Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf

所需积分/C币:9 2019-05-14 21:29:01 6.25MB PDF
收藏 收藏
举报

Build practical applications of computer vision using the OpenCV library with Python. This book discusses different facets of computer vision such as image and object detection, tracking and motion analysis and their applications with examples. The author starts with an introduction to computer vis
Learn Computer vision Using OpenCV: With Deep Learning CNNs and RNNs Sunila gollapudi Hyderabad, Telangana, India ISBN-13(pbk):978-1-4842-4260-5 ISBN-13( electronic):978-1-4842-4261-2 htos:// doi. org/10.1007/978-1-4842-4261-2 Copyright o 2019 by Sunila Gollapudi This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein Managing Director, Apress Media LLC: Welmoed Spahr Acquisitions Editor: Celestin Suresh John Development Editor: Matthew Moodie Coordinating editor: Shrikant Vishwakarma Cover designed by eStudio Calamar CoverimagedesignedbyFreepik(www.freepik.com) Distributed to the book trade worldwide by Springer Science+ Business Media New York, 233 Spring Street, 6th Floor, New York, NY 10013. Phone 1-800-SPRINGER, fax(201)348-4505 e-mailorders-ny@springer-sbm.com,orvisitwww.springeronline.com.ApressMedia,Llcis California LLC and the sole member (owner)is Springer Science+ Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a Delaware corporation Forinformationontranslationspleasee-mailrights@apress.comorvisitwww.apress.com/ rights-permissions Apress titles may be purchased in bulk for academic, corporate, or promotional use eBook versions and licenses are also available for most titles for more information reference our print andebooKBulkSaleswebpageatwww.apress.com/bulk-sales Any source code or other supplementary material referenced by the author in this book is available toreadersonGithubviathebooksproductpagelocatedatwww.apress.com/978-1-4842-4260-5 Formoredetailedinformationpleasevisitwww.apress.com/source-code Printed on acid-free paper To my angel, my BFe my raison detre-my daughter Sai Srividya nikita-for being proud of me always! Table of contents About the author…uix About the technical reviewer Acknowledgments Foreword mXV Introduction… XVI Chapter 1: Artificial Intelligence and computer Vision mmmmmmmmmmmmmn. Introduction to Artificial Intelligence . Natural Language Processing Robotics Machine Learning Expert Systems.....,,.,.,,.,…,…,………13 Speech and Voice Recognition 13 Intelligent Process Automation 14 Introduction to Computer vision. Scope.......,.,,,,, 15 Challenges of Computer Vision Real-World Applications of Computer vision . Images and Their Features 24 Core Building Blocks(Input- Process-Output) Conclusion…28 TABLE OF CONTENTS Chapter 2: Open CV with Python ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 31 About OpenCV… Setting Up OpenCv with Python 32 Windows installation 32 macos Installation 36 Using Modules 38 Working with Images and videos. Using numPy. Videos Conclusion 49 Chapter 3: Deep Learning for computer Vision ■■ 51 Deep Learning: An Overview 152 Deep Learning Applications in Computer Vision 53 Classification mmmmmmm. 53 Detection and localization 54 (Semantic) Segmentation 55 Similarity Learning… 55 Image Captioning…...,,………………55 Generative mode|s…56 Video analysis.ammannia. Neural networks at their core …57 Artificial Neural Networks mmmm 58 Artificial Neurons or perceptrons …58 Training Neural Networksmememnmenmn. TABLE OF CONTENTS Convolutional neural networks 63 Convolution Layer . Pooling layer. Fully connected Layer… 65 ecurrent neural networks……………………,66 Backpropagation Through Time 68 Conclusion…69 Chapter 4: Image Manipulation and Segmentation mmmmmamaaat Image Manipulations 172 Accessing and Manipulating Pixels 73 Drawing Geometric Shapes or Writing Text on a Color Image .mmmmmm 75 Filtering Images …79 Transforming Images.mmmmmmmnmmammmanmmnmamemnmemaemmamnmmnmnn 82 Image Segmentation 90 Line detection, mmmm. 92 Circle Detection 93 Conclusion mm 96 Chapter 5: Object Detection and Recognition ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 97 Basics of object Detection 97 Object Detection Vs Object Recognition mma... et. 98 Template Matching…… Challenges with Template Matching.…,…,………,102 Understanding image" Features”……102 Feature Matching.… 105 mage Corners As Features….......,.,,…,105 Harris Corner algorithm n,106 Feature Tracking and matching Flow.…… 108 TABLE OF CONTENTS Scale variant Feature Transform ,109 Speeded-Up Robust Features 112 Features from Accelerated Segment Test. Binary Robust Independent Elementary Features 114 Oriented fast and rotated brief116 Conclusion…117 Chapter 6: Motion Analysis and object Tracking mmmmmmmm 119 Introduction to object Tracking….,,……120 Challenges of object Tracking. Object Detection Techniques for tracking 121 Frame Differentiation mmm. 122 Background Subtraction 123 Optical Flow 125 Object Classification ammmmnnmmemamaannnnmnnmamannn 131 Shaped-Based Classification.mmaanaaaaaaaanaaaaaan., 132 M0t0n- Based classificati0n,……132 Color-Based Texture-Based Object Tracking Methods Point Tracking Method.mnaeaannannanannnnnanannnn 134 Kernel-Based Tracking Methods. Silhouette-Based Tracking....,,,,,,,…,………144 Conclusion…145 Index ■■■■■■■■ 147 About the author Sunila gollapudi is an executive vice president at Broadridge Financial Solutions IndiaPvt)Ltd Sunila is a passionate and pragmatic technology leader with more than 17 years of experience in architecting, designing, and developing client-centric, enterprise-scale, and data-driven solutions She oversees every stage of the technology implementation and is a thought leader and technology visionary with a proven ability to build the technology road map Primarily focused on the banking and financial services domain over the past ten years, she is a data connoisseur and an architect, adept at designing an overall data strategy to maximize the value of data through analytics. She is also an author and a mentor with an entrepreneur mind-set who believes in continuous learning as a key to organizational growth Her specialties include building overall intelligent automation strategies by synthesizing the business and domain drivers and emerging technology trends in Big data engineering and analytics; leading cloud migration and devOps strategies for CI/CD and steering application (legacy) modernization, reuse, and technology standardization initiatives About the technical reviewer Lentin Joseph is an author and robotics entrepreneur from India. He runs a robotics Creativity software company called Qbotics Labs in India 鲁 He has more than eight years of experience in the robotics domain, primarily in ROS, Opencv, and Pcl He has authored several books on ros namely, Learning Robotics Using Python, E Mastering ROS for Robotics Programming ROS Robotics Projects, ROS Programming, and Robot Operating System for Absolute beginners He is also the technical reviewer of six robotics books He completed his master's in robotics and automation in India and also conducted research work at the Robotics Institute, Carnegie Mellon University, in the United States

...展开详情
试读 127P Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf
立即下载 低至0.43元/次 身份认证VIP会员低至7折
    抢沙发
    一个资源只可评论一次,评论内容不能少于5个字
    img
    jetlan2014

    关注 私信 TA的资源

    上传资源赚积分,得勋章
    最新推荐
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf 9积分/C币 立即下载
    1/127
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第1页
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第2页
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第3页
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第4页
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第5页
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第6页
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第7页
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第8页
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第9页
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第10页
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第11页
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第12页
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第13页
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第14页
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第15页
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第16页
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第17页
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第18页
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第19页
    Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs.pdf第20页

    试读已结束,剩余107页未读...

    9积分/C币 立即下载 >