"Cover"--"Half tilte"--"Series title" -- "Title page" -- "Copyright" -- "Dedication" -- "Contents" -- "Preface" -- "Acknowledgments" -- "Authors" -- "Chapter 1: Introduction to Visual Computing" -- " Image Representation Basics" -- "Transform-Domain Representations" -- " Image Histograms" -- " Image Gradients and Edges" -- "Going beyond Image Gradients" -- " Line Detection Using the Hough Transform" -- " Harris Corners" -- "Scale-Invariant Feature Transform" -- " Histogram of Oriented Gradients" -- "Decision-Making in a Hand-Crafted Feature Space" -- "Bayesian Decision-Making" -- "Decision-Making with Linear Decision Boundaries" -- "A Case Study with Deformable Part Models" -- "Migration toward Neural Computer Vision" -- "Summary" -- "References" -- "Chapter 2: Learning As a Regression Problem" -- "Supervised Learning" -- "Linear Models" -- "Least Squares" -- "Maximum-Likelihood Interpretation" -- "Extension to Nonlinear Models" -- "Regularization" -- "Cross-Validation" -- "Gradient Descent" -- "Geometry of Regularization" -- "Nonconvex Error Surfaces" -- "Stochastic, Batch, and Online Gradient Descent" -- "Alternative Update Rules Using Adaptive Learning Rates" -- "Momentum" -- "Summary" -- "References" -- "Chapter 3: Artificial Neural Networks" -- "The Perceptron" -- " Multilayer Neural Networks" -- "The Back-Propagation Algorithm" -- "Improving BP- Based Learning" -- " Activation Functions" -- " Weight Pruning" -- " Batch Normalization" -- "Summary" -- "References" -- "Chapter 4: Convolutional Neural Networks" -- "Convolution and Pooling Layer" -- "Convolutional Neural Networks" -- "Summary" -- "References" -- "Chapter 5: Modern and Novel Usages of CNNs" -- "Pretrained Networks" -- "Generality and Transferability" -- "Using Pretrained Networks for Model Compression" -- "Mentee Networks and FitNets