Share Email Print

Proceedings Paper

Generalized adaptive strategies for edge detection in digital imagery
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Edges in digital imagery can be identified from the zero- crossings of Laplacian of Gaussian (LOG) filtered images. Time or frequency-sampled LOG filters have been developed for the detection and localization of edges in digital image data. The image is decomposed into overlapping subblocks and processed in the transform domain. Adaptive algorithms are developed to minimize spurious edge classifications. In order to achieve accurate and efficient implementations, the discrete symmetric cosine transform of the input data is employed in conjunction with adaptive filters. The adaptive selection of the filter coefficients is based on the gradient criterion. For instance, in the case of the frequency-sampled LOG filter, the filter parameter is systemically varied to force the rejection of false or weak edges. In addition, the proposed algorithms easily extend to higher dimensions. This is useful where 3D medical image data containing edge information has been corrupted by noise. This paper employs isotropic and non-isotropic filters to track edges in such images.

Paper Details

Date Published: 1 October 1998
PDF: 12 pages
Proc. SPIE 3460, Applications of Digital Image Processing XXI, (1 October 1998); doi: 10.1117/12.323169
Show Author Affiliations
Ramakrishnan Sundaram, Information Systems Inc. (United States)

Published in SPIE Proceedings Vol. 3460:
Applications of Digital Image Processing XXI
Andrew G. Tescher, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?