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Neural Network Computer Vision with OpenCV 5

Neural Network Computer Vision with OpenCV 5
Author: Gopi Krishna Nuti
Publisher: BPB Publications
Total Pages: 351
Release: 2023-12-30
Genre: Computers
ISBN: 9355516967
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Unlocking computer vision with Python and OpenCV KEY FEATURES ● Practical solutions to image processing challenges. ● Detect and classify objects in images. ● Recognize faces and text from images using character detection and recognition models. DESCRIPTION Neural Network Computer Vision with OpenCV equips you with professional skills and knowledge to build intelligent vision systems using OpenCV. It creates a sequential pathway for understanding morphological operations, edge and corner detection, object localization, image classification, segmentation, and advanced applications like face detection and recognition, and optical character recognition. This book offers a practical roadmap to explore the nuances of image processing with detailed discussions on each topic, supported by hands-on Python code examples. The readers will learn the basics of neural networks, deep learning and CNNs by using deep learning frameworks like Keras, Tensorflow, PyTorch, Caffe etc. They will be able to utilize OpenCV DNN module to classify images by using models like Inception V3, Resnet 101, Mobilenet V2. Moreover, the book will help to successfully Implement object detection using YOLOv3, SSD and R-CNN models. The character detection and recognition models are also covered in depth with code examples. You will gain a deeper understanding of how these techniques impact real-world scenarios and learn to harness the potential of Python and OpenCV to solve complex problems. Whether you are building intelligent systems, automating processes, or working on image-related projects, this book equips you with the skills to revolutionize your approach to visual data. WHAT YOU WILL LEARN ● Acquire expertise in image manipulation techniques. ● Apply knowledge to practical scenarios in computer vision. ● Implement robust systems for face detection and recognition. ● Enhance projects with accurate object localization capabilities. ● Extract text information from images effectively. WHO THIS BOOK IS FOR This book is designed for those with basic Python skills, from beginners to intermediate-level readers. Whether you are building intelligent robots that perceive their surroundings or crafting advanced vision systems for object detection and image analysis, this book will equip you with the tools and skills to push the boundaries of AI perception. TABLE OF CONTENTS 1. Introduction to Computer Vision 2. Basics of Imaging 3. Challenges in Computer Vision 4. Classical Solutions 5. Deep Learning and CNNs 6. OpenCV DNN Module 7. Modern Solutions for Image Classification 8. Modern Solutions for Object Detection 9. Faces and Text 10. Running the Code 11. End-to-end Demo

Building Computer Vision Applications Using Artificial Neural Networks

Building Computer Vision Applications Using Artificial Neural Networks
Author: Shamshad Ansari
Publisher: Apress
Total Pages: 451
Release: 2020-07-17
Genre: Computers
ISBN: 9781484258866
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Apply computer vision and machine learning concepts in developing business and industrial applications ​using a practical, step-by-step approach. The book comprises four main sections starting with setting up your programming environment and configuring your computer with all the prerequisites to run the code examples. Section 1 covers the basics of image and video processing with code examples of how to manipulate and extract useful information from the images. You will mainly use OpenCV with Python to work with examples in this section. Section 2 describes machine learning and neural network concepts as applied to computer vision. You will learn different algorithms of the neural network, such as convolutional neural network (CNN), region-based convolutional neural network (R-CNN), and YOLO. In this section, you will also learn how to train, tune, and manage neural networks for computer vision. Section 3 provides step-by-step examples of developing business and industrial applications, such as facial recognition in video surveillance and surface defect detection in manufacturing. The final section is about training neural networks involving a large number of images on cloud infrastructure, such as Amazon AWS, Google Cloud Platform, and Microsoft Azure. It walks you through the process of training distributed neural networks for computer vision on GPU-based cloud infrastructure. By the time you finish reading Building Computer Vision Applications Using Artificial Neural Networks and working through the code examples, you will have developed some real-world use cases of computer vision with deep learning. What You Will Learn · Employ image processing, manipulation, and feature extraction techniques · Work with various deep learning algorithms for computer vision · Train, manage, and tune hyperparameters of CNNs and object detection models, such as R-CNN, SSD, and YOLO · Build neural network models using Keras and TensorFlow · Discover best practices when implementing computer vision applications in business and industry · Train distributed models on GPU-based cloud infrastructure Who This Book Is For Data scientists, analysts, and machine learning and software engineering professionals with Python programming knowledge.

Learn Computer Vision Using OpenCV

Learn Computer Vision Using OpenCV
Author: Sunila Gollapudi
Publisher: Apress
Total Pages: 163
Release: 2019-04-26
Genre: Computers
ISBN: 1484242610
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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, you’ll 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 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 Who This Book Is ForThose who have a basic understanding of machine learning and Python and are looking to learn computer vision and its applications.

Learning OpenCV 4 Computer Vision with Python 3

Learning OpenCV 4 Computer Vision with Python 3
Author: Joseph Howse
Publisher: Packt Publishing Ltd
Total Pages: 364
Release: 2020-02-20
Genre: Computers
ISBN: 1789530644
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Updated for OpenCV 4 and Python 3, this book covers the latest on depth cameras, 3D tracking, augmented reality, and deep neural networks, helping you solve real-world computer vision problems with practical code Key Features Build powerful computer vision applications in concise code with OpenCV 4 and Python 3 Learn the fundamental concepts of image processing, object classification, and 2D and 3D tracking Train, use, and understand machine learning models such as Support Vector Machines (SVMs) and neural networks Book Description Computer vision is a rapidly evolving science, encompassing diverse applications and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You'll be able to put theory into practice by building apps with OpenCV 4 and Python 3. You'll start by understanding OpenCV 4 and how to set it up with Python 3 on various platforms. Next, you'll learn how to perform basic operations such as reading, writing, manipulating, and displaying still images, videos, and camera feeds. From taking you through image processing, video analysis, and depth estimation and segmentation, to helping you gain practice by building a GUI app, this book ensures you'll have opportunities for hands-on activities. Next, you'll tackle two popular challenges: face detection and face recognition. You'll also learn about object classification and machine learning concepts, which will enable you to create and use object detectors and classifiers, and even track objects in movies or video camera feed. Later, you'll develop your skills in 3D tracking and augmented reality. Finally, you'll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age. By the end of this book, you'll have the skills you need to execute real-world computer vision projects. What you will learn Install and familiarize yourself with OpenCV 4's Python 3 bindings Understand image processing and video analysis basics Use a depth camera to distinguish foreground and background regions Detect and identify objects, and track their motion in videos Train and use your own models to match images and classify objects Detect and recognize faces, and classify their gender and age Build an augmented reality application to track an image in 3D Work with machine learning models, including SVMs, artificial neural networks (ANNs), and deep neural networks (DNNs) Who this book is for If you are interested in learning computer vision, machine learning, and OpenCV in the context of practical real-world applications, then this book is for you. This OpenCV book will also be useful for anyone getting started with computer vision as well as experts who want to stay up-to-date with OpenCV 4 and Python 3. Although no prior knowledge of image processing, computer vision or machine learning is required, familiarity with basic Python programming is a must.

Learn Computer Vision Using OpenCV

Learn Computer Vision Using OpenCV
Author: Sunila Gollapudi
Publisher:
Total Pages:
Release: 2019
Genre: Computer vision
ISBN: 9781484242629
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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, you'll 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 Learn 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 Who This Book Is For Those who have a basic understanding of machine learning and Python and are looking to learn computer vision and its applications.

Learn OpenCV with Python by Examples

Learn OpenCV with Python by Examples
Author: James Chen
Publisher:
Total Pages: 0
Release: 2023-05
Genre: Computers
ISBN: 9781738908448
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This book is a comprehensive guide to learning the basics of computer vision and machine learning using the powerful OpenCV library and the Python programming language. The book offers a practical, hands-on approach to learn the concepts and techniques of computer vision through practical example. All codes in this book are available at Github. Through a series of examples, the book covers a wide range of topics including image and video processing, feature detection, object detection and recognition, machine learning and deep neural networks. Each chapter includes detailed explanations of the concepts and techniques involved, as well as practical examples and code snippets that demonstrate how to implement them in Python. Throughout the book, readers will work through hands-on examples and projects, learning how to build image processing applications from scratch. Whether you are a beginner or an experienced programmer, this book provides a valuable resource for learning computer vision with OpenCV and Python. The clear and concise writing style makes it easy for readers to follow along, and the numerous examples ensure that readers can practice and apply what they have learned. By the end of the book, readers will have a solid understanding of the fundamentals of computer vision and be able to build their own computer vision applications with confidence. This book is an excellent resource for anyone looking to learn computer vision and machine learning using the OpenCV library and Python programming language. Table of Contents 1. Introduction 5 2. Installation 13 2.1 Install on Windows 14 2.2 Install Python on Ubuntu 16 2.3 Configure PyCharm and Install OpenCV 18 3. OpenCV Basics 25 3.1 Load and Display Images 26 3.2 Load and Display Videos 30 3.3 Display Webcam 32 3.4 Image Fundamentals 35 3.5 Draw Shapes 42 3.6 Draw Texts 48 3.7 Draw an OpenCV-like Icon 50 4. User Interaction 52 4.1 Mouse Operations 53 4.2 Draw Circles with Mouse 56 4.3 Draw Polygon with Mouse 60 4.4 Crop an Image with Mouse 62 4.5 Input Values with Trackbars 64 5. Image Processing 70 5.1 Conversion of Color Spaces 72 5.2 Resize, Crop and Rotate an Image 77 5.3 Adjust Contrast and Brightness of an Image 83 5.4 Adjust Hue, Saturation and Value 87 5.5 Blend Image 91 5.6 Bitwise Operation 94 5.7 Warp Image 101 5.8 Blur Image 107 5.9 Histogram 114 6. Object Detection 120 6.1 Canny Edge Detection 122 6.2 Dilation and Erosion 125 6.3 Shape Detection 129 6.4 Color Detection 139 6.5 Text Recognition with Tesseract 150 6.6 Human Detection 161 6.7 Face and Eye Detection 165 6.8 Remove Background 170 6.9 Blur Background 189 7. Machine Learning 196 7.1 K-Means Clustering 200 7.2 K-Nearest Neighbors 216 7.3 Support Vector Machine 237 7.4 Artificial Neural Network (ANN) 254 7.5 Convolutional Neural Network (CNN) 276 Index 305 References 308 About the Author 310

Elements of Deep Learning for Computer Vision

Elements of Deep Learning for Computer Vision
Author: Bharat Sikka
Publisher: BPB Publications
Total Pages: 224
Release: 2021-06-24
Genre: Computers
ISBN: 9390684684
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Conceptualizing deep learning in computer vision applications using PyTorch and Python libraries. KEY FEATURES ● Covers a variety of computer vision projects, including face recognition and object recognition such as Yolo, Faster R-CNN. ● Includes graphical representations and illustrations of neural networks and teaches how to program them. ● Includes deep learning techniques and architectures introduced by Microsoft, Google, and the University of Oxford. DESCRIPTION Elements of Deep Learning for Computer Vision gives a thorough understanding of deep learning and provides highly accurate computer vision solutions while using libraries like PyTorch. This book introduces you to Deep Learning and explains all the concepts required to understand the basic working, development, and tuning of a neural network using Pytorch. The book then addresses the field of computer vision using two libraries, including the Python wrapper/version of OpenCV and PIL. After establishing and understanding both the primary concepts, the book addresses them together by explaining Convolutional Neural Networks(CNNs). CNNs are further elaborated using top industry standards and research to explain how they provide complicated Object Detection in images and videos, while also explaining their evaluation. Towards the end, the book explains how to develop a fully functional object detection model, including its deployment over APIs. By the end of this book, you are well-equipped with the role of deep learning in the field of computer vision along with a guided process to design deep learning solutions. WHAT YOU WILL LEARN ● Get to know the mechanism of deep learning and how neural networks operate. ● Learn to develop a highly accurate neural network model. ● Access to rich Python libraries to address computer vision challenges. ● Build deep learning models using PyTorch and learn how to deploy using the API. ● Learn to develop Object Detection and Face Recognition models along with their deployment. WHO THIS BOOK IS FOR This book is for the readers who aspire to gain a strong fundamental understanding of how to infuse deep learning into computer vision and image processing applications. Readers are expected to have intermediate Python skills. No previous knowledge of PyTorch and Computer Vision is required. TABLE OF CONTENTS 1. An Introduction to Deep Learning 2. Supervised Learning 3. Gradient Descent 4. OpenCV with Python 5. Python Imaging Library and Pillow 6. Introduction to Convolutional Neural Networks 7. GoogLeNet, VGGNet, and ResNet 8. Understanding Object Detection 9. Popular Algorithms for Object Detection 10. Faster RCNN with PyTorch and YoloV4 with Darknet 11. Comparing Algorithms and API Deployment with Flask 12. Applications in Real World

Learning OpenCV 3 Computer Vision with Python

Learning OpenCV 3 Computer Vision with Python
Author: Joe Minichino
Publisher: Packt Publishing Ltd
Total Pages: 266
Release: 2015-09-29
Genre: Computers
ISBN: 1785289772
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Unleash the power of computer vision with Python using OpenCV About This Book Create impressive applications with OpenCV and Python Familiarize yourself with advanced machine learning concepts Harness the power of computer vision with this easy-to-follow guide Who This Book Is For Intended for novices to the world of OpenCV and computer vision, as well as OpenCV veterans that want to learn about what's new in OpenCV 3, this book is useful as a reference for experts and a training manual for beginners, or for anybody who wants to familiarize themselves with the concepts of object classification and detection in simple and understandable terms. Basic knowledge about Python and programming concepts is required, although the book has an easy learning curve both from a theoretical and coding point of view. What You Will Learn Install and familiarize yourself with OpenCV 3's Python API Grasp the basics of image processing and video analysis Identify and recognize objects in images and videos Detect and recognize faces using OpenCV Train and use your own object classifiers Learn about machine learning concepts in a computer vision context Work with artificial neural networks using OpenCV Develop your own computer vision real-life application In Detail OpenCV 3 is a state-of-the-art computer vision library that allows a great variety of image and video processing operations. Some of the more spectacular and futuristic features such as face recognition or object tracking are easily achievable with OpenCV 3. Learning the basic concepts behind computer vision algorithms, models, and OpenCV's API will enable the development of all sorts of real-world applications, including security and surveillance. Starting with basic image processing operations, the book will take you through to advanced computer vision concepts. Computer vision is a rapidly evolving science whose applications in the real world are exploding, so this book will appeal to computer vision novices as well as experts of the subject wanting to learn the brand new OpenCV 3.0.0. You will build a theoretical foundation of image processing and video analysis, and progress to the concepts of classification through machine learning, acquiring the technical know-how that will allow you to create and use object detectors and classifiers, and even track objects in movies or video camera feeds. Finally, the journey will end in the world of artificial neural networks, along with the development of a hand-written digits recognition application. Style and approach This book is a comprehensive guide to the brand new OpenCV 3 with Python to develop real-life computer vision applications.

Deep Learning for Computer Vision

Deep Learning for Computer Vision
Author: Rajalingappaa Shanmugamani
Publisher: Packt Publishing Ltd
Total Pages: 304
Release: 2018-01-23
Genre: Computers
ISBN: 1788293355
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Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks Key Features Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more Includes tips on optimizing and improving the performance of your models under various constraints Book Description Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation. What you will learn Set up an environment for deep learning with Python, TensorFlow, and Keras Define and train a model for image and video classification Use features from a pre-trained Convolutional Neural Network model for image retrieval Understand and implement object detection using the real-world Pedestrian Detection scenario Learn about various problems in image captioning and how to overcome them by training images and text together Implement similarity matching and train a model for face recognition Understand the concept of generative models and use them for image generation Deploy your deep learning models and optimize them for high performance Who this book is for This book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python—and some understanding of machine learning concepts—is required to get the best out of this book.