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Concise Computer Vision

Concise Computer Vision Author Reinhard Klette
ISBN-10 9781447163206
Release 2014-01-04
Pages 429
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This textbook provides an accessible general introduction to the essential topics in computer vision. Classroom-tested programming exercises and review questions are also supplied at the end of each chapter. Features: provides an introduction to the basic notation and mathematical concepts for describing an image and the key concepts for mapping an image into an image; explains the topologic and geometric basics for analysing image regions and distributions of image values and discusses identifying patterns in an image; introduces optic flow for representing dense motion and various topics in sparse motion analysis; describes special approaches for image binarization and segmentation of still images or video frames; examines the basic components of a computer vision system; reviews different techniques for vision-based 3D shape reconstruction; includes a discussion of stereo matchers and the phase-congruency model for image features; presents an introduction into classification and learning.



Concise Computer Vision

Concise Computer Vision Author Reinhard Klette
ISBN-10 1447174143
Release 2019-06-13
Pages
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This textbook provides a concise and accessible general introduction to the essential topics in computer vision, complete with classroom-tested programming exercises and review questions supplied at the end of each chapter. This enhanced and updated new edition also includes two new chapters on uncertainties and on learning.



Computer Vision and Applications

Computer Vision and Applications Author Bernd Jahne
ISBN-10 9780080502625
Release 2000-05-24
Pages 679
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Based on the highly successful 3-volume reference Handbook of Computer Vision and Applications, this concise edition covers in a single volume the entire spectrum of computer vision ranging form the imaging process to high-end algorithms and applications. This book consists of three parts, including an application gallery. Bridges the gap between theory and practical applications Covers modern concepts in computer vision as well as modern developments in imaging sensor technology Presents a unique interdisciplinary approach covering different areas of modern science



Machine Vision

Machine Vision Author E. R. Davies
ISBN-10 9781483275611
Release 2014-07-10
Pages 572
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Machine Vision: Theory, Algorithms, Practicalities covers the limitations, constraints, and tradeoffs of vision algorithms. This book is organized into four parts encompassing 21 chapters that tackle general topics, such as noise suppression, edge detection, principles of illumination, feature recognition, Bayes’ theory, and Hough transforms. Part 1 provides research ideas on imaging and image filtering operations, thresholding techniques, edge detection, and binary shape and boundary pattern analyses. Part 2 deals with the area of intermediate-level vision, the nature of the Hough transform, shape detection, and corner location. Part 3 demonstrates some of the practical applications of the basic work previously covered in the book. This part also discusses some of the principles underlying implementation, including on lighting and hardware systems. Part 4 highlights the limitations and constraints of vision algorithms and their corresponding solutions. This book will prove useful to students with undergraduate course on vision for electronic engineering or computer science.



Programming Computer Vision with Python

Programming Computer Vision with Python Author Jan Erik Solem
ISBN-10 9781449341930
Release 2012-06-19
Pages 264
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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. Programming 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. Learn techniques used in robot navigation, medical image analysis, and other computer vision applications Work with image mappings and transforms, such as texture warping and panorama creation Compute 3D reconstructions from several images of the same scene Organize images based on similarity or content, using clustering methods Build efficient image retrieval techniques to search for images based on visual content Use algorithms to classify image content and recognize objects Access the popular OpenCV library through a Python interface



Guide to 3D Vision Computation

Guide to 3D Vision Computation Author Kenichi Kanatani
ISBN-10 9783319484938
Release 2016-12-09
Pages 321
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This classroom-tested and easy-to-understand textbook/reference describes the state of the art in 3D reconstruction from multiple images, taking into consideration all aspects of programming and implementation. Unlike other computer vision textbooks, this guide takes a unique approach in which the initial focus is on practical application and the procedures necessary to actually build a computer vision system. The theoretical background is then briefly explained afterwards, highlighting how one can quickly and simply obtain the desired result without knowing the derivation of the mathematical detail. Features: reviews the fundamental algorithms underlying computer vision; describes the latest techniques for 3D reconstruction from multiple images; summarizes the mathematical theory behind statistical error analysis for general geometric estimation problems; presents derivations at the end of each chapter, with solutions supplied at the end of the book; provides additional material at an associated website.



Computer Vision

Computer Vision Author E. R. Davies
ISBN-10 9780128095751
Release 2017-11-14
Pages 900
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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/ Three new chapters on Machine Learning emphasise the way the subject has been developing; Two chapters cover Basic Classification Concepts and Probabilistic Models; and the The third covers the principles of Deep Learning Networks and shows their impact on computer vision, reflected in a new chapter Face Detection and Recognition. A new chapter on Object Segmentation and Shape Models reflects the methodology of machine learning and gives practical demonstrations of its application. In-depth discussions have been included on geometric transformations, the EM algorithm, boosting, semantic segmentation, face frontalisation, RNNs and other key topics. Examples and applications—including the location of biscuits, foreign bodies, faces, eyes, road lanes, surveillance, vehicles and pedestrians—give the ‘ins and outs’ of developing real-world vision systems, showing the realities of practical implementation. Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples. The ‘recent developments’ sections included in each chapter aim to bring students and practitioners up to date with this fast-moving subject. Tailored programming examples—code, methods, illustrations, tasks, hints and solutions (mainly involving MATLAB and C++)



Modern Mathematics and Applications in Computer Graphics and Vision

Modern Mathematics and Applications in Computer Graphics and Vision Author Hongyu Guo
ISBN-10 9789814449359
Release 2014-04-01
Pages 524
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This book presents a concise exposition of modern mathematical concepts, models and methods with applications in computer graphics, vision and machine learning. The compendium is organized in four parts — Algebra, Geometry, Topology, and Applications. One of the features is a unique treatment of tensor and manifold topics to make them easier for the students. All proofs are omitted to give an emphasis on the exposition of the concepts. Effort is made to help students to build intuition and avoid parrot-like learning. There is minimal inter-chapter dependency. Each chapter can be used as an independent crash course and the reader can start reading from any chapter — almost. This book is intended for upper level undergraduate students, graduate students and researchers in computer graphics, geometric modeling, computer vision, pattern recognition and machine learning. It can be used as a reference book, or a textbook for a selected topics course with the instructor's choice of any of the topics.



An Introduction to 3D Computer Vision Techniques and Algorithms

An Introduction to 3D Computer Vision Techniques and Algorithms Author Boguslaw Cyganek
ISBN-10 9781119964476
Release 2011-08-10
Pages 504
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Computer vision encompasses the construction of integrated vision systems and the application of vision to problems of real-world importance. The process of creating 3D models is still rather difficult, requiring mechanical measurement of the camera positions or manual alignment of partial 3D views of a scene. However using algorithms, it is possible to take a collection of stereo-pair images of a scene and then automatically produce a photo-realistic, geometrically accurate digital 3D model. This book provides a comprehensive introduction to the methods, theories and algorithms of 3D computer vision. Almost every theoretical issue is underpinned with practical implementation or a working algorithm using pseudo-code and complete code written in C++ and MatLab®. There is the additional clarification of an accompanying website with downloadable software, case studies and exercises. Organised in three parts, Cyganek and Siebert give a brief history of vision research, and subsequently: present basic low-level image processing operations for image matching, including a separate chapter on image matching algorithms; explain scale-space vision, as well as space reconstruction and multiview integration; demonstrate a variety of practical applications for 3D surface imaging and analysis; provide concise appendices on topics such as the basics of projective geometry and tensor calculus for image processing, distortion and noise in images plus image warping procedures. An Introduction to 3D Computer Vision Algorithms and Techniques is a valuable reference for practitioners and programmers working in 3D computer vision, image processing and analysis as well as computer visualisation. It would also be of interest to advanced students and researchers in the fields of engineering, computer science, clinical photography, robotics, graphics and mathematics.



Computer Vision

Computer Vision Author Simon J. D. Prince
ISBN-10 9781107011793
Release 2012-06-18
Pages 580
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A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms.



Dictionary of Computer Vision and Image Processing

Dictionary of Computer Vision and Image Processing Author Robert B. Fisher
ISBN-10 9781118706817
Release 2013-11-08
Pages 384
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Written by leading researchers, the 2nd Edition of the Dictionary of Computer Vision & Image Processing is a comprehensive and reliable resource which now provides explanations of over 3500 of the most commonly used terms across image processing, computer vision and related fields including machine vision. It offers clear and concise definitions with short examples or mathematical precision where necessary for clarity that ultimately makes it a very usable reference for new entrants to these fields at senior undergraduate and graduate level, through to early career researchers to help build up knowledge of key concepts. As the book is a useful source for recent terminology and concepts, experienced professionals will also find it a valuable resource for keeping up to date with the latest advances. New features of the 2nd Edition: Contains more than 1000 new terms, notably an increased focus on image processing and machine vision terms; Includes the addition of reference links across the majority of terms pointing readers to further information about the concept under discussion so that they can continue to expand their understanding; Now available as an eBook with enhanced content: approximately 50 videos to further illustrate specific terms; active cross-linking between terms so that readers can easily navigate from one related term to another and build up a full picture of the topic in question; and hyperlinked references to fully embed the text in the current literature.



Introductory Techniques for 3 D Computer Vision

Introductory Techniques for 3 D Computer Vision Author Emanuele Trucco
ISBN-10 STANFORD:36105119837941
Release 1998
Pages 343
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This text provides readers with a starting point to understand and investigate the literature of computer vision, listing conferences, journals and Internet sites.



Algorithms for Image Processing and Computer Vision

Algorithms for Image Processing and Computer Vision Author J. R. Parker
ISBN-10 UOM:39015040683305
Release 1996-11-25
Pages 432
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A cookbook of the hottest new algorithms and cutting-edge techniques in image processing and computer vision This amazing book/CD package puts the power of all the hottest new image processing techniques and algorithms in your hands. Based on J. R. Parker's exhaustive survey of Internet newsgroups worldwide, Algorithms for Image Processing and Computer Vision answers the most frequently asked questions with practical solutions. Parker uses dozens of real-life examples taken from fields such as robotics, space exploration, forensic analysis, cartography, and medical diagnostics, to clearly describe the latest techniques for morphing, advanced edge detection, wavelets, texture classification, image restoration, symbol recognition, and genetic algorithms, to name just a few. And, best of all, he implements each method covered in C and provides all the source code on the CD. For the first time, you're rescued from the hours of mind-numbing mathematical calculations it would ordinarily take to program these state-of-the-art image processing capabilities into software. At last, nonmathematicians get all the shortcuts they need for sophisticated image recognition and processing applications. On the CD-ROM you'll find: * Complete code for examples in the book * A gallery of images illustrating the results of advanced techniques * A free GNU compiler that lets you run source code on any platform * A system for restoring damaged or blurred images * A genetic algorithms package



Handbook of Computer Vision Algorithms in Image Algebra

Handbook of Computer Vision Algorithms in Image Algebra Author Joseph N. Wilson
ISBN-10 9781420042382
Release 2000-09-21
Pages 448
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Image algebra is a comprehensive, unifying theory of image transformations, image analysis, and image understanding. In 1996, the bestselling first edition of the Handbook of Computer Vision Algorithms in Image Algebra introduced engineers, scientists, and students to this powerful tool, its basic concepts, and its use in the concise representation of computer vision algorithms. Updated to reflect recent developments and advances, the second edition continues to provide an outstanding introduction to image algebra. It describes more than 80 fundamental computer vision techniques and introduces the portable iaC++ library, which supports image algebra programming in the C++ language. Revisions to the first edition include a new chapter on geometric manipulation and spatial transformation, several additional algorithms, and the addition of exercises to each chapter. The authors-both instrumental in the groundbreaking development of image algebra-introduce each technique with a brief discussion of its purpose and methodology, then provide its precise mathematical formulation. In addition to furnishing the simple yet powerful utility of image algebra, the Handbook of Computer Vision Algorithms in Image Algebra supplies the core of knowledge all computer vision practitioners need. It offers a more practical, less esoteric presentation than those found in research publications that will soon earn it a prime location on your reference shelf.



A Guide to Convolutional Neural Networks for Computer Vision

A Guide to Convolutional Neural Networks for Computer Vision Author Salman Khan
ISBN-10 9781681730226
Release 2018-02-13
Pages 207
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Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.



Multiple View Geometry in Computer Vision

Multiple View Geometry in Computer Vision Author Richard Hartley
ISBN-10 9781139449144
Release 2004-03-25
Pages
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A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes from multiple images. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition. Comprehensive background material is provided, so readers familiar with linear algebra and basic numerical methods can understand the projective geometry and estimation algorithms presented, and implement the algorithms directly from the book.



Concise Computer Mathematics

Concise Computer Mathematics Author Ovidiu Bagdasar
ISBN-10 9783319017518
Release 2013-10-28
Pages 109
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Adapted from a modular undergraduate course on computational mathematics, Concise Computer Mathematics delivers an easily accessible, self-contained introduction to the basic notions of mathematics necessary for a computer science degree. The text reflects the need to quickly introduce students from a variety of educational backgrounds to a number of essential mathematical concepts. The material is divided into four units: discrete mathematics (sets, relations, functions), logic (Boolean types, truth tables, proofs), linear algebra (vectors, matrices and graphics), and special topics (graph theory, number theory, basic elements of calculus). The chapters contain a brief theoretical presentation of the topic, followed by a selection of problems (which are direct applications of the theory) and additional supplementary problems (which may require a bit more work). Each chapter ends with answers or worked solutions for all of the problems.