This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models

Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques. Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at an associated website. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

Computer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fifth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date text suitable for undergraduate and graduate students, researchers and R&D engineers working in this vibrant subject. See an interview with the author explaining his approach to teaching and learning computer vision - http://scitechconnect.elsevier.com/computer-vision/

Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy's expert instruction and illustration of real-world projects, you'll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision!\nPurchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Acquire, process, and analyze visual content to build full-fledged imaging applications using OpenCVAbout This Book\nCreate OpenCV programs with a rich user interface Develop real-world imaging applications using free tools and libraries Understand the intricate details of OpenCV and its implementation using easy-to-follow examples Who This Book Is ForThis book is intended for C++ developers who want to learn how to implement the main techniques of OpenCV and get started with it quickly. Working experience with computer vision / image processing is expected.What You Will Learn\nExplore advanced image processing techniques such as the retina algorithm, morphing, and color transfer Create programs using advanced segmentation tools such as the new connectedComponents and connectedComponentsWithStats functions Use flood filling along with the watershed transform to obtain better segmentations Explore the new powerful KAZE features Use advanced video-based background/foreground segmentation for class BackgroundSubtractor and ECC-based warping Leverage the available object detection frameworks and the new scene text detection functionality Get a grasp of advanced topics such as machine learning and GPU optimization In DetailOpenCV, arguably the most widely used computer vision library, includes hundreds of ready-to-use imaging and vision functions used in both academia and industry. It mainly focuses on real-time image processing. As cameras get cheaper and imaging features grow in demand, the range of applications using OpenCV increases significantly, both for desktop and mobile platforms. The book provides an example-based tour of OpenCV's main modules and algorithms, including the latest available in version 3.0. Starting with the setup and description of the library, this book teaches you how to add graphical user interface capabilities to OpenCV programs. Further, you will learn about the essential techniques such as image processing, image segmentation, object detection, and motion, which will help you process and analyze images better. You will also learn how to extract 2D features from images and how to take advantage of machine learning. By the end of this book, you will completely understand how to put those computer vision techniques into practice.

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 to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices 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.\n\nTrain, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite.\nDevelop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral.\nExplore fun projects, from Silicon Valley's Not Hotdog app to 40+ industry case studies.\nSimulate an autonomous car in a video game environment and build a miniature version with reinforcement learning.\nUse transfer learning to train models in minutes.\nDiscover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users.\n\nList of Chapters\nExploring the Landscape of Artificial Intelligence\nWhat's in the Picture: Image Classification with Keras\nCats Versus Dogs: Transfer Learning in 30 Lines with Keras\nBuilding a Reverse Image Search Engine: Understanding Embeddings\nFrom Novice to Master Predictor: Maximizing Convolutional Neural Network Accuracy\nMaximizing Speed and Performance of TensorFlow: A Handy Checklist\nPractical Tools, Tips, and Tricks\nCloud APIs for Computer Vision: Up and Running in 15 Minutes\nScalable Inference Serving on Cloud with TensorFlow Serving and KubeFlow\nAI in the Browser with TensorFlow.js and ml5.js\nReal-Time Object Classification on iOS with Core ML\nNot Hotdog on iOS with Core ML and Create ML\nShazam for Food: Developing Android Apps with TensorFlow Lite and ML Kit\nBuilding the Purrfect Cat Locator App with TensorFlow Object Detection API\nBecoming a Maker: Exploring Embedded AI at the Edge\nSimulating a Self-Driving Car Using End-to-End Deep Learning with Keras\nBuilding an Autonomous Car in Under an Hour: Reinforcement Learning with AWS DeepRacer\n\nGuest-contributed ContentThe book features chapters from the following industry experts:\nSunil Mallya (Amazon AWS DeepRacer)\nAditya Sharma and Mitchell Spryn (Microsoft Autonomous Driving Cookbook)\nSam Sterckval (Edgise)\nZaid Alyafeai (TensorFlow.js)\nThe book also features content contributed by several industry veterans including François Chollet (Keras, Google), Jeremy Howard (Fast.ai), Pete Warden (TensorFlow Mobile), Anima Anandkumar (NVIDIA), Chris Anderson (3D Robotics), Shanqing Cai (TensorFlow.js), Daniel Smilkov (TensorFlow.js), Cristobal Valenzuela (ml5.js), Daniel Shiffman (ml5.js), Hart Woolery (CV 2020), Dan Abdinoor (Fritz), Chitoku Yato (NVIDIA Jetson Nano), John Welsh (NVIDIA Jetson Nano), and Danny Atsmon (Cognata).

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 \nBuild 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\n 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 \nInstall 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)\n 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.Table of Contents \nUnderstanding OpenCV and Setting Up Environment Handling Files, Cameras, and GUIs Processing Images with OpenCV Depth Estimation and Segmentation Detecting and Recognizing Faces Retrieving Images and Searching Using Image Descriptors Building Custom Object Detectors Tracking Objects Camera Models and Augmented Reality Neural Networks with OpenCV - An Introduction Appendix 1: Bending Color Space with a Curves Filter\n

Computer Vision: A Modern Approach, 2e, is appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering. This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods

Using a progressive intuitive/mathematical approach, this introduction to computer vision provides necessary theory and examples for practitioners who work in fields where significant information must be extracted automatically from images-- including those interested in multimedia, art and design, geographic information systems, and image databases, in addition to the traditional areas of automation, image science, medical imaging, remote sensing and computer cartography. The book provides a basic set of fundamental concepts, (representations of image information, extraction of 3D scene information from 2D images, etc.) algorithms for analyzing images, and discusses some of the exciting evolving application areas of computer vision. The approach is language and software independent, and includes two significant commercial case studies. Imaging and Image Representation. Binary Image Analysis. Pattern Recognition Concepts. Filtering and Enhancing Images. Color and Shading. Texture. Content-Based Image Retrieval. Motion from 2D Image Sequences. Image Segmentation. Matching in 2D. Perceiving 3D from 2D Images. 3D Sensing and Object Pose Computation. 3D Models and Matching. Virtual Reality. Case Studies. For practitioners in any field where information must be extracted automatically from images.

This book revolutionizes how vision can be taught to undergraduate and graduate students in cognitive science, psychology, and optometry. It is the first comprehensive textbook on vision to reflect the integrated computational approach of modern research scientists. This new interdisciplinary approach, called "vision science," integrates psychological, computational, and neuroscientific perspectives.\nThe book covers all major topics related to vision, from early neural processing of image structure in the retina to high-level visual attention, memory, imagery, and awareness. The presentation throughout is theoretically sophisticated yet requires minimal knowledge of mathematics. There is also an extensive glossary, as well as appendices on psychophysical methods, connectionist modeling, and color technology. The book will serve not only as a comprehensive textbook on vision, but also as a valuable reference for researchers in cognitive science, psychology, neuroscience, computer science, optometry, and philosophy.

This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. - Covers cutting-edge techniques, including graph cuts, machine learning, and multiple view geometry. - A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition, and object tracking. - More than 70 algorithms are described in sufficient detail to implement. - More than 350 full-color illustrations amplify the text. - The treatment is self-contained, including all of the background mathematics. - Additional resources at www.computervisionmodels.com.

High Dynamic Range Imaging, Second Edition, is an essential resource for anyone working with images, whether it is for computer graphics, film, video, photography, or lighting design. It describes HDRI technology in its entirety and covers a wide-range of topics, from capture devices to tone reproduction and image-based lighting. The techniques described enable students to produce images that have a dynamic range much closer to that found in the real world, leading to an unparalleled visual experience. This revised edition includes new chapters on High Dynamic Range Video Encoding, High Dynamic Range Image Encoding, and High Dynamic Range Display Devices. All existing chapters have been updated to reflect the current state-of-the-art technology. As both an introduction to the field and an authoritative technical reference, this book is essential for anyone working with images, whether in computer graphics, film, video, photography, or lighting design.

Gain a firm understanding of image processing as you cover the major topics of the field using a balanced approach that progresses from simple explanations to more detailed descriptions within IMAGE PROCESSING AND ANALYSIS. This easy-to-follow, accessible book emphasizes a basic, fundamental understanding of the classic algorithms in the field while also highlighting recent research results. You can grasp the subtle tradeoffs among different approaches as well as understand them in context with the latest developments in the field. Numerous full-color illustrations and detailed pseudocode bridge the gap between mathematical equations underlying the important concepts and real-world application of those concepts. This understanding makes it easier for you to program your own implementations of these algorithms. In addition, consistent notation throughout this edition makes it easier to follow the various concepts.

Get to grips with deep learning techniques for building image processing applications using PyTorch with the help of code notebooks and test questions Key Features Implement solutions to 50 real-world computer vision applications using PyTorch Understand the theory and working mechanisms of neural network architectures and their implementation Discover best practices using a custom library created especially for this book Book Description Deep learning is the driving force behind many recent advances in various computer vision (CV) applications. This book takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets. You'll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. You'll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress, you'll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks. You'll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, you'll move your NN model to production on the AWS Cloud. By the end of this book, you'll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently. What you will learn Train a NN from scratch with NumPy and PyTorch Implement 2D and 3D multi-object detection and segmentation Generate digits and DeepFakes with autoencoders and advanced GANs Manipulate images using CycleGAN, Pix2PixGAN, StyleGAN2, and SRGAN Combine CV with NLP to perform OCR, image captioning, and object detection Combine CV with reinforcement learning to build agents that play pong and self-drive a car Deploy a deep learning model on the AWS server using FastAPI and Docker Implement over 35 NN architectures and common OpenCV utilities Who this book is for This book is for beginners to PyTorch and intermediate-level machine learning practitioners who are looking to get well-versed with computer vision techniques using deep learning and PyTorch. If you are just getting started with neural networks, you'll find the use cases accompanied by notebooks in GitHub present in this book useful. Basic knowledge of the Python programming language and machine learning is all you need to get started with this book.

Introduce your students to image processing with the industry’s most prized text For 40 years, Image Processing has been the foundational text for the study of digital image processing. The book is suited for students at the college senior and first-year graduate level with prior background in mathematical analysis, vectors, matrices, probability, statistics, linear systems, and computer programming. As in all earlier editions, the focus of this edition of the book is on fundamentals. The 4th Edition, which celebrates the book’s 40th anniversary, is based on an extensive survey of faculty, students, and independent readers in 150 institutions from 30 countries. Their feedback led to expanded or new coverage of topics such as deep learning and deep neural networks, including convolutional neural nets, the scale-invariant feature transform (SIFT), maximally-stable extremal regions (MSERs), graph cuts, k-means clustering and superpixels, active contours (snakes and level sets), and exact histogram matching. Major improvements were made in reorganizing the material on image transforms into a more cohesive presentation, and in the discussion of spatial kernels and spatial filtering. Major revisions and additions were made to examples and homework exercises throughout the book. For the first time, we added MATLAB projects at the end of every chapter, and compiled support packages for you and your teacher containing, solutions, image databases, and sample code. The support materials for this title can be found at www.ImageProcessingPlace.com

If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python.\nProgramming Computer Vision with Python explains computer vision in broad terms that won’t bog you down in theory. You get complete code samples with explanations on how to reproduce and build upon each example, along with exercises to help you apply what you’ve learned. This book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills.\n\nLearn techniques used in robot navigation, medical image analysis, and other computer vision applications\nWork with image mappings and transforms, such as texture warping and panorama creation\nCompute 3D reconstructions from several images of the same scene\nOrganize images based on similarity or content, using clustering methods\nBuild efficient image retrieval techniques to search for images based on visual content\nUse algorithms to classify image content and recognize objects\nAccess the popular OpenCV library through a Python interface\n

This library is useful for practitioners, and is an excellent tool for those entering the field: it is a set of computer vision algorithms that work as advertised. -William T. Freeman, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology 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. Computer vision is everywhere-in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It stitches Google maps and Google Earth together, checks the pixels on Lcd screens, and makes sure the stitches in your shirt are sewn properly. 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. Learning OpenCv will teach any developer or hobbyist to use the framework quickly with the help of hands-on exercises in each chapter. This book includes: A thorough introduction to OpenCv Getting input from cameras Transforming images Segmenting images and shape matching Pattern recognition, including face detection Tracking and motion in 2 and 3 dimensions 3D reconstruction from stereo vision Machine learning algorithms Getting machines to see is a challenging but entertaining goal. Whether you want to build simple or sophisticated vision applications, Learning OpenCv is the book you need to get started.

Computer vision is the field of science that is concerned with the development of computers to achieve high-level understanding using digital images or videos. It includes the processes of acquiring, processing and understanding of digital images. It also involves the extraction of data from the real world for the purpose of producing numerical or symbolic information. Some of the areas of interest in computer vision include scene reconstruction, object recognition, 3D pose interpretation, motion estimation, image restoration, etc. The applications of computer vision are in the development of artificial intelligence, surveillance, medical imaging, topographical modeling, navigation, among many others. This book brings forth some of the most innovative concepts and elucidates the unexplored aspects of this discipline. From theories to research to practical applications, studies related to all contemporary topics of relevance to this field have also been included. This book attempts to assist those with a goal of delving into the field of computer vision.

Get started in the rapidly expanding field of computer vision with this practical guide. Written by Adrian Kaehler and Gary Bradski, creator of the open source OpenCV library, this book provides a thorough introduction for developers, academics, roboticists, and hobbyists. You’ll learn what it takes to build applications that enable computers to "see" and make decisions based on that data.\nWith over 500 functions that span many areas in vision, OpenCV is used for commercial applications such as security, medical imaging, pattern and face recognition, robotics, and factory product inspection. This book gives you a firm grounding in computer vision and OpenCV for building simple or sophisticated vision applications. Hands-on exercises in each chapter help you apply what you’ve learned.\nThis volume covers the entire library, in its modern C++ implementation, including machine learning tools for computer vision.\n\nLearn OpenCV data types, array types, and array operations\nCapture and store still and video images with HighGUI\nTransform images to stretch, shrink, warp, remap, and repair\nExplore pattern recognition, including face detection\nTrack objects and motion through the visual field\nReconstruct 3D images from stereo vision\nDiscover basic and advanced machine learning techniques in OpenCV\n

The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.

This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.

Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasksKey Features\nTrain different kinds of deep learning model from scratch to solve specific problems in Computer Vision\nCombine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more\nIncludes tips on optimizing and improving the performance of your models under various constraints\nBook DescriptionDeep 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. \nIn 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\nSet up an environment for deep learning with Python, TensorFlow, and Keras\nDefine and train a model for image and video classification\nUse features from a pre-trained Convolutional Neural Network model for image retrieval\nUnderstand and implement object detection using the real-world Pedestrian Detection scenario\nLearn about various problems in image captioning and how to overcome them by training images and text together\nImplement similarity matching and train a model for face recognition\nUnderstand the concept of generative models and use them for image generation\nDeploy your deep learning models and optimize them for high performance\nWho This Book Is ForThis 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.Table of Contents\nIntroduction to Deep Learning\nImage Classification\nImage Retrieval\nObject Detection\nSemantic Segmentation\nSimilarity Learning\nGenerative Models\nImage Captioning\nVideo Classification\nDeployment\n

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.

Renowned professor and author Gilbert Strang demonstrates that linear algebra is a fascinating subject by showing both its beauty and value. While the mathematics is there, the effort is not all concentrated on proofs. Strang's emphasis is on understanding. He explains concepts, rather than deduces. This book is written in an informal and personal style and teaches real mathematics. The gears change in Chapter 2 as students reach the introduction of vector spaces. Throughout the book, the theory is motivated and reinforced by genuine applications, allowing pure mathematicians to teach applied mathematics.

About the Author\\nKatsushi Ikeuchi is a Professor of the University of Tokyo, Japan. He received the Ph.D. degree in Information Engineering from the University of Tokyo in 1978. After working at the Artificial Intelligence Laboratory, MIT for three years, the Electrotechnical Laboratory, MITI for five years, and the School of Computer Science, Carnegie Mellon University for ten years, he joined the university in 1996.\nHe has served program/general chairman of several international conferences, including 1995 IEEE-IROS (General), 1996 IEEE-CVPR (Program), 1999 IEEE-ITSC (General) and 2003 ICCV (Program). He is an Editor-in-Chief of the International Journal of Computer Vision. He is a fellow of IEEE, IEICE, IPSJ, and RSJ.He has received several awards, including Distinguished Researcher Award from IEEE PAMI-TC and Shiju Hou Sho (Medal of Honor with Purple ribbons) from Japanese Emperor.\\nThis comprehensive reference provides easy access to relevant information on all aspects of Computer Vision. An A-Z format of over 240 entries offers a diverse range of topics for those seeking entry into any aspect within the broad field of Computer Vision. Over 200 Authors from both industry and academia contributed to this volume.\nEach entry includes synonyms, a definition and discussion of the topic, and a robust bibliography. Extensive cross-references to other entries support efficient, user-friendly searches for immediate access to relevant information. Entries were peer-reviewed by a distinguished international advisory board, both scientifically and geographically diverse, ensuring balanced coverage. Over 3700 bibliographic references for further reading enable deeper exploration into any of the topics covered.\nThe content of Computer Vision: A Reference Guide is expository and tutorial, making the book a practical resource for students who are considering entering the field, as well as professionals in other fields who need to access this vital information but may not have the time to work their way through an entire text on their topic of interest.

This book develops the mathematical foundation of modern image processing and low-level computer vision, bridging contemporary mathematics with state-of-the-art methodologies in modern image processing, whilst organizing contemporary literature into a coherent and logical structure. The authors have integrated the diversity of modern image processing approaches by revealing the few common threads that connect them to Fourier and spectral analysis, the machinery that image processing has been traditionally built on. The text is systematic and well organized: the geometric, functional, and atomic structures of images are investigated, before moving to a rigorous development and analysis of several image processors. The book is comprehensive and integrative, covering the four most powerful classes of mathematical tools in contemporary image analysis and processing while exploring their intrinsic connections and integration. The material is balanced in theory and computation, following a solid theoretical analysis of model building and performance with computational implementation and numerical examples.

Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic design from a practical standpoint and provides insight into the theoretical tools needed to support these skills. The book covers a wide range of topics―from numerical linear algebra to optimization and differential equations―focusing on real-world motivation and unifying themes. It incorporates cases from computer science research and practice, accompanied by highlights from in-depth literature on each subtopic. Comprehensive end-of-chapter exercises encourage critical thinking and build students’ intuition while introducing extensions of the basic material. The text is designed for advanced undergraduate and beginning graduate students in computer science and related fields with experience in calculus and linear algebra. For students with a background in discrete mathematics, the book includes some reminders of relevant continuous mathematical background.