The second edition of this accepted reference work has been updated to reflect the rapid developments in the field and now covers both 2D and 3D imaging. Written by expert practitioners from leading companies operating in machine vision, this one-stop handbook guides readers through all aspects of image acquisition and image processing, including optics, electronics and software. The authors approach the subject in terms of industrial applications, elucidating such topics as illumination and camera calibration. Initial chapters concentrate on the latest hardware aspects, ranging from lenses and camera systems to camera-computer interfaces, with the software necessary discussed to an equal depth in later sections. These include digital image basics as well as image analysis and image processing. The book concludes with extended coverage of industrial applications in optics and electronics, backed by case studies and design strategies for the conception of complete machine vision systems. As a result, readers are not only able to understand the latest systems, but also to plan and evaluate this technology. With more than 500 images and tables to illustrate relevant principles and steps.
Pattern recognition, image processing and computer vision are closely linked areas which have seen enormous progress in the last fifty years. Their applications in our daily life, commerce and industry are growing even more rapidly than theoretical advances. Hence, the need for a new handbook in pattern recognition and computer vision every five or six years as envisioned in 1990 is fully justified and valid. The book consists of three parts: (1) Pattern recognition methods and applications; (2) Computer vision and image processing; and (3) Systems, architecture and technology. This book is intended to capture the major developments in pattern recognition and computer vision though it is impossible to cover all topics. The chapters are written by experts from many countries, fully reflecting the strong international research interests in the areas. This fifth edition will complement the previous four editions of the book. Contents:Pattern Recognition Methods and Applications:Syntactic Pattern Recognition: Paradigm Issues and Open Problems (Mariusz Flasiński)Deep Discriminative and Generative Models for Speech Pattern Recognition (Li Deng and Navdeep Jaitly)On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and An Example in Semi-supervised Learning (Marco Loog, Jesse H Krijthe, and Are C Jensen)Information Theoretic Clustering Using a k-Nearest Neighbors-based Divergence Measure (Vidar V Vikjord and Robert Jenssen)Pruning Trees in Random Forests for Minimizing Non Detection in Medical Imaging (Laurent Heutte, Caroline Petitjean, and Chesner Désir)Recent Advances on Optimum-path Forest for Data Classification: Supervised, Semi-supervised, and Unsupervised Learning (João Paulo Papa, Willian Paraguassu Amorim, Alexandre Xavier Falcão, and João Manuel R S Tavares)On Curvelet-based Texture Features for Pattern Classification (Ching-Chung Li and Wen-Chyi Lin)Computer Recognition and Evaluation of Coins (Bo-Yuan Feng, Ke Sun, Parmida Atighechian, and Ching Y Suen)Supervised and Unsupervised Feature Descriptors for Error-resilient Underwater Live Fish Recognition (Meng-Che Chuang, Jeng-Neng Hwang, and Kresimir Willimans)Model Adaptation for Personalized Music Emotion Recognition (Yi-Hsuan Yang, Ju-Chiang Wang, Yu-An Chen, and Homer H Chen)Computer Vision and Image Processing:Context Assisted Person Identification for Images and Videos (Liyan Zhang, Dmitri V Kalashnikov, and Sharad Mehrotra)Statistical Shape Spaces for 3D Data: A Review (Alan Brunton, Augusto Salazar, Timo Bolkart, and Stefanie Wuhrer)Tracking Without Appearance Descriptors (Mehrsan Javan Roshtkari and Martin D Levine)Knowledge Augmented Visual Learning (Ziheng Wang and Qiang Ji)Graph Edit Distance — Novel Approximation Algorithms (Kaspar Riesen and Horst Bunke)Latest Developments of LSTM Neural Networks with Applications of Document Image Analysis (Marcus Liwicki, Volkmar Frinken, and Muhammad Zeshan Afzal)Analyzing Remote Sensing Images with Hierarchial Morphological Representations (Gabriele Cavallaro, Mauro Dalla Mura and Jón Atli Benediktsson)Manifold-Based Sparse Representation for Hyperspectral Image Classification (Yuan Yan Tang and Haoliang Yuan)A Review of Texture Classification Methods and Their Applications in Medical Image Analysis of the Brain (Rouzbeh Maani, Sanjay Kalra, and Yee-Hong Yang)3D Tomosynthesis to Detect Breast Cancer (Yanbin Lu, Mina Yousefi, John Ellenberger, Richard H Moore, Daniel B Kopans, Adam Krzyżak, and Ching Y Suen)System, Architecture, and Technology:Combining Representations for Improved Sketch Recognition (Sonya Cates)Visual Object Recognition with Image Retrieval (Sedat Ozer)Efficient Identification of Faces in Video Streams Using Low-power Multi-core Devices (Donavan Prieur, Eric Granger, Yvon Savaria, and Claude Thibeault)Kernel-based Learning for Fault Detection and Identification in Fuel Cell Systems (Gabriele Moser, Paola Costamagna, Andrea De Giorgi, Lissy Pellaco, Andrea Trucco, and Sebastiano B Serpico)Outdoor Shadow Modelling and its Applications (Lin Gu and Antonio Robles-Kelly)Fast Structured Tracker with Improved Motion Model Using Robust Kalman Filter (Ivan Bogun and Eraldo Ribeiro)Using 3D Vision for Automated Industrial Inspection (David J Michael)Vision Challenges in Image-based Barcode Readers (Xianju Wang and Xiangyun (Mary) Ye)Parallel Pattern Matching Using the Automata Processor (Matt Tanner, Matt Grimm, and Harold B Noyes) Readership: Graduate students, academics, practitioners, researchers, computer scientists, electrical and medical engineers.Key Features:This book provides a comprehensive and concise account of major developments in pattern recognition and is written by leading experts in the fieldsThis book provides coverage of major applications in human identification, medical imaging, remote sensing, speech/ audio processing, and industrial machine visionThis book provides in-depth coverage of fundamental theory of pattern recognition and computer visionKeywords:Pattern Recognition/Image Analysis;Machine Perception/Computer Vision;Speech/Audio Recognition;Personal Identification;Remote Sensing Recognition;Industrial Vision;Medical Imaging and Recognition
With the demands of quality management and process control in an industrial environment machine vision is becoming an important issue. This handbook of machine vision is written by experts from leading companies in this field. It goes through all aspects of image acquisition and image processing. From the viewpoint of the industrial application the authors also elucidate in topics like illumination or camera calibration. Attention is paid to all hardware aspects, starting from lenses and camera systems to camera-computer interfaces. Besides the detailed hardware descriptions the necessary software is discussed with equal profoundness. This includes sections on digital image basics as well as image analysis and image processing. Finally the user is introduced to general aspects of industrial applications of machine vision, such as case studies and strategies for the conception of complete machine vision systems. With this handbook the reader will be enabled not only to understand up to date systems for machine vision but will also be qualified for the planning and evaluation of such technology.
CD-ROM files contain complete text of all three print vols., as well as hyperlinks to figures, tables, etc. and between the index and the text. Also included are hyperlinks to movies, interactive 3-D models, demonstration software and other materials not contained in the print version.
Both pattern recognition and computer vision have experienced rapid progress in the last twenty-five years. This book provides the latest advances on pattern recognition and computer vision along with their many applications. It features articles written by renowned leaders in the field while topics are presented in readable form to a wide range of readers. The book is divided into five parts: basic methods in pattern recognition, basic methods in computer vision and image processing, recognition applications, life science and human identification, and systems and technology.
Applications in Pattern Recognition, Computer Vision, Neuralcomputing, and Robotics
Author: Eduardo Bayro Corrochano
Publisher: Springer Science & Business Media
Many computer scientists, engineers, applied mathematicians, and physicists use geometry theory and geometric computing methods in the design of perception-action systems, intelligent autonomous systems, and man-machine interfaces. This handbook brings together the most recent advances in the application of geometric computing for building such systems, with contributions from leading experts in the important fields of neuroscience, neural networks, image processing, pattern recognition, computer vision, uncertainty in geometric computations, conformal computational geometry, computer graphics and visualization, medical imagery, geometry and robotics, and reaching and motion planning. For the first time, the various methods are presented in a comprehensive, unified manner. This handbook is highly recommended for postgraduate students and researchers working on applications such as automated learning; geometric and fuzzy reasoning; human-like artificial vision; tele-operation; space maneuvering; haptics; rescue robots; man-machine interfaces; tele-immersion; computer- and robotics-aided neurosurgery or orthopedics; the assembly and design of humanoids; and systems for metalevel reasoning.
With the ongoing release of 3D movies and the emergence of 3D TVs, 3D imaging technologies have penetrated our daily lives. Yet choosing from the numerous 3D vision methods available can be frustrating for scientists and engineers, especially without a comprehensive resource to consult. Filling this gap, Handbook of 3D Machine Vision: Optical Metrology and Imaging gives an extensive, in-depth look at the most popular 3D imaging techniques. It focuses on noninvasive, noncontact optical methods (optical metrology and imaging). The handbook begins with the well-studied method of stereo vision and explains how random speckle patterns or space-time varying patterns substantially improve the results of stereo vision. It then discusses stereo particle image velocimetry as a major experimental means in fluid dynamics, the robust and easy-to-implement structured-light technique for computer science applications, digital holography for performing micro- to nanoscale measurements, and grating, interferometry, and fringe projection techniques for precisely measuring dynamically deformable natural objects. The book goes on to describe techniques that do not require triangulation to recover a 3D shape, including time-of-flight techniques and uniaxial 3D shape measurement, as well as 3D measurement techniques that are not restricted to surface capture, such as 3D ultrasound, optical coherence tomography, and 3D endoscopy. The book also explores how novel 3D imaging techniques are being applied in the promising field of biometrics—which may prove essential to security and public safety. Written by key players in the field and inventors of important imaging technologies, this authoritative, state-of-the-art handbook helps you understand the core of 3D imaging technology and choose the proper 3D imaging technique for your needs. For each technique, the book provides its mathematical foundations, summarizes its successful applications, and discusses its limitations.
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.
Handbook of Pattern Recognition and Image Processing incorporates the significant advances achieved since the publication of Dr. Youngs highly successful first volume in 1986. Volume 2 emphasizes computervision and three-dimensional shapes-their representation, recovery, recognition, and extraction. Additional topics covered include stereo and robotic vision and motion analysis. All of the fifteen chapters are authored by leading researchers in pattern recognition. Key Features * Covers the methods for 3D shape recovery, including shape from shading, shape from edge and contours, range image analysis, and stereo vision * Presents analysis of 3D motion from an image sequence, including nonrigid motion and human movement * Provides coverage of representation, matching, and recognition of 3D objects
"This book investiges machine learning (ML), one of the most fruitful fields of current research, both in the proposal of new techniques and theoretic algorithms and in their application to real-life problems"--Provided by publisher.