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Photogrammetric Computer Vision

Photogrammetric Computer Vision Author Wolfgang Förstner
ISBN-10 9783319115504
Release 2016-10-04
Pages 816
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This textbook offers a statistical view on the geometry of multiple view analysis, required for camera calibration and orientation and for geometric scene reconstruction based on geometric image features. The authors have backgrounds in geodesy and also long experience with development and research in computer vision, and this is the first book to present a joint approach from the converging fields of photogrammetry and computer vision. Part I of the book provides an introduction to estimation theory, covering aspects such as Bayesian estimation, variance components, and sequential estimation, with a focus on the statistically sound diagnostics of estimation results essential in vision metrology. Part II provides tools for 2D and 3D geometric reasoning using projective geometry. This includes oriented projective geometry and tools for statistically optimal estimation and test of geometric entities and transformations and their relations, tools that are useful also in the context of uncertain reasoning in point clouds. Part III is devoted to modelling the geometry of single and multiple cameras, addressing calibration and orientation, including statistical evaluation and reconstruction of corresponding scene features and surfaces based on geometric image features. The authors provide algorithms for various geometric computation problems in vision metrology, together with mathematical justifications and statistical analysis, thus enabling thorough evaluations. The chapters are self-contained with numerous figures and exercises, and they are supported by an appendix that explains the basic mathematical notation and a detailed index. The book can serve as the basis for undergraduate and graduate courses in photogrammetry, computer vision, and computer graphics. It is also appropriate for researchers, engineers, and software developers in the photogrammetry and GIS industries, particularly those engaged with statistically based geometric computer vision methods.



Photogrammetric Computer Vision

Photogrammetric Computer Vision Author Wolfgang Förstner
ISBN-10 3319115510
Release 2016-05-09
Pages
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This book is about concepts and methods for developing software for automatically analyzing images, with a focus on main application areas, mapping and image-based metrology. The first part of the book introduces the related statistics and estimation theory required, including probability theory, testing and estimation. The second part of the book explains all aspects of the related geometry, with chapters on homogeneous representations of points, lines and planes; transformations; geometric operations; rotations; oriented projective geometry; and reasoning with uncertain geometric entities. The third part of the book explains orientation and reconstruction. The book is suitable for advanced undergraduate and graduate students, practitioners, and researchers in computer vision.



Multiple View Geometry in Computer Vision

Multiple View Geometry in Computer Vision Author Richard Hartley
ISBN-10 9781139449144
Release 2004-03-25
Pages
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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 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.



Photogrammetry

Photogrammetry Author Karl Kraus
ISBN-10 9783110892871
Release 2007-01-01
Pages 474
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This textbook deals with the basics and methods of photogrammetry and laser scanning which are used to determine the form and location of objects, with measurements provided by sensors placed in air planes as well as on terrestrial platforms. Many examples and exercises with solutions are included. Photogrammetry, Laserscanning.



Reconstruction and Analysis of 3D Scenes

Reconstruction and Analysis of 3D Scenes Author Martin Weinmann
ISBN-10 9783319292465
Release 2016-03-17
Pages 233
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This unique work presents a detailed review of the processing and analysis of 3D point clouds. A fully automated framework is introduced, incorporating each aspect of a typical end-to-end processing workflow, from raw 3D point cloud data to semantic objects in the scene. For each of these components, the book describes the theoretical background, and compares the performance of the proposed approaches to that of current state-of-the-art techniques. Topics and features: reviews techniques for the acquisition of 3D point cloud data and for point quality assessment; explains the fundamental concepts for extracting features from 2D imagery and 3D point cloud data; proposes an original approach to keypoint-based point cloud registration; discusses the enrichment of 3D point clouds by additional information acquired with a thermal camera, and describes a new method for thermal 3D mapping; presents a novel framework for 3D scene analysis.



Guide to 3D Vision Computation

Guide to 3D Vision Computation Author Kenichi Kanatani
ISBN-10 9783319484938
Release 2016-12-09
Pages 321
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This classroom-tested and easy-to-understand textbook/reference describes the state of the art in 3D reconstruction from multiple images, taking into consideration all aspects of programming and implementation. Unlike other computer vision textbooks, this guide takes a unique approach in which the initial focus is on practical application and the procedures necessary to actually build a computer vision system. The theoretical background is then briefly explained afterwards, highlighting how one can quickly and simply obtain the desired result without knowing the derivation of the mathematical detail. Features: reviews the fundamental algorithms underlying computer vision; describes the latest techniques for 3D reconstruction from multiple images; summarizes the mathematical theory behind statistical error analysis for general geometric estimation problems; presents derivations at the end of each chapter, with solutions supplied at the end of the book; provides additional material at an associated website.



Close Range Photogrammetry and 3D Imaging

Close Range Photogrammetry and 3D Imaging Author Thomas Luhmann
ISBN-10 9783110302783
Release 2014-01-01
Pages 702
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This is the second edition of the well-known guide to close-range photogrammetry. It provides a thorough presentation of the methods, mathematics, systems and applications which comprise the subject of close-range photogrammetry, which uses accurate imaging techniques to analyse the three-dimensional shape of a wide range of manufactured and natural objects.



Introduction to modern photogrammetry

Introduction to modern photogrammetry Author Edward M. Mikhail
ISBN-10 0471309249
Release 2001
Pages 479
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This book is designed to give the reader a strong grounding in the mathematical basis of photogrammetry while introducing them to the related fields, such as remote sensing and digital image processing, which are increasingly important to photogrammetric research and practice.



Theory and Applications of Image Registration

Theory and Applications of Image Registration Author Arthur Ardeshir Goshtasby
ISBN-10 9781119171737
Release 2017-07-03
Pages 520
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A hands-on guide to image registration theory and methods—with examples of a wide range of real-world applications Theory and Applications of Image Registration offers comprehensive coverage of feature-based image registration methods. It provides in-depth exploration of an array of fundamental issues, including image orientation detection, similarity measures, feature extraction methods, and elastic transformation functions. Also covered are robust parameter estimation, validation methods, multi-temporal and multi-modality image registration, methods for determining the orientation of an image, methods for identifying locally unique neighborhoods in an image, methods for detecting lines in an image, methods for finding corresponding points and corresponding lines in images, registration of video images to create panoramas, and much more. Theory and Applications of Image Registration provides readers with a practical guide to the theory and underpinning principles. Throughout the book numerous real-world examples are given, illustrating how image registration can be applied to problems in various fields, including biomedicine, remote sensing, and computer vision. Also provided are software routines to help readers develop their image registration skills. Many of the algorithms described in the book have been implemented, and the software packages are made available to the readers of the book on a companion website. In addition, the book: Explores the fundamentals of image registration and provides a comprehensive look at its multi-disciplinary applications Reviews real-world applications of image registration in the fields of biomedical imaging, remote sensing, computer vision, and more Discusses methods in the registration of long videos in target tracking and 3-D reconstruction Addresses key research topics and explores potential solutions to a number of open problems in image registration Includes a companion website featuring fully implemented algorithms and image registration software for hands-on learning Theory and Applications of Image Registration is a valuable resource for researchers and professionals working in industry and government agencies where image registration techniques are routinely employed. It is also an excellent supplementary text for graduate students in computer science, electrical engineering, software engineering, and medical physics.



Computer Vision Metrics

Computer Vision Metrics Author Scott Krig
ISBN-10 9783319337623
Release 2016-09-16
Pages 637
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Based on the successful 2014 book published by Apress, this textbook edition is expanded to provide a comprehensive history and state-of-the-art survey for fundamental computer vision methods and deep learning. With over 800 essential references, as well as chapter-by-chapter learning assignments, both students and researchers can dig deeper into core computer vision topics and deep learning architectures. The survey covers everything from feature descriptors, regional and global feature metrics, feature learning architectures, deep learning, neuroscience of vision, neural networks, and detailed example architectures to illustrate computer vision hardware and software optimization methods. To complement the survey, the textbook includes useful analyses which provide insight into the goals of various methods, why they work, and how they may be optimized. The text delivers an essential survey and a valuable taxonomy, thus providing a key learning tool for students, researchers and engineers, to supplement the many effective hands-on resources and open source projects, such as OpenCV and other imaging and deep learning tools.



Digital Photogrammetry

Digital Photogrammetry Author Wilfried Linder
ISBN-10 9783540927259
Release 2009-01-23
Pages 220
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1.1 Basic idea and main task of photogrammetry If you want to measure the size of an object, let’s say the length, width and height of a house, then normally you will carry this out directly at the object. Now imagine that the house didn’t exist anymore – it was destroyed, but some historic photos exist. Then, if you can determine the scale of the photos, it must be pos- ble to get the desired data. Of course you can use photos to get information about objects. This kind of - formation is different: So, for example, you may receive qualitative data (the house seems to be old, the walls are coloured light yellow) from photo interpre- tion, or quantitative data like mentioned before (the house has a base size of 8 by 6 meters) from photo measurement, or information in addition to your background knowledge (the house has elements of the “art nouveau” style, so may be c- th structed at the beginning of the 20 century), and so on.



Programming Computer Vision with Python

Programming Computer Vision with Python Author Jan Erik Solem
ISBN-10 9781449341930
Release 2012-06-19
Pages 264
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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



Computer Vision

Computer Vision Author Richard Szeliski
ISBN-10 1848829345
Release 2010-10-19
Pages 812
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Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? 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.



Computer Vision

Computer Vision Author David A. Forsyth
ISBN-10 0273764144
Release 2012
Pages 791
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Appropriate for upper-division undergraduate and graduate level courses in computer vision found in departments of computer science, computer engineering and electrical engineering, this book offers a treatment of modern computer vision methods.



Elements of Photogrammetry

Elements of Photogrammetry Author Paul R. Wolf
ISBN-10 0072924543
Release 2000-01-01
Pages 608
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The first new edition in 13 years incorporates recent changes on the subject of streamlining from advances in computers. Their ever increasing speed and storage capabilities have directly led to an entire new approach in photogrammetric mapping known as “Soft-Copy†photogrammetry. Digital Imaging systems, including those used in modern satellite programs, scanners for digitizing photographic images, and digital image processing techniques are new topics to be covered that are fundamental to soft copy photogrammetry.



Multi View Stereo

Multi View Stereo Author Yasutaka Furukawa
ISBN-10 1601988362
Release 2015-06-25
Pages 166
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Presents a hands-on view of the field of multi-view stereo with a focus on practical algorithms. It frames the multiview stereo problem as an image/geometry consistency optimization problem and describesits main two ingredients: robust implementations of photometric consistency measures and efficient optimization algorithms.



Pattern Recognition in Practice II

Pattern Recognition in Practice II Author L.N. Kanal
ISBN-10 9780444599223
Release 2012-12-02
Pages 589
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The 1985 Amsterdam conference brought together researchers active in pattern recognition methodology and the development of practical applications. The first part of the book covers various methodological aspects of image processing, knowledge based and model driven image understanding systems, 3-D reconstruction methods, and application oriented papers. Part II deals with aspects of statistical pattern recognition, the problem of population classification, and topics common to both pattern recognition and artificial intelligence.