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A Guide to Convolutional Neural Networks for Computer Vision 无水印原版pdf.pdf

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    Cover
    Copyright
    Contents
    Preface
    1 Introduction
    What is Computer Vision?
    Applications
    Image Processing vs. Computer Vision
    What is Machine Learning?
    Why Deep Learning?
    Book Overview
    2 Features and Classifiers
    Importance of Features and Classifiers
    Features
    Classifiers
    Traditional Feature Descriptors
    Histogram of Oriented Gradients (HOG)
    Scale-invariant Feature Transform (SIFT)
    Speeded-up Robust Features (SURF)
    Limitations of Traditional Hand-engineered Features
    Machine Learning Classifiers
    Support Vector Machine (SVM)
    Random Decision Forest
    Conclusion
    3 Neural Networks Basics
    Introduction
    Multi-layer Perceptron
    Architecture Basics
    Parameter Learning
    Recurrent Neural Networks
    Architecture Basics
    Parameter Learning
    Link with Biological Vision
    Biological Neuron
    Computational Model of a Neuron
    Artificial vs. Biological Neuron
    4 Convolutional Neural Network
    Introduction
    Network Layers
    Pre-processing
    Convolutional Layers
    Pooling Layers
    Nonlinearity
    Fully Connected Layers
    Transposed Convolution Layer
    Region of Interest Pooling
    Spatial Pyramid Pooling Layer
    Vector of Locally Aggregated Descriptors Layer
    Spatial Transformer Layer
    CNN Loss Functions
    Cross-entropy Loss
    SVM Hinge Loss
    Squared Hinge Loss
    Euclidean Loss
    The 1 Error
    Contrastive Loss
    Expectation Loss
    Structural Similarity Measure
    5 CNN Learning
    Weight Initialization
    Gaussian Random Initialization
    Uniform Random Initialization
    Orthogonal Random Initialization
    Unsupervised Pre-training
    Xavier Initialization
    ReLU Aware Scaled Initialization
    Layer-sequential Unit Variance
    Supervised Pre-training
    Regularization of CNN
    Data Augmentation
    Dropout
    Drop-connect
    Batch Normalization
    Ensemble Model Averaging
    The 2 Regularization
    The 1 Regularization
    Elastic Net Regularization
    Max-norm Constraints
    Early Stopping
    Gradient-based CNN Learning
    Batch Gradient Descent
    Stochastic Gradient Descent
    Mini-batch Gradient Descent
    Neural Network Optimizers
    Momentum
    Nesterov Momentum
    Adaptive Gradient
    Adaptive Delta
    RMSprop
    Adaptive Moment Estimation
    Gradient Computation in CNNs
    Analytical Differentiation
    Numerical Differentiation
    Symbolic Differentiation
    Automatic Differentiation
    Understanding CNN through Visualization
    Visualizing Learned Weights
    Visualizing Activations
    Visualizations based on Gradients
    6 Examples of CNN Architectures
    LeNet
    AlexNet
    Network in Network
    VGGnet
    GoogleNet
    ResNet
    ResNeXt
    FractalNet
    DenseNet
    7 Applications of CNNs in Computer Vision
    Image Classification
    PointNet
    Object Detection and Localization
    Region-based CNN
    Fast R-CNN
    Regional Proposal Network (RPN)
    Semantic Segmentation
    Fully Convolutional Network (FCN)
    Deep Deconvolution Network (DDN)
    DeepLab
    Scene Understanding
    DeepContext
    Learning Rich Features from RGB-D Images
    PointNet for Scene Understanding
    Image Generation
    Generative Adversarial Networks (GANs)
    Deep Convolutional Generative Adversarial Networks (DCGANs)
    Super Resolution Generative Adversarial Network (SRGAN)
    Video-based Action Recognition
    Action Recognition From Still Video Frames
    Two-stream CNNs
    Long-term Recurrent Convolutional Network (LRCN)
    8 Deep Learning Tools and Libraries
    Caffe
    TensorFlow
    MatConvNet
    Torch7
    Theano
    Keras
    Lasagne
    Marvin
    Chainer
    PyTorch
    Conclusion
    Bibliography
    Authors' Biographies
    Blank Page
    Series ISSN: 2153-1056 Sven Dickinson, University of Toronto Series Editors: Gérard Medioni, University of Southern California A Guide to Convolutional Neural Networks for Computer Vision Salman Khan, Data61-CSIRO and Australian National University Hossein Rahmani, The University of Western Australia Syed Afaq Ali Shah, The University of Western Australia Mohammed Bennamoun, The University of Western Australia Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models. About SYNTHESIS This volume is a printed version of a work that appears in the Synthesis Digital Library of Engineering and Computer Science. Synthesis books provide concise, original presentations of important research and development topics, published quickly, in digital and print formats. store.morganclaypool.com K H A N • E T A L A G U I D E T O C O N V O L U T I O N A L N E U R A L N E T W O R K S F O R C O M P U T E R V I S I O N M O R G A N & C L A Y P O O L A Guide to Convolutional Neural Networks for Computer Vision Salman Khan Hossein Rahmani Syed Afaq Ali Shah Mohammed Bennamoun
    A Guide to Convolutional Neural Networks for Computer Vision
    Synthesis Lectures on Computer Vision Editors Gérard Medioni, University of Southern California Sven Dickinson, University of Toronto Synthesis Lectures on Computer Vision is edited by Gérard Medioni of the University of Southern California and Sven Dickinson of the University of Toronto. The series publishes 50–150 page publications on topics pertaining to computer vision and pattern recognition. The scope will largely follow the purview of premier computer science conferences, such as ICCV, CVPR, and ECCV. Potential topics include, but not are limited to: • Applications and Case Studies for Computer Vision • Color, Illumination, and Texture • Computational Photography and Video • Early and Biologically-inspired Vision • Face and Gesture Analysis • Illumination and Reflectance Modeling • Image-Based Modeling • Image and Video Retrieval • Medical Image Analysis • Motion and Tracking • Object Detection, Recognition, and Categorization • Segmentation and Grouping • Sensors • Shape-from-X • Stereo and Structure from Motion • Shape Representation and Matching
    iv • Statistical Methods and Learning • Performance Evaluation • Video Analysis and Event Recognition A Guide to Convolutional Neural Networks for Computer Vision Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, and Mohammed Bennamoun 2018 Covariances in Computer Vision and Machine Learning Hà Quang Minh and Vittorio Murino 2017 Elastic Shape Analysis of Three-Dimensional Objects Ian H. Jermyn, Sebastian Kurtek, Hamid Laga, and Anuj Srivastava 2017 The Maximum Consensus Problem: Recent Algorithmic Advances Tat-Jun Chin and David Suter 2017 Extreme Value Theory-Based Methods for Visual Recognition Walter J. Scheirer 2017 Data Association for Multi-Object Visual Tracking Margrit Betke and Zheng Wu 2016 Ellipse Fitting for Computer Vision: Implementation and Applications Kenichi Kanatani, Yasuyuki Sugaya, and Yasushi Kanazawa 2016 Computational Methods for Integrating Vision and Language Kobus Barnard 2016 Background Subtraction: Theory and Practice Ahmed Elgammal 2014 Vision-Based Interaction Matthew Turk and Gang Hua 2013
    v Camera Networks: The Acquisition and Analysis of Videos over Wide Areas Amit K. Roy-Chowdhury and Bi Song 2012 Deformable Surface 3D Reconstruction from Monocular Images Mathieu Salzmann and Pascal Fua 2010 Boosting-Based Face Detection and Adaptation Cha Zhang and Zhengyou Zhang 2010 Image-Based Modeling of Plants and Trees Sing Bing Kang and Long Quan 2009
    Copyright © 2018 by Morgan & Claypool All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher. A Guide to Convolutional Neural Networks for Computer Vision Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, and Mohammed Bennamoun www.morganclaypool.com ISBN: 9781681730219 ISBN: 9781681730226 ISBN: 9781681732787 paperback ebook hardcover DOI 10.2200/S00822ED1V01Y201712COV015 A Publication in the Morgan & Claypool Publishers series SYNTHESIS LECTURES ON COMPUTER VISION Lecture #15 Series Editors: Gérard Medioni, University of Southern California Sven Dickinson, University of Toronto Series ISSN Print 2153-1056 Electronic 2153-1064
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