DocumentCode
2466048
Title
Edge recognition in dynamic vision
Author
McIvor, Alan M.
Author_Institution
Robotics Res. Group, Oxford Univ., UK
fYear
1989
fDate
4-8 Jun 1989
Firstpage
118
Lastpage
123
Abstract
Using a model of an edge´s motion through a sequence of images, the problem of its localization can be formulated as a stochastic filtering problem. The extended Kalman filter for such a system is considered in detail and is shown to be interpretable as a sequence of oriented special convolutions. Results are presented which show that the edge localization obtained using this filter is substantially better than that obtained using either the Sobel or Canny edge operators on each image individually
Keywords
Kalman filters; pattern recognition; picture processing; Kalman filter; convolutions; dynamic vision; edge localization; edge recognition; pattern recognition; picture processing; stochastic filtering; Convolution; Filtering theory; Image edge detection; Machine vision; Nonlinear filters; Pixel; Robots; Signal to noise ratio; Stochastic processes; Stochastic resonance;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition, 1989. Proceedings CVPR '89., IEEE Computer Society Conference on
Conference_Location
San Diego, CA
ISSN
1063-6919
Print_ISBN
0-8186-1952-x
Type
conf
DOI
10.1109/CVPR.1989.37838
Filename
37838
Link To Document