Könyv Mathematical Methods in Computer Vision Peter J. Olver

Mathematical Methods in Computer Vision

Nyelv: Angol
Kötés: Kemény kötésű
Elérhetőség: Beszállítói készleten alacsony példányszámban
Küldés 13-18 napon belül
40 590 Ft
This volume comprises some of the key work presented at two IMA §Workshops on Computer Vision during...

Információk a könyvről

Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2003
oldal
156
EAN
9780387004976
ISBN
0387004971
Enbook ID
01380712
Súly
412
Méretek
156 x 234 x 11

Teljes leírás

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.

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