Computer And Machine Vision
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Computer And Machine Vision
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Author : E. R. Davies
language : en
Publisher: Academic Press
Release Date : 2012-03-05
Computer And Machine Vision written by E. R. Davies and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-05 with Computers categories.
Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fourth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date tutorial text suitable for graduate students, researchers and R&D engineers working in this vibrant subject. Key features include: Practical examples and case studies give the 'ins and outs' of developing real-world vision systems, giving engineers the realities of implementing the principles in practice. New chapters containing case studies on surveillance and driver assistance systems give practical methods on these cutting-edge applications in computer vision. Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples. Updated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imaging. The 'recent developments' section now included in each chapter will be useful in bringing students and practitioners up to date with the subject. Roy Davies is Emeritus Professor of Machine Vision at Royal Holloway, University of London. He has worked on many aspects of vision, from feature detection to robust, real-time implementations of practical vision tasks. His interests include automated visual inspection, surveillance, vehicle guidance and crime detection. He has published more than 200 papers, and three books - Machine Vision: Theory, Algorithms, Practicalities (1990), Electronics, Noise and Signal Recovery (1993), and Image Processing for the Food Industry (2000); the first of these has been widely used internationally for more than 20 years, and is now out in this much enhanced fourth edition. Roy holds a DSc at the University of London, and has been awarded Distinguished Fellow of the British Machine Vision Association, and Fellow of the International Association of Pattern Recognition.
Computer And Machine Vision
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Author : E. R. Davies
language : en
Publisher: Academic Press
Release Date : 2012-04-18
Computer And Machine Vision written by E. R. Davies and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-04-18 with Technology & Engineering categories.
Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fourth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date tutorial text suitable for graduate students, researchers and R&D engineers working in this vibrant subject. Key features include: - Practical examples and case studies give the 'ins and outs' of developing real-world vision systems, giving engineers the realities of implementing the principles in practice - New chapters containing case studies on surveillance and driver assistance systems give practical methods on these cutting-edge applications in computer vision - Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples - Updated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imaging - The 'recent developments' section now included in each chapter will be useful in bringing students and practitioners up to date with the subject - Mathematics and essential theory are made approachable by careful explanations and well-illustrated examples - Updated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imaging - The 'recent developments' section now included in each chapter will be useful in bringing students and practitioners up to date with the subject
Machine Vision
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Author : E. R. Davies
language : en
Publisher: Elsevier
Release Date : 2004-12-22
Machine Vision written by E. R. Davies and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-12-22 with Computers categories.
In the last 40 years, machine vision has evolved into a mature field embracing a wide range of applications including surveillance, automated inspection, robot assembly, vehicle guidance, traffic monitoring and control, signature verification, biometric measurement, and analysis of remotely sensed images. While researchers and industry specialists continue to document their work in this area, it has become increasingly difficult for professionals and graduate students to understand the essential theory and practicalities well enough to design their own algorithms and systems. This book directly addresses this need.As in earlier editions, E.R. Davies clearly and systematically presents the basic concepts of the field in highly accessible prose and images, covering essential elements of the theory while emphasizing algorithmic and practical design constraints. In this thoroughly updated edition, he divides the material into horizontal levels of a complete machine vision system. Application case studies demonstrate specific techniques and illustrate key constraints for designing real-world machine vision systems.· Includes solid, accessible coverage of 2-D and 3-D scene analysis.· Offers thorough treatment of the Hough Transform—a key technique for inspection and surveillance.· Brings vital topics and techniques together in an integrated system design approach.· Takes full account of the requirement for real-time processing in real applications.
Machine Vision Algorithms And Applications
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Author : Carsten Steger
language : en
Publisher: John Wiley & Sons
Release Date : 2018-03-12
Machine Vision Algorithms And Applications written by Carsten Steger and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-12 with Science categories.
Die zweite Auflage dieses erfolgreichen Lehrbuchs zum maschinellen Sehen ist vollständig aktualisiert, überarbeitet und erweitert, um die Entwicklungen der vergangenen Jahre auf den Gebieten der Bilderfassung, Algorithmen des maschinellen Sehens und dessen Anwendungen zu berücksichtigen. Hinzugekommen sind insbesondere neue Kameratechniken und Schnittstellen, 3D-Sensorik und -technologie, 3D-Objekterkennung und 3D-Bildrekonstruktion. Die Autoren folgen weiterhin dem Ansatz "soviel Theorie wie nötig, soviel Anwendungsbezug wie möglich". Alle Beispiele basieren auf der aktuellen Version der Software HALCON, von der nach Registrierung auf der Autorenwebseite eine Testversion erhältlich ist.
Challenges And Applications For Implementing Machine Learning In Computer Vision
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Author : Kashyap, Ramgopal
language : en
Publisher: IGI Global
Release Date : 2019-10-04
Challenges And Applications For Implementing Machine Learning In Computer Vision written by Kashyap, Ramgopal and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-04 with Computers categories.
Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.
Machine Learning For Computer Vision
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Author : Roberto Cipolla
language : en
Publisher: Springer
Release Date : 2012-07-27
Machine Learning For Computer Vision written by Roberto Cipolla and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-27 with Technology & Engineering categories.
Computer vision is the science and technology of making machines that see. It is concerned with the theory, design and implementation of algorithms that can automatically process visual data to recognize objects, track and recover their shape and spatial layout. The International Computer Vision Summer School - ICVSS was established in 2007 to provide both an objective and clear overview and an in-depth analysis of the state-of-the-art research in Computer Vision. The courses are delivered by world renowned experts in the field, from both academia and industry, and cover both theoretical and practical aspects of real Computer Vision problems. The school is organized every year by University of Cambridge (Computer Vision and Robotics Group) and University of Catania (Image Processing Lab). Different topics are covered each year. A summary of the past Computer Vision Summer Schools can be found at: http://www.dmi.unict.it/icvss This edited volume contains a selection of articles covering some of the talks and tutorials held during the last editions of the school. The chapters provide an in-depth overview of challenging areas with key references to the existing literature.
Practical Machine Learning For Computer Vision
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Author : Valliappa Lakshmanan
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-07-21
Practical Machine Learning For Computer Vision written by Valliappa Lakshmanan and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-21 with Computers categories.
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
Advances In Machine Vision
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Author : Colin Archibald
language : en
Publisher: World Scientific
Release Date : 1992
Advances In Machine Vision written by Colin Archibald and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Computers categories.
This book describes recent strategies and applications for extracting useful information from sensor data. For example, the methods presented by Roth and Levine are becoming widely accepted as the ?best? way to segment range images, and the neural network methods for Alpha-numeric character recognition, presented by K Yamada, are believed to be the best yet presented. An applied system to analyze the images of dental imprints presented by J Ct, et al. is one of several examples of image processing systems that have already been proven to be practical, and can serve as a model for the image processing system designer. Important aspects of the automation of processes are presented in a practical way which can provide immediate new capabilities in fields as diverse as biomedical image processing, document processing, industrial automation, understanding human perception, and the defence industries. The book is organized into sections describing Model Driven Feature Extraction, Data Driven Feature Extraction, Neural Networks, Model Building, and Applications.
Machine Learning In Computer Vision
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Author : Nicu Sebe
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-10-04
Machine Learning In Computer Vision written by Nicu Sebe and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-10-04 with Computers categories.
The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system. In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.