Structured Computer Vision
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Author | : Sebastian Nowozin |
Publisher | : Now Publishers Inc |
Total Pages | : 195 |
Release | : 2011 |
Genre | : Computers |
ISBN | : 1601984561 |
Structured Learning and Prediction in Computer Vision introduces the reader to the most popular classes of structured models in computer vision.
Author | : Steven Tanimoto |
Publisher | : |
Total Pages | : 256 |
Release | : 1980 |
Genre | : Technology & Engineering |
ISBN | : |
Author | : S Tanimoto |
Publisher | : Elsevier |
Total Pages | : 247 |
Release | : 2014-08-05 |
Genre | : Technology & Engineering |
ISBN | : 0323159265 |
Structured Computer Vision
Author | : |
Publisher | : |
Total Pages | : 573 |
Release | : 1996 |
Genre | : Computer organization |
ISBN | : |
Author | : Simon J. D. Prince |
Publisher | : Cambridge University Press |
Total Pages | : 599 |
Release | : 2012-06-18 |
Genre | : Computers |
ISBN | : 1107011795 |
A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms.
Author | : Dongming Feng |
Publisher | : John Wiley & Sons |
Total Pages | : 260 |
Release | : 2021-01-11 |
Genre | : Science |
ISBN | : 1119566584 |
Provides comprehensive coverage of theory and hands-on implementation of computer vision-based sensors for structural health monitoring This book is the first to fill the gap between scientific research of computer vision and its practical applications for structural health monitoring (SHM). It provides a complete, state-of-the-art review of the collective experience that the SHM community has gained in recent years. It also extensively explores the potentials of the vision sensor as a fast and cost-effective tool for solving SHM problems based on both time and frequency domain analytics, broadening the application of emerging computer vision sensor technology in not only scientific research but also engineering practice. Computer Vision for Structural Dynamics and Health Monitoring presents fundamental knowledge, important issues, and practical techniques critical to successful development of vision-based sensors in detail, including robustness of template matching techniques for tracking targets; coordinate conversion methods for determining calibration factors to convert image pixel displacements to physical displacements; sensing by tracking artificial targets vs. natural targets; measurements in real time vs. by post-processing; and field measurement error sources and mitigation methods. The book also features a wide range of tests conducted in both controlled laboratory and complex field environments in order to evaluate the sensor accuracy and demonstrate the unique features and merits of computer vision-based structural displacement measurement. Offers comprehensive understanding of the principles and applications of computer vision for structural dynamics and health monitoring Helps broaden the application of the emerging computer vision sensor technology from scientific research to engineering practice such as field condition assessment of civil engineering structures and infrastructure systems Includes a wide range of laboratory and field testing examples, as well as practical techniques for field application Provides MATLAB code for most of the issues discussed including that of image processing, structural dynamics, and SHM applications Computer Vision for Structural Dynamics and Health Monitoring is ideal for graduate students, researchers, and practicing engineers who are interested in learning about this emerging sensor technology and advancing their applications in SHM and other engineering problems. It will also benefit those in civil and aerospace engineering, energy, and computer science.
Author | : Guanghui Wang |
Publisher | : Springer Science & Business Media |
Total Pages | : 219 |
Release | : 2010-10-20 |
Genre | : Computers |
ISBN | : 0857290460 |
The problem of structure and motion recovery from image sequences is an important theme in computer vision. Considerable progress has been made in this field during the past two decades, resulting in successful applications in robot navigation, augmented reality, industrial inspection, medical image analysis, and digital entertainment, among other areas. However, many of these methods work only for rigid objects and static scenes. The study of non-rigid structure from motion is not only of academic significance, but also has important practical applications in real-world, nonrigid or dynamic scenarios, such as human facial expressions and moving vehicles. This practical guide/reference provides a comprehensive overview of Euclidean structure and motion recovery, with a specific focus on factorization-based algorithms. The book discusses the latest research in this field, including the extension of the factorization algorithm to recover the structure of non-rigid objects, and presents some new algorithms developed by the authors. Readers require no significant knowledge of computer vision, although some background on projective geometry and matrix computation would be beneficial. Topics and features: presents the first systematic study of structure and motion recovery of both rigid and non-rigid objects from images sequences; discusses in depth the theory, techniques, and applications of rigid and non-rigid factorization methods in three dimensional computer vision; examines numerous factorization algorithms, covering affine, perspective and quasi-perspective projection models; provides appendices describing the mathematical principles behind projective geometry, matrix decomposition, least squares, and nonlinear estimation techniques; includes chapter-ending review questions, and a glossary of terms used in the book. This unique text offers practical guidance in real applications and implementations of 3D modeling systems for practitioners in computer vision and pattern recognition, as well as serving as an invaluable source of new algorithms and methodologies for structure and motion recovery for graduate students and researchers.
Author | : Linda G. Shapiro |
Publisher | : Pearson |
Total Pages | : 628 |
Release | : 2001 |
Genre | : Biography & Autobiography |
ISBN | : |
For upper level courses in Computer Vision and Image Analysis.Provides necessary theory and examples for students and practitioners who will work in fields where significant information must be extracted automatically from images. Appropriate for those interested in multimedia, art and design, geographic information systems, and image databases, in addition to the traditional areas of automation, image science, medical imaging, remote sensing and computer cartography. The text provides a basic set of fundamental concepts and algorithms for analyzing images, and discusses some of the exciting evolving application areas of computer vision.
Author | : Richard J. Radke |
Publisher | : Cambridge University Press |
Total Pages | : 409 |
Release | : 2013 |
Genre | : Business & Economics |
ISBN | : 0521766877 |
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.
Author | : Ioannis Brilakis |
Publisher | : Butterworth-Heinemann |
Total Pages | : 410 |
Release | : 2019-11-28 |
Genre | : Technology & Engineering |
ISBN | : 0128172584 |
Infrastructure Computer Vision delves into this field of computer science that works on enabling computers to see, identify, process images and provide appropriate output in the same way that human vision does. However, implementing these advanced information and sensing technologies is difficult for many engineers. This book provides civil engineers with the technical detail of this advanced technology and how to apply it to their individual projects. - Explains how to best capture raw geometrical and visual data from infrastructure scenes and assess their quality - Offers valuable insights on how to convert the raw data into actionable information and knowledge stored in Digital Twins - Bridges the gap between the theoretical aspects and real-life applications of computer vision