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Books : Computers & Internet : Computer Science : Artificial Intelligence : Computer Vision
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"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.
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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 used in the book for solving this are taken from projective geometry and photogrammetry. 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. Recent major developments in the theory and practice of scene reconstruction are described in detail in a unified framework. The authors provide comprehensive background material, so a reader familiar with linear algebra and basic numerical methods will be able to understand the projective geometry and estimation algorithms presented, and implement the algorithms directly from the book.
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The field of machine vision has expanded extensively since the First Edition of Machine Vision was published by Academic Press in 1990. As a result, this Second Edition contains significant amounts of new material on artificial neural networks, mathematical morphology, motion, invariance, texture analysis, x-ray inspection, and foreign object detection. Intermediate level vision is examined in depth (especially Hough transforms), and automated visual inspectionis discussed. The author takes care to consider theoretical aspects as well as practical applications, including perspective invariants and robust statistics. Written in a user-friendly style and full of up-to-date methods, Machine Vision, Second Edition will be an essential volume for students and professionals in the field.
Key Features
* Gives considerable emphasis to robust analysis of images to demonstrate how problems of occlusion, noise, distortion, and variability may be overcome
* Introduces Hough transforms as an integral part of the text and shows how they may be applied in a variety of situations
* Presents the topic of robust statistics non-mathematically in the context of vision algorithms
* Considers the role of neural networks in machine vision
* Shows how the concepts of perspective invariance provide basic strategies for 2-D and 3-D vision
* Studies image transformations and the prespective n-point problem systematically to clarify how interpretation may proceed in various geometrical situations
* Pays special attention to the detection of defects, foreign objects, and real-time implementation hardware in consideration of automated visual inspection
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(Pearson Education) A textbook and reference for students and practitioners, presenting the necessary theory for work in fields where significant information must be extracted from images. Topics covered include databases and virtual and augmented reality, and the text includes more than 250 exercises and programming projects. DLC: Computer vision.
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The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.
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Biometric recognition, or simply Biometrics, is a rapidly evolving field with applications ranging from accessing one's computer to gaining entry into a country. Biometric systems rely on the use of physical or behavioral traits, such as fingerprints, face, voice and hand geometry, to establish the identity of an individual. The deployment of large-scale biometric systems in both commercial (e.g., grocery stores, amusement parks, airports) and government (e.g., US-VISIT) applications has served to increase the public's awareness of this technology. This rapid growth has also highlighted the challenges associated with designing and deploying biometric systems. Indeed, the problem of biometric recognition is a "Grand Challenge" in its own right. The past five years has seen a significant growth in biometric research resulting in the development of innovative sensors, robust and efficient algorithms for feature extraction and matching, enhanced test methodologies and novel applications. These advances have resulted in robust, accurate, secure and cost effective biometric systems.
The Handbook of Biometrics -- an edited volume contributed by prominent invited researchers in Biometrics -- describes the fundamentals as well as the latest advancements in the burgeoning field of biometrics. It is designed for professionals composed of practitioners and researchers in Biometrics, Pattern Recognition and Computer Security. The Handbook of Biometrics can be used as a primary textbook for an undergraduate biometrics class. This book is also suitable as a secondary textbook or reference for advanced-level students in computer science.
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Despite the availability of cheap, fast, accurate and usable eye trackers, there is little information available on how to develop, implement and use these systems. This book aims to fill that gap in the market by providing an accessible introduction for practitioners and students.The first part of the book covers useful background information, including an introduction to the human visual system and key issues in visual perception and eye movement. The second part surveys eye-tracking devices and gives a detailed introduction to the technical requirements for installing a system and developing an application program. It focuses on video-based, corneal-reflection eye trackers -- the most widely available and affordable type of system.
The final part looks at a number of interesting and challenging applications in areas such as Human Factors, Collaborative Systems, Virtual Reality, Marketing and Advertising. Eye Tracking Methodology: Theory and Practice will be an invaluable guide for practitioners responsible for developing or implementing an eye tracking system. It will also be an invaluable teaching text for relevant modules on advanced undergraduate and postgraduate courses such as Computer Vision, Cognition, HCI and Usability.
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Senior/Graduate level courses on computer vision, robot vision and image processing in electrical and computer engineering, mathematics, and computer science departments, and an essential reference for researchers and scientists in the field of computer vision. An applied introduction to modern computer vision, focusing on a set of computational techniques for 3-D imaging. Covers a wide range of fundamental problems encountered within computer vision and provides detailed algorithmic and theoretical solutions for each. Each chapter concentrates on a specific problem and solves it by building on previous results.
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This introduction to the field of computer vision focuses on basic concepts and techniques. The thrust is to give practitioners what they need to know to develop a practical machine vision system. Binary vision, segmentation, constraint propagation techniques are presented as are camera calibration, color and texture, detection of motion, and object recognition. This text is appropriate for use in Computer Science and Electrical Engineering departments at the senior and graduate level.
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A blending of concepts and approaches from the fields of photogrammetry and computer vision, examining techniques relating to quantitative image analysis. Includes real-world examples and study material. For researchers, practitioners, developers and students. DLC: Computer vision.
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This robust text provides deep and wide coverage of the full range of topics encountered in the field of image processing and machine vision. As a result, it can serve undergraduates, graduates, researchers, and professionals looking for a readable reference. The book's encyclopedic coverage of topics is wide, and it can be used in more than one course (both image processing and machine vision classes). In addition, while advanced mathematics is not needed to understand basic concepts (making this a good choice for undergraduates), rigorous mathematical coverage is included for more advanced readers. It is also distinguished by its easy-to-understand algorithm descriptions of difficult concepts, and a wealth of carefully selected problems and examples.
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"A magnificant tour de force. Three-Dimensional Computer Vision deals with an extremely broad and important chunk of computer vision and covers the area with excellent breadth. It provides examples of the described techniques being applied to real images, and it is built on the kind of solid mathematical underpinnings that are essential if the field is to move from the 'black art' stage to a real science. Anyone who claims to be serious about research in this area absolutely must be aware of this work." -- W. Eric L. Grimson, AI Laboratory, M.I.T.This monograph by one of the world's leading vision researchers provides a thorough, mathematically rigorous exposition of a broad and vital area in computer vision: the problems and techniques related to three-dimensional (stereo) vision and motion. The emphasis is on using geometry to solve problems in stereo and motion, with examples from navigation and object recognition. Artificial Intelligence series
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This book is about visualization, systematically incorporating the fantastic human pattern recognition into the problem-solving process, and focusing on parallel coordinates. The barrier, imposed by our three-dimensional habitation and perceptual experience, has been breached by this innovative and versatile methodology. The accurate visualization of multidimensional problems and multivariate data unlocks insights into the role of dimensionality.
Beginning with an introductory chapter on geometry, the mathematical foundations are intuitively developed, interlaced with applications to data mining, information visualization, computer vision, geometric modeling, collision avoidance for air traffic and process-control. Many results appear for the first time. Multidimensional lines, planes, proximities, surfaces and their properties are unambiguously recognized (i.e. convexity viewed in any dimension) enabling powerful construction algorithms (for intersections, interior-points, linear-programming).
Key features of Parallel Coordinates:
* An easy-to-read self-contained chapter on data mining and information visualization
* Numerous exercises with solutions, from basic to advanced topics, course projects and research directions
* "Fast Track" markers throughout provide a quick grasp of essential material.
* Interactive Learning Module (ILM) CD: designed for classroom demonstration and fun experimentation for mastering key topics and examples cross-referenced in the text
* Extensive bibliography, index, and a chapter containing a collection of recent results (i.e. visualizing large networks, complex-valued functions and more)
Parallel Coordinates requires only an elementary knowledge of linear algebra. It is well-suited for self-study and as a textbook (or companion) for courses on information visualization, data mining, mathematics, statistics, computer science, engineering, finance, management, manufacturing, in scientific disciplines and even the arts.
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This book is a dialogue between researchers who study biological visual and computer scientists and engineers who seek to build computer vision systems that actively explore the environment. By describing new and important ways to design robots analogous to biological visual systems, it provides deep insights into the problems and solutions of computer vision. The book is divided into four parts, each addressing a different aspect of exploratory or active vision in biological and machine vision systems. The chapters are written by a cross-disciplinary selection of leading researchers who study computer and biological vision. As a result, many researchers and students concerned with vision will find this an invaluable survey to this fast-moving field.
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Embedded Computer Vision, exemplified by the migration from powerful workstations to embedded processors in computer vision applications, is a new and emerging field that enables an associated shift in application development and implementation.
This comprehensive volume brings together a wealth of experiences from leading researchers in the field of embedded computer vision, from both academic and industrial research centers, and covers a broad range of challenges and trade-offs brought about by this paradigm shift. Part I provides an exposition of basic issues and applications in the area necessary for understanding the present and future work. Part II offers chapters based on the most recent research and results. Finally, the last part looks ahead, providing a sense of what major applications could be expected in the near future, describing challenges in mobile environments, video analytics, and automotive safety applications.
Features:
• Discusses the latest state-of-the-art techniques in embedded computer vision
• Presents a thorough introductory section on hardware and architectures, design methodologies, and video analytics to aid the reader’s understanding through the following chapters
• Offers emphasis on tackling important problems for society, safety, security, health, mobility, connectivity, and energy efficiency
• Discusses evaluation of trade-offs required to design cost-effective systems for successful products
• Explores the advantages of various architectures, development of high-level software frameworks and cost-effective algorithmic alternatives
• Examines issues of implementation on fixed-point processors, presented through an example of an automotive safety application
• Offers insights from leaders in the field on what future applications will be
This book is a welcome collection of stand-alone articles, ideal for researchers, practitioners, and graduate students. It provides historical perspective, the latest research results, and a vision for future developments in the emerging field of embedded computer vision. Supplementary material can be found at http://www.embeddedvisioncentral.com.
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Interactive curve modeling techniques and their applications are extremely useful in a number of academic and industrial settings, and specifically play a significant role in multidisciplinary problem solving, such as in font design, designing objects, CAD/CAM, medical operations, scientific data visualization, virtual reality, character recognition, and object recognition, etc. Various problems such as iris, fingerprint, and signature recognition, can also be intelligently solved and automated using curve techniques.
This broad-ranging textbook covers curve modeling with solutions to real life problems relating to computer graphics, vision, image processing, geometric modeling and CAD/CAM. Well-explained, easy-to-understand chapters deal with basic concepts, curve design techniques and their use to various applications, and a wide range of problems with their automated solutions via computers.
Features and topics:
• Provides a class of practical solutions to real life and multidisciplinary problems
• Offers students supporting pedagogical tools in the form of a thorough introductory chapter, individual chapter introductions and end summaries, as well as end-of-chapter exercises
• Presents both classical and up-to-date theory, with practice to get problems solved in diverse disciplines
• Focuses on interdisciplinary methods and up-to-date methodologies in the field
• Imparts a description and analysis of a variety of classes of splines for use in CAGD (computer-aided geometric design), CAD (computer-aided design), CAE (computer-aided engineering), computer graphics, computer vision, image processing and other disciplines
• Aims to stimulate views and provide a source where readers can find the latest state-of-the-art developments in the field, including a variety of techniques, applications, and systems necessary for solving problems
Interactive Curve Modeling also will serve as an important tool for readers; as an extremely useful textbook for senior undergraduates as well as graduate students in the areas of computer science, engineering, and other computational sciences. This comprehensive text can equally act as an invaluable resource for those practitioners and researchers looking for an introduction to the state-of-the-art on the topic.
Professor Sarfraz has many years of experience researching and teaching in the field, winning an award for Excellence in Research at the King Fahd University of Petroleum and Minerals, Saudi Arabia.
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An introduction to computer vision, covering the structure and properties of the visual world. This concise guide stresses fundamental concepts, and also provides details and pointers with respect to recent developments. The author pursues the narrow view of vision covering the structure and properties of the visual world, thereby providing a lucid introduction for the novice and a fresh perspective to the expert.
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Image-based rendering, as an area of overlap between computer graphics and computer vision, uses computer vision techniques to aid in sythesizing new views of scenes. Image-based rendering methods are having a substantial impact on the field of computer graphics, and also play an important role in the related field of multimedia systems, for applications such as teleconferencing, remote instruction and surgery, virtual reality and entertainment. The book develops a novel way of formalizing the view synthesis problem under the full perspective model, yielding a clean, linear warping equation. It shows new techniques for dealing with visibility issues such as partial occlusion and "holes". Furthermore, the author thoroughly re-evaluates the requirements that view synthesis places on stereo algorithms and introduces two novel stereo algorithms specifically tailored to the application of view synthesis.
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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, and is accompanied by an interactive CD-ROM.
* 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
* An accompanying CD-ROM provides full text with hyperlinks for quick searching and browsing along with reference material, interactive software components, code examples, image material, full-color figures, and references to Internet sources
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Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables systematic development of optimal vision algorithms when used with optimization principles.
This detailed and thoroughly enhanced third edition presents a comprehensive study / reference to theories, methodologies and recent developments in solving computer vision problems based on MRFs, statistics and optimisation. It treats various problems in low- and high-level computational vision in a systematic and unified way within the MAP-MRF framework. Among the main issues covered are: how to use MRFs to encode contextual constraints that are indispensable to image understanding; how to derive the objective function for the optimal solution to a problem; and how to design computational algorithms for finding an optimal solution.
Easy-to-follow and coherent, the revised edition is accessible, includes the most recent advances, and has new and expanded sections on such topics as:
• Discriminative Random Fields (DRF)
• Strong Random Fields (SRF)
• Spatial-Temporal Models
• Total Variation Models
• Learning MRF for Classification (motivation + DRF)
• Relation to Graphic Models
• Graph Cuts
• Belief Propagation
Features:
• Focuses on the application of Markov random fields to computer vision problems, such as image restoration and edge detection in the low-level domain, and object matching and recognition in the high-level domain
• Presents various vision models in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation
• Uses a variety of examples to illustrate how to convert a specific vision problem involving uncertainties and constraints into essentially an optimization problem under the MRF setting
• Introduces readers to the basic concepts, important models and various special classes of MRFs on the regular image lattice and MRFs on relational graphs derived from images
• Examines the problems of parameter estimation and function optimization
• Includes an extensive list of references
This broad-ranging and comprehensive volume is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It has been class-tested and is suitable as a textbook for advanced courses relating to these areas.