DocumentCode
2238730
Title
Quadratic filter and feature detection
Author
Chou, Kae-Jy ; Schunck, Brian G.
Author_Institution
Dept. of Electr. Eng. & Comput. Sci., Michigan Univ., Ann Arbor, MI, USA
fYear
1993
fDate
15-17 Jun 1993
Firstpage
692
Lastpage
693
Abstract
The quadratic filter for low-level vision applications is explored. The quadratic filter is the simplest nonlinear time-invariant filter and corresponds to the second term in the Volterra expansion. Concepts behind the quadratic filter are elaborated, and it is shown that it can be derived from fundamental properties of regions and the principle of model competition. Two properties of the filter are presented. It has better spatial localization than a linear filter, and it is insensitive to blurred boundaries
Keywords
computer vision; edge detection; feature extraction; image segmentation; nonlinear filters; Volterra expansion; feature detection; low-level vision; model competition; nonlinear time-invariant filter; quadratic filter; spatial localization; Application software; Artificial intelligence; Computer vision; Equations; Image edge detection; Image segmentation; Kernel; Laboratories; Nonlinear filters; Pixel;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition, 1993. Proceedings CVPR '93., 1993 IEEE Computer Society Conference on
Conference_Location
New York, NY
ISSN
1063-6919
Print_ISBN
0-8186-3880-X
Type
conf
DOI
10.1109/CVPR.1993.341024
Filename
341024
Link To Document