OpenCV 3 Computer Vision Application Programming Cookbook is appropriate for novice C++ programmers who want to learn how to use the OpenCV library to build computer vision applications. It is also suitable for professional software developers wishing to be introduced to the concepts of computer vision programming. It can also be used as a companion book in a university-level computer vision courses. It constitutes an excellent reference for graduate students and researchers in image processing and computer vision.
Practical OpenCV is a hands-on project book that shows you how to get the best results from OpenCV, the open-source computer vision library. Computer vision is key to technologies like object recognition, shape detection, and depth estimation. OpenCV is an open-source library with over 2500 algorithms that you can use to do all of these, as well as track moving objects, extract 3D models, and overlay augmented reality. It’s used by major companies like Google (in its autonomous car), Intel, and Sony; and it is the backbone of the Robot Operating System’s computer vision capability. In short, if you’re working with computer vision at all, you need to know OpenCV.
This is the definitive advanced tutorial for OpenCV, designed for those with basic C++ skills. The computer vision projects are divided into easily assimilated chapters with an emphasis on practical involvement for an easier learning curve. Mastering OpenCV with Practical Computer Vision Projects is the perfect book for developers with just basic OpenCV skills who want to try practical computer vision projects, as well as the seasoned OpenCV experts who want to add more Computer Vision topics to their skill set or gain more experience with OpenCV’s new C++ interface before migrating from the C API to the C++ API.
This book is for programmers who want to expand their skills by building fun, smart, and useful systems with OpenCV. The projects are ideal in helping you to think creatively about the uses of computer vision, natural user interfaces, and ubiquitous computers (in your home, car, and hand). OpenCV is a grand collection of image processing functions and computer vision algorithms. It is open source, it supports many programming languages and platforms, and it is fast enough for many real-time applications. What a lot of gadgets we can build with such a handy library! Taking inspiration from the world of James Bond, this book adds a spark of adventure and computer vision to your daily routine. Protect your home and car with intelligent camera systems that analyze people, cats, and obstacles. Let your search engine praise or criticize the images that it finds. Hear a voice from your phone that responds to your body language. Attune yourself to another person’s rhythm by glancing at a display that magnifies a heartbeat or a breath. Learn OpenCV and see your world as never before.
OpenCV, arguably the most widely used computer vision library, includes hundreds of ready-to-use imaging and vision functions and is used in both academia and enterprises. This book provides an example-based tour of OpenCV’s main image processing algorithms. Starting with an exploration of library installation, wherein the library structure and basics of image and video reading/writing are covered, you will dive into image filtering and the color manipulation features of OpenCV with LUTs. You’ll then be introduced to techniques such as inpainting and denoising to enhance images as well as the process of HDR imaging. Finally, you’ll master GPU-based accelerations. By the end of this book, you will be able to create smart and powerful image processing applications with ease! All the topics are described with short, easy-to-follow examples.
Now you have access to second edition and early release of this classic in OpenCV and machine learning. Enjoy it and don’t forget to share it with your friends 🙂 Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to “see” and make decisions based on that data. The second edition is updated to cover new features and changes in OpenCV 2.0, especially the C++ interface. Computer vision is everywhere—in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book any developer or hobbyist needs to get started, with the help of hands-on exercises in each chapter.
This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as exercises.
Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. He brings unifying principles to the fore, and reviews the state of the subject. Ripley also includes many examples to illustrate real problems in pattern recognition and how to overcome them.
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book.
Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques. This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book has been updated to cover new methods and applications, and includes a wide range of techniques such as Bayesian methods, neural networks, support vector machines, feature selection and feature reduction techniques.Technical descriptions and motivations are provided, and the techniques are illustrated using real examples. Statistical Pattern Recognition, 3rd Edition: * Provides a self-contained introduction to statistical pattern recognition. * Includes new material presenting the analysis of complex networks. * Introduces readers to methods for Bayesian density estimation. * Presents descriptions of new applications in biometrics, security, finance and condition monitoring. * Provides descriptions and guidance for implementing techniques, which will be invaluable to software engineers and developers seeking to develop real applications * Describes mathematically the range of statistical pattern recognition techniques. * Presents a variety of exercises including more extensive computer projects. The in-depth technical descriptions make the book suitable for senior undergraduate and graduate students in statistics, computer science and engineering. Statistical Pattern Recognition is also an excellent reference source for technical professionals. Chapters have been arranged to facilitate implementation of the techniques by software engineers and developers in non-statistical engineering fields.