3d Computer Vision
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3D Computer Vision
Author: Christian Wöhler
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-07-23
This indispensable text introduces the foundations of three-dimensional computer vision and describes recent contributions to the field. Fully revised and updated, this much-anticipated new edition reviews a range of triangulation-based methods, including linear and bundle adjustment based approaches to scene reconstruction and camera calibration, stereo vision, point cloud segmentation, and pose estimation of rigid, articulated, and flexible objects. Also covered are intensity-based techniques that evaluate the pixel grey values in the image to infer three-dimensional scene structure, and point spread function based approaches that exploit the effect of the optical system. The text shows how methods which integrate these concepts are able to increase reconstruction accuracy and robustness, describing applications in industrial quality inspection and metrology, human-robot interaction, and remote sensing.
An Introduction to 3D Computer Vision Techniques and Algorithms
Author: Boguslaw Cyganek
language: en
Publisher: John Wiley & Sons
Release Date: 2011-08-10
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.
3D Computer Vision
This book offers a comprehensive and unbiased introduction to 3D Computer Vision, ranging from its foundations and essential principles to advanced methodologies and technologies. Divided into 11 chapters, it covers the main workflow of 3D computer vision as follows: camera imaging and calibration models; various modes and means of 3D image acquisition; binocular, trinocular and multi-ocular stereo vision matching techniques; monocular single-image and multi-image scene restoration methods; point cloud data processing and modeling; simultaneous location and mapping; generalized image and scene matching; and understanding spatial-temporal behavior. Each topic is addressed in a uniform manner: the dedicated chapter first covers the essential concepts and basic principles before presenting a selection of typical, specific methods and practical techniques. In turn, it introduces readers to the most important recent developments, especially in the last three years. This approachallows them to quickly familiarize themselves with the subject, implement the techniques discussed, and design or improve their own methods for specific applications. The book can be used as a textbook for graduate courses in computer science, computer engineering, electrical engineering, data science, and related subjects. It also offers a valuable reference guide for researchers and practitioners alike.