Smoothed differentiation filters for images

Author

Meer, Peter ; Weiss, Isaac

Author_Institution

Center for Autom. Res., Maryland Univ., College Park, MD, USA

Volume

ii

fYear

1990

fDate

16-21 Jun 1990

Firstpage

121

Abstract

A systematic approach to least square approximation of images and of their derivatives is presented. Derivatives of any order can be obtained by convolving the image with a priori known filters. It is shown that if orthonormal polynomial bases are employed the filters have closed-form solutions. The same filter is obtained when the fitted polynomial functions have one consecutive degree. Moment-preserving properties, sparse structure for some of the filters, and the relationship to the Marr-Hildreth and Canny edge detectors are proven

Keywords

computer vision; filtering and prediction theory; least squares approximations; polynomials; Canny edge detectors; Marr-Hildreth edge detectors; closed-form solutions; computer vision; least square approximation; moment-preserving properties; orthonormal polynomial bases; smoothed differentiation filters; sparse structure; Automation; Chebyshev approximation; Computer vision; Educational institutions; Filters; Image edge detection; Image sampling; Lattices; Least squares methods; Polynomials;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1990. Proceedings., 10th International Conference on

Conference_Location

Atlantic City, NJ

Print_ISBN

0-8186-2062-5

Type

conf

DOI

10.1109/ICPR.1990.119341

Filename

119341