- PART 1: THEORY, TECHNOLOGY AND SYSTEMS -- A Brief Introduction to Part 1 (by C.H. Chen) -- Chapter 1.1 Optimal Statistical Classification -- 1 Introduction -- 2 Optimal Bayesian Classifier -- 2.1 OBC Design -- 3 OBC for the Discrete Model -- 4 OBC for the Gaussian Model -- 5 Multi-class Classification -- 5.1 Optimal Bayesian Risk Classification -- 6 Prior Construction -- 7 Optimal Bayesian Transfer Learning -- 8 Conclusion -- References -- Chapter 1.2 Deep Discriminative Feature Learning Method for Object Recognition -- 1. Introduction -- 2. Entropy-Orthogonality Loss Based Deep Discriminative Feature Learning Method -- 2.1. Framework -- 2.2. Entropy-Orthogonality Loss (EOL) -- 2.3. Optimization -- 3. Min-Max Loss Based Deep Discriminative Feature Learning Method -- 3.1. Framework -- 3.2. Min-Max Loss -- 3.2.1. Min-Max Loss Based on Intrinsic and Penalty Graphs -- 3.2.2. Min-Max Loss Based on Within-Manifold and Between-Manifold Distances -- 3.3. Optimization -- 3.3.1. Optimization for Min-Max Loss Based on Intrinsic and Penalty Graphs -- 3.3.2. Optimization for Min-Max Loss Based on Within-Manifold and Between-Manifold Distances -- 4. Experiments with Image Classification Task -- 4.1. Experimental Setups -- 4.2. Datasets -- 4.3. Experiments using QCNN Model -- 4.4. Experiments using NIN Model -- 4.5. Feature Visualization -- 5. Discussions -- References -- Chapter 1.3 Deep Learning Based Background Subtraction: A Systematic Survey -- 1. Introduction -- 2. Background Subtraction -- 2.1. Convolutional Neural Networks -- 2.2. Multi-scale and Cascaded CNNs -- 2.3. Fully CNNs -- 2.4. Deep CNNs -- 2.5. Structured CNNs -- 2.6. 3D CNNs -- 2.7. Generative Adversarial Networks (GANs) -- 3. Experimental Results -- 4. Conclusion -- References.