Mathematical methods in computer vision
Author(s)
Bibliographic Information
Mathematical methods in computer vision
（The IMA volumes in mathematics and its applications, v. 133）
Springer, c2003
Available at / 17 libraries
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Library, Research Institute for Mathematical Sciences, Kyoto University数研
C-P||Minneapolis||2000.10-1103054497
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Note
Includes bibliographical references and index
"This volume comprises some of the key work presented at the IMA Workshops on Computer Vision during fall of 2000. ... The first Workshop was devoted to "Image Processing and Low Level Vision" (September 2000) ... the second Workshop was "High-Level Vision." This took place at the IMA in November 2000." - pref.
Description and Table of Contents
Description
This volume comprises some of the key work presented at two IMA
Workshops on Computer Vision during fall of 2000. Recent years have
seen significant advances in the application of sophisticated
mathematical theories to the problems arising in image processing.
Basic issues include image smoothing and denoising, image enhancement,
morphology, image compression, and segmentation (determining
boundaries of objects-including problems of camera distortion and
partial occlusion). Several mathematical approaches have emerged,
including methods based on nonlinear partial differential equations,
stochastic and statistical methods, and signal processing techniques,
including wavelets and other transform theories.
Shape theory is of fundamental importance since it is the bottleneck
between high and low level vision, and formed the bridge between the
two workshops on vision. The recent geometric partial differential
equation methods have been essential in throwing new light on this
very difficult problem area. Further, stochastic processes, including
Markov random fields, have been used in a Bayesian framework to
incorporate prior constraints on smoothness and the regularities of
discontinuities into algorithms for image restoration and
reconstruction.
A number of applications are considered including optical character
and handwriting recognizers, printed-circuit board inspection systems
and quality control devices, motion detection, robotic control by
visual feedback, reconstruction of objects from stereoscopic view
and/or motion, autonomous road vehicles, and many others.
Table of Contents
A large deviation theory analysis of Bayesian tree search * Expectation-based, multi-focal, saccadic vision * Statistical shape analysis in high-level vision * Maximal entropy for reconstruction of back projection images * On the Monge-Kantorovich problem and image warping * Analysis and synthesis of visual images in the brain: evidence for pattern theory * Nonlinear diffusions and optimal estimation * The Mumford-Shah functional: from segmentation to stereo
by "Nielsen BookData"
