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Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging : Mathematical Imaging and Vision by Ke Chen, Carola-Bibiane Schönlieb, Xue-Cheng Tai, Laurent Younes, (eds.) is a nonfiction available to read on EtoBox.

What is Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging : Mathematical Imaging and Vision about?

Preface Contents About the Editors Contributors Part I Convex and Non-convex Large-Scale Optimization in Imaging 1 Convex Non-convex Variational Models Contents Introduction Convex or Non-convex: Main Idea and Related Works Sparsity-Inducing Separable Regularizers CNC Models with Sparsity-Inducing Separable Regularizers Sparsity-Inducing Non-separable Regularizers CNC Models with Sparsity-Inducing Non-separable Regularizers Construction of Matrix B A Simple CNC Example Path of Solution Components Forward-Backward Minimization Algorithms FB Strategy for Separable CNC Models FB Strategy for Non-separable CNC Models Efficient Solution of the Backward Steps by ADMM Numerical Examples Examples Using CNC Separable Models Examples Using CNC Non-separable Models Conclusion References 2 Subsampled First-Order Optimization Methods with Applications in Imaging Contents Introduction Convolutional Neural Networks Convolutional Layer Max Pooling Layer Stochastic Gradient and Variance Reduction Methods Gradient Methods with Adaptive Steplength Selection Based on Globalization Strategies Accuracy Requirements Stochastic Line Search Adaptive Regularization and Trust-Region Numerical Experiments The

Who reads Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging : Mathematical Imaging and Vision?

Author

Ke Chen, Carola-Bibiane Schönlieb, Xue-Cheng Tai, Laurent Younes, (eds.)

Publisher

Springer International Publishing Springer

Published

2023

Language

EN

ISBN

9783030986605

Category

nonfiction

Subjects

Mathematics, Computer Science, Applied Mathematics