Machine Vision Algorithms and Applications

Author: Carsten Steger,Markus Ulrich,Christian Wiedemann

Publisher: John Wiley & Sons

ISBN: 3527413650

Category: Science

Page: 516

View: 778

The second edition of this successful machine vision textbook is completely updated, revised and expanded by 15% to reflect the developments of recent years in the fields of image acquisition, machine vision algorithms and applications. The new content includes, but is not limited to, a discussion of new cameras and image acquisition interfaces, 3D sensors and technologies, 3D object recognition and 3D image reconstruction. The authors retain their balanced approach with sufficient coverage of the theory and a strong focus on applications. All examples are based on the latest version of the machine vision software HALCON 13, a trial version of which is available from the authors' website.

Machine Vision Algorithms and Applications

Author: Carsten Steger,Markus Ulrich,Christian Wiedemann

Publisher: John Wiley & Sons

ISBN: 352781289X

Category: Science

Page: 280

View: 6775

The second edition of this successful machine vision textbook is completely updated, revised and expanded by 15% to reflect the developments of recent years in the fields of image acquisition, machine vision algorithms and applications. The new content includes, but is not limited to, a discussion of new cameras and image acquisition interfaces, 3D sensors and technologies, 3D object recognition and 3D image reconstruction. The authors retain their balanced approach with sufficient coverage of the theory and a strong focus on applications. All examples are based on the latest version of the machine vision software HALCON 13, a trial version of which is available from the authors' website.

Computer Vision

Algorithms and Applications

Author: Richard Szeliski

Publisher: Springer Science & Business Media

ISBN: 9781848829350

Category: Computers

Page: 812

View: 4766

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 the associated website, http://szeliski.org/Book/. 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.

Machine Vision

Theory, Algorithms, Practicalities

Author: E. R. Davies

Publisher: Elsevier

ISBN: 1483275612

Category: Computers

Page: 572

View: 3563

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.

Computer Vision

Principles, Algorithms, Applications, Learning

Author: E. R. Davies

Publisher: Academic Press

ISBN: 012809575X

Category: Computers

Page: 900

View: 4484

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++)

Concise Computer Vision

An Introduction into Theory and Algorithms

Author: Reinhard Klette

Publisher: Springer Science & Business Media

ISBN: 1447163206

Category: Computers

Page: 429

View: 2227

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.

Machine Vision Algorithms in Java

Techniques and Implementation

Author: Paul F. Whelan,Derek Molloy

Publisher: Springer Science & Business Media

ISBN: 1447102517

Category: Computers

Page: 284

View: 2511

This book presents key machine vision techniques and algorithms, along with the associated Java source code. Special features include a complete self-contained treatment of all topics and techniques essential to the understanding and implementation of machine vision; an introduction to object-oriented programming and to the Java programming language, with particular reference to its imaging capabilities; Java source code for a wide range of real-world image processing and analysis functions; an introduction to the Java 2D imaging and Java Advanced Imaging (JAI) API; and a wide range of illustrative examples.

Computer Vision for Visual Effects

Author: Richard J. Radke

Publisher: Cambridge University Press

ISBN: 0521766877

Category: Business & Economics

Page: 405

View: 3051

This book explores the fundamental computer vision principles and state-of-the-art algorithms used to create cutting-edge visual effects for movies and television. It describes classical computer vision algorithms and recent developments, features more than 200 original images, and contains in-depth interviews with Hollywood visual effects artists that tie the mathematical concepts to real-world filmmaking.

Multiple View Geometry in Computer Vision

Author: Richard Hartley,Andrew Zisserman

Publisher: Cambridge University Press

ISBN: 1139449141

Category: Computers

Page: N.A

View: 2191

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.

Modern Mathematics and Applications in Computer Graphics and Vision

Author: Hongyu Guo

Publisher: World Scientific Publishing Company

ISBN: 9814449350

Category: Computers

Page: 524

View: 6348

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.

Numerical Algorithms

Methods for Computer Vision, Machine Learning, and Graphics

Author: Justin Solomon

Publisher: CRC Press

ISBN: 1482251892

Category: Computers

Page: 400

View: 1510

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.

Programming Computer Vision with Python

Tools and algorithms for analyzing images

Author: Jan Erik Solem

Publisher: "O'Reilly Media, Inc."

ISBN: 1449341934

Category: Computers

Page: 264

View: 6489

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

An Introduction to 3D Computer Vision Techniques and Algorithms

Author: Boguslaw Cyganek,J. Paul Siebert

Publisher: John Wiley & Sons

ISBN: 1119964474

Category: Science

Page: 504

View: 3005

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

Models, Learning, and Inference

Author: Simon J. D. Prince

Publisher: Cambridge University Press

ISBN: 1107011795

Category: Computers

Page: 580

View: 3776

A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms.

Color in Computer Vision

Fundamentals and Applications

Author: Theo Gevers,Arjan Gijsenij,Joost van de Weijer,Jan-Mark Geusebroek

Publisher: John Wiley & Sons

ISBN: 1118350073

Category: Technology & Engineering

Page: 384

View: 2595

While the field of computer vision drives many of today’s digital technologies and communication networks, the topic of color has emerged only recently in most computer vision applications. One of the most extensive works to date on color in computer vision, this book provides a complete set of tools for working with color in the field of image understanding. Based on the authors’ intense collaboration for more than a decade and drawing on the latest thinking in the field of computer science, the book integrates topics from color science and computer vision, clearly linking theories, techniques, machine learning, and applications. The fundamental basics, sample applications, and downloadable versions of the software and data sets are also included. Clear, thorough, and practical, Color in Computer Vision explains: Computer vision, including color-driven algorithms and quantitative results of various state-of-the-art methods Color science topics such as color systems, color reflection mechanisms, color invariance, and color constancy Digital image processing, including edge detection, feature extraction, image segmentation, and image transformations Signal processing techniques for the development of both image processing and machine learning Robotics and artificial intelligence, including such topics as supervised learning and classifiers for object and scene categorization Researchers and professionals in computer science, computer vision, color science, electrical engineering, and signal processing will learn how to implement color in computer vision applications and gain insight into future developments in this dynamic and expanding field.

Machine Vision for Inspection and Measurement

Author: Herbert Freeman

Publisher: Elsevier

ISBN: 0323155588

Category: Technology & Engineering

Page: 332

View: 4902

Machine Vision for Inspection and Measurement contains the proceedings of the Second Annual Workshop on Machine Vision sponsored by the Center for Computer Aids for Industrial Productivity (CAIP) at Rutgers University and held on April 25-26, 1988 in New Brunswick, New Jersey. The papers explore the application of machine vision to inspection and measurement and cover topics such as the problem of object-pose estimation and depth recovery through inverse optics. The use of machine vision techniques in inspection of integrated circuits and semiconductor wafers is also discussed. Comprised of 11 chapters, this book opens with the problem of using fine-grained parallel machines for VLSI inspection. The discussion then turns to a variety of real-life applications of machine vision, including inspection of integrated circuits, semiconductor wafers, TV-tube glass, and mechanical parts. The use of machine vision to measure the curvature of the human cornea for vision correction and contact lens fitting purposes is also considered. The remaining chapters focus on motion estimation from stereo sequences using orthographic-view algorithms; photometric sampling for determining surface shape and reflectance; and efficient depth recovery by means of inverse optics. A chapter addresses the question of whether the industry is ready for machine vision and comes up with some optimistic predictions. This monograph will be of interest to practitioners in the fields of computer science and applied mathematics.

Research Developments in Computer Vision and Image Processing: Methodologies and Applications

Methodologies and Applications

Author: Srivastava, Rajeev

Publisher: IGI Global

ISBN: 1466645598

Category: Computers

Page: 451

View: 2817

Similar to the way in which computer vision and computer graphics act as the dual fields that connect image processing in modern computer science, the field of image processing can be considered a crucial middle road between the vision and graphics fields. Research Developments in Computer Vision and Image Processing: Methodologies and Applications brings together various research methodologies and trends in emerging areas of application of computer vision and image processing. This book is useful for students, researchers, scientists, and engineers interested in the research developments of this rapidly growing field.

Sparse Coding and its Applications in Computer Vision

Author: Zhaowen Wang,Jianchao Yang,Haichao Zhang,Zhangyang Wang,Yingzhen Yang,Ding Liu,Thomas S Huang

Publisher: World Scientific

ISBN: 9814725064

Category: Computers

Page: 240

View: 8569

This book provides a broader introduction to the theories and applications of sparse coding techniques in computer vision research. It introduces sparse coding in the context of representation learning, illustrates the fundamental concepts, and summarizes the most active research directions. A variety of applications of sparse coding are discussed, ranging from low-level image processing tasks such as super-resolution and de-blurring to high-level semantic understanding tasks such as image recognition, clustering and fusion. The book is suitable to be used as an introductory overview to this field, with its theoretical part being both easy and precious enough for quick understanding. It is also of great value to experienced researchers as it offers new perspective to the underlying mechanism of sparse coding, and points out potential future directions for different applications. Contents:IntroductionTheories of Sparse CodingImage Super-ResolutionImage DeblurringSensor FusionClusteringObject RecognitionHyper-Spectral Image ModelingConclusions Readership: Graduate students, researchers and professionals in the field of machine perception, pattern recognition, image analysis, artificial intelligence, machine learning. Key Features:Explanation of sparse coding from both theoretical and practical point of viewsA comprehensive review of the applications of sparse coding in both low-level and high-level vision problemsInvestigating future research directions of sparse coding by making connection with the current state-of-the-art feature learning models, including deep neural networksKeywords:Sparse Coding;Sparse Representation;Dictionary Learning;Super-Resolution;De-Blurring;Sensor Fusion;Image Classification;Hyper-Spectral Image

Intelligent Machine Vision

Techniques, Implementations and Applications

Author: Bruce Batchelor,Frederick Waltz

Publisher: Springer Science & Business Media

ISBN: 1447102398

Category: Computers

Page: 422

View: 5220

A number of important aspects of intelligent machine vision in one volume, describing the state of the art and current developments in the field, including: fundamentals of 'intelligent'image processing for machine vision systems; algorithm optimisation; implementation in high-speed electronic digital hardware; implementation in an integrated high-level software environment and applications for industrial product quality and process control. Backed by numerous illustrations, created using the authors IP software, this book will be of interest to researchers in the field of machine vision wishing to understand the discipline and develop new techniques. Also useful for under- and postgraduates.

Robot Vision

Author: Berthold Horn,Berthold Klaus,Paul Horn

Publisher: MIT Press

ISBN: 9780262081597

Category: Computers

Page: 509

View: 4106

"Presents a solid framework for understanding existing work and planning future research."--Cover.