A Guide to Convolutional Neural Networks for Computer Vision. Salman Khan

      

       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

      • 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

      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 paperback

      ISBN: 9781681730226 ebook

      ISBN: 9781681732787 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

       A Guide to Convolutional Neural Networks for Computer Vision

      Salman Khan

      Data61-CSIRO and Australian National University

      Hossein Rahmani

      The University of Western Australia, Crawley, WA

      Syed Afaq Ali Shah

      The University of Western Australia, Crawley, WA

      Mohammed Bennamoun

      The University of Western Australia, Crawley, WA

       SYNTHESIS LECTURES ON COMPUTER VISION #15

       ABSTRACT

      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