Learn Computer Vision Using OpenCV: With Deep Learning CNNs and RNNs by Sunila G

Description: Learn Computer Vision Using OpenCV by Sunila Gollapudi 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 vision followed by setting up OpenCV from scratch using Python. The next section discusses specialized image processing and segmentation and how images are stored and processed by a computer. This involves pattern recognition and image tagging using the OpenCV library. Next, youll work with object detection, video storage and interpretation, and human detection using OpenCV. Tracking and motion is also discussed in detail. The book also discusses creating complex deep learning models with CNN and RNN. The author finally concludes with recent applications and trends in computer vision.After reading this book, you will be able to understand and implement computer vision and its applications with OpenCV using Python. You will also be able to create deep learning models with CNN and RNN and understand how these cutting-edge deep learning architectures work.What You Will LearnUnderstand what computer vision is, and its overall application in intelligent automation systemsDiscover the deep learning techniques required to build computer vision applicationsBuild complex computer vision applications using the latest techniques in OpenCV, Python, and NumPyCreate practical applications and implementations such as face detection and recognition, handwriting recognition, object detection, and tracking and motion analysisWho This Book Is ForThose who have a basic understanding of machine learning and Python and are looking to learn computer vision and its applications. FORMAT Paperback LANGUAGE English CONDITION Brand New Back Cover 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 vision followed by setting up OpenCV from scratch using Python. The next section discusses specialized image processing and segmentation and how images are stored and processed by a computer. This involves pattern recognition and image tagging using the OpenCV library. Next, youll work with object detection, video storage and interpretation, and human detection using OpenCV. Tracking and motion is also discussed in detail. The book also discusses creating complex deep learning models with CNN and RNN. The author finally concludes with recent applications and trends in computer vision. After reading this book, you will be able to understand and implement computer vision and its applications with OpenCV using Python. You will also be able to create deep learning models with CNN and RNN and understand how these cutting-edge deep learning architectures work. You will: Understand what computer vision is, and its overall application in intelligent automation systems Discover the deep learning techniques required to build computer vision applications Build complex computer vision applications using the latest techniques in OpenCV, Python, and NumPy Create practical applications and implementations such as face detection and recognition, handwriting recognition, object detection, and tracking and motion analysis Author Biography Sunila Gollapudi has over 17 years of experience in developing, designing and architecting data-driven solutions with a focus on the banking and financial services sector. She is currently working at Broadridge, India as vice president. Shes played various roles as chief architect, big data and AI evangelist, and mentor.She has been a speaker at various conferences and meetups on Java and big data technologies. Her current big data and data science expertise includes Hadoop, Greenplum, MarkLogic, GemFire, ElasticSearch, Apache Spark, Splunk, R, Julia, Python (scikit-learn), Weka, MADlib, Apache Mahout, and advanced analytics techniques such as deep learning, computer vision, reinforcement, and ensemble learning. Table of Contents Chapter 1: Artificial Intelligence and Computer Vision.- Chapter 2: OpenCV with Python.- Chapter 3: Deep learning for Computer Vision.- Chapter 4: Image Manipulation and Segmentation.- Chapter 5 : Object Detection and Recognition.- Chapter 6: Motion Analysis and Tracking. Feature Helps readers get a jump start to computer vision implementations Offers use-case driven implementation for computer vision with focused learning on OpenCV and Python libraries Helps create deep learning models with CNN and RNN, and explains how these cutting-edge deep learning architectures work Details ISBN1484242602 Author Sunila Gollapudi Publisher APress Year 2019 Edition 1st ISBN-10 1484242602 ISBN-13 9781484242605 Format Paperback Imprint APress Place of Publication Berkley Country of Publication United States Pages 151 DEWEY 006.37 Subtitle With Deep Learning CNNs and RNNs Publication Date 2019-04-27 Short Title Learn Computer Vision Using OpenCV Language English DOI 10.1007/978-1-4842-4261-2 AU Release Date 2019-04-27 NZ Release Date 2019-04-27 US Release Date 2019-04-27 UK Release Date 2019-04-27 Illustrations 61 Illustrations, color; 27 Illustrations, black and white; XX, 151 p. 88 illus., 61 illus. in color. Edition Description 1st ed. 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