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Deep learning in computer vision: principles and applications

✍ Scribed by Mahmoud Hassaballah, Ali Ismail Awad

Publisher
CRC Press
Year
2020
Tongue
English
Leaves
105
Series
Digital imaging and computer vision
Edition
First edition.
Category
Library
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✦ Synopsis

Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.

✦ Table of Contents

Doc1
9781351003827_preview
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Foreword #8,0,-32767 Preface #10,0,-32767 Editors Bio #14,0,-32767 Contributors #16,0,-32767 Chapter 1 Accelerating the CNN Inference on FPGAs #18,0,-32767 1.1 Introduction #19,0,-32767 1.2 Background on CNNs and Their Computational Workload #20,0,-32767 1.2.1 General Overview #20,0,-32767 1.2.2 Inference versus Training #20,0,-32767 1.2.3 Inference, Layers, and CNN Models #20,0,-32767 1.2.4 Workloads and Computations #23,0,-32767 1.2.4.1 Computational Workload #23,0,-32767 1.2.4.2 Parallelism in CNNs #25,0,-32767 1.2.4.3 Memory Accesses #26,0,-32767 1.2.4.4 Hardware, Libraries, and Frameworks #27,0,-32767 1.3 FPGA-Based Deep Learning #28,0,-32767 1.4 Computational Transforms #29,0,-32767 1.4.1 The im2col Transformation #30,0,-32767 1.4.2 Winograd Transform #31,0,-32767 1.4.3 Fast Fourier Transform #33,0,-32767 1.5 Data-Path Optimizations #33,0,-32767 1.5.1 Systolic Arrays #33,0,-32767 1.5.2 Loop Optimization in Spatial Architectures #35,0,-32767 Loop Unrolling #36,0,-32767 Loop Tiling #37,0,-32767 1.5.3 Design Space Exploration #38,0,-32767 1.5.4 FPGA Implementations #39,0,-32767 1.6 Approximate Computing of CNN Models #40,0,-32767 1.6.1 Approximate Arithmetic for CNNs #40,0,-32767 1.6.1.1 Fixed-Point Arithmetic #40,0,-32767 1.6.1.2 Dynamic Fixed Point for CNNs #45,0,-32767 1.6.1.3 FPGA Implementations #46,0,-32767 1.6.1.4 Extreme Quantization and Binary Networks #46,0,-32767 1.6.2 Reduced Computations #47,0,-32767 1.6.2.1 Weight Pruning #48,0,-32767 1.6.2.2 Low Rank Approximation #48,0,-32767 1.6.2.3 FPGA Implementations #49,0,-32767 1.7 Conclusions #49,0,-32767 Bibliography #50,0,-32767 References

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