Title :
Smoothed differentiation filters for images
Author :
Meer, Peter ; Weiss, Isaac
Author_Institution :
Center for Autom. Res., Maryland Univ., College Park, MD, USA
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;
Conference_Titel :
Pattern Recognition, 1990. Proceedings., 10th International Conference on
Conference_Location :
Atlantic City, NJ
Print_ISBN :
0-8186-2062-5
DOI :
10.1109/ICPR.1990.119341
