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See below for a selection of the latest books from Computer vision category. Presented with a red border are the Computer vision books that have been lovingly read and reviewed by the experts at Lovereading. With expert reading recommendations made by people with a passion for books and some unique features Lovereading will help you find great Computer vision books and those from many more genres to read that will keep you inspired and entertained. And it's all free!
Biometric Recognition and Security: Theory, Methods and Applications describes recent methods of biometric recognition for the identification and verification of individuals based on their physiological traits (palm of the hand, fingerprints of the fingers of the hand, and ear) and behavioral characteristics (walking characteristics). The book is ideal for engineering students, professional masters or research doctoral students and others who are interested in the field of biometrics. It can also be used by industrialists wishing to develop biometric recognition systems or by teacher-researchers responsible for developing lectures on biometric recognition.
High-throughput microscopy enables researchers to acquire thousands of images automatically over a short time, making it possible to conduct large-scale, image-based experiments for biological or biomedical discovery. However, visual analysis of large-scale image data is a daunting task. The post-acquisition component of high-throughput microscopy experiments calls for effective and efficient computer vision techniques. Computer Vision for Microscopy Image Analysis provides a comprehensive and in-depth introduction to state-of-the-art computer vision techniques for microscopy image analysis, demonstrating how they can be effectively applied to biological and medical data. The reader of the book will learn: How computer vision analysis can automate and enhance human assessment of microscopy images for discovery The important steps in microscopy image analysis State-of-the-art methods for microscopy image analysis including machine learning and deep neural network approaches This reference on the state-of-the-art computer vision methods in microscopy image analysis is suitable for researchers and graduate students interested in analyzing microscopy images or for developing toolsets for general biomedical image analysis applications.
Spectral Geometry of Shapes presents unique shape analysis approaches based on shape spectrum in differential geometry. It provides insights on how to develop geometry-based methods for 3D shape analysis. The book is an ideal learning resource for graduate students and researchers in computer science, computer engineering and applied mathematics who have an interest in 3D shape analysis, shape motion analysis, image analysis, medical image analysis, computer vision and computer graphics. Due to the rapid advancement of 3D acquisition technologies there has been a big increase in 3D shape data that requires a variety of shape analysis methods, hence the need for this comprehensive resource.
Tone and Gamut Mapping for High Dynamic Range and Colour Gamut Imaging: Vision Models, Techniques and Applications explains tone and color gamut mapping in HDR and WCG imaging within a framework of vision science, presenting the underlying principles and latest practical methods. In addition, it highlights how the use of vision models is a key element of all state-of- the-art methods for these emerging technologies. This book provides university researchers and graduate students in computer science, computer engineering, vision science, and R&D engineers insights into the science and methods of tone and color gamut mapping in HDR and WCG.
Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where do I begin? This step-by-step guide teaches you how to build practical deep learning applications for the cloud and mobile using a hands-on approach. Relying on years of industry experience transforming deep-learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, CoreML, and TensorFlow Lite and go from zero to a production-quality system quickly. Develop deep learning applications for the desktop, cloud, smartphones, browser, and Raspberry Pi Learn by building examples such as Silicon Valley's Not Hotdog, image search engines, and your own mini-autonomous car Use transfer learning to train models in minutes Optimize your apps to run efficiently on different hardware Discover strategies to scale up from a single user to millions Sharpen practical skills for data collection, model interoperability, and model debugging using visualizations Uncover the potential for bias and explore the ethical underpinnings for AI-driven technology
Feature Extraction for Image Processing and Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in MATLAB and Python. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, The main strength of the proposed book is the link between theory and exemplar code of the algorithms. Essential background theory is carefully explained. This text gives students and researchers in image processing and computer vision a complete introduction to classic and state-of-the art methods in feature extraction together with practical guidance on their implementation.
A concise and accessible guide to techniques for detecting doctored and fake images in photographs and digital media. Stalin, Mao, Hitler, Mussolini, and other dictators routinely doctored photographs so that the images aligned with their messages. They erased people who were there, added people who were not, and manipulated backgrounds. They knew if they changed the visual record, they could change history. Once, altering images required hours in the darkroom; today, it can be done with a keyboard and mouse. Because photographs are so easily faked, fake photos are everywhere-supermarket tabloids, fashion magazines, political ads, and social media. How can we tell if an image is real or false? In this volume in the MIT Press Essential Knowledge series, Hany Farid offers a concise and accessible guide to techniques for detecting doctored and fake images in photographs and digital media. Farid, an expert in photo forensics, has spent two decades developing techniques for authenticating digital images. These techniques model the entire image-creation process in order to find the digital disruption introduced by manipulation of the image. Each section of the book describes a different technique for analyzing an image, beginning with those requiring minimal technical expertise and advancing to those at intermediate and higher levels. There are techniques for, among other things, reverse image searches, metadata analysis, finding image imperfections introduced by JPEG compression, image cloning, tracing pixel patterns, and detecting images that are computer generated. In each section, Farid describes the techniques, explains when they should be applied, and offers examples of image analysis.
Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention.
The typical computational approach to object understanding derives shape information from the 2D outline of the objects. For complex object structures, however, such a planar approach cannot determine object shape; the structural edges have to be encoded in terms of their full 3D spatial configuration. Computer Vision: From Surfaces to 3D Objects is the first book to take a full approach to the challenging issue of veridical 3D object representation. It introduces mathematical and conceptual advances that offer an unprecedented framework for analyzing the complex scene structure of the world. An Unprecedented Framework for Complex Object Representation Presenting the material from both computational and neural implementation perspectives, the book covers novel analytic techniques for all levels of the surface representation problem. The cutting-edge contributions in this work run the gamut from the basic issue of the ground plane for surface estimation through mid-level analyses of surface segmentation processes to complex Riemannian space methods for representing and evaluating surfaces. State-of-the-Art 3D Surface and Object Representation This well-illustrated book takes a fresh look at the issue of 3D object representation. It provides a comprehensive survey of current approaches to the computational reconstruction of surface structure in the visual scene.
Face detection and recognition are the nonintrusive biometrics of choice in many security applications. Examples of their use include border control, driver's license issuance, law enforcement investigations, and physical access control. Face Detection and Recognition: Theory and Practice elaborates on and explains the theory and practice of face detection and recognition systems currently in vogue. The book begins with an introduction to the state of the art, offering a general review of the available methods and an indication of future research using cognitive neurophysiology. The text then: Explores subspace methods for dimensionality reduction in face image processing, statistical methods applied to face detection, and intelligent face detection methods dominated by the use of artificial neural networks Covers face detection with colour and infrared face images, face detection in real time, face detection and recognition using set estimation theory, face recognition using evolutionary algorithms, and face recognition in frequency domain Discusses methods for the localization of face landmarks helpful in face recognition, methods of generating synthetic face images using set estimation theory, and databases of face images available for testing and training systems Features pictorial descriptions of every algorithm as well as downloadable source code (in MATLAB (R)/PYTHON) and hardware implementation strategies with code examples Demonstrates how frequency domain correlation techniques can be used supplying exhaustive test results Face Detection and Recognition: Theory and Practice provides students, researchers, and practitioners with a single source for cutting-edge information on the major approaches, algorithms, and technologies used in automated face detection and recognition.
The book familiarizes readers with fundamental concepts and issues related to computer vision and major approaches that address them. The focus of the book is on image acquisition and image formation models, radiometric models of image formation, image formation in the camera, image processing concepts, concept of feature extraction and feature selection for pattern classification/recognition, and advanced concepts like object classification, object tracking, image-based rendering, and image registration. Intended to be a companion to a typical teaching course on computer vision, the book takes a problem-solving approach.
The 2-volume set LNCS 11613 and 11614 constitutes the refereed proceedings of the 6th International Conference on Augmented Reality, Virtual Reality, and Computer Graphics, AVR 2019, held in Santa Maria al Bagno, Italy, in June 2019. The 32 full papers and 35 short papers presented were carefully reviewed and selected from numerous submissions. The papers discuss key issues, approaches, ideas, open problems, innovative applications and trends in virtual and augmented reality, 3D visualization and computer graphics in the areas of medicine, cultural heritage, arts, education, entertainment, military and industrial applications. They are organized in the following topical sections: virtual reality; medicine; augmented reality; cultural heritage; education; and industry.