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(R). 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 wellas 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.
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