Generalized adaptive strategies for edge detection in digital imagery

Ramakrishnan Sundaram

doi:10.1117/12.323169

1 October 1998 Generalized adaptive strategies for edge detection in digital imagery

Ramakrishnan Sundaram

Proceedings Volume 3460, Applications of Digital Image Processing XXI; (1998) https://doi.org/10.1117/12.323169
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

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.

Citation Download Citation

Ramakrishnan Sundaram "Generalized adaptive strategies for edge detection in digital imagery", Proc. SPIE 3460, Applications of Digital Image Processing XXI, (1 October 1998); https://doi.org/10.1117/12.323169

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KEYWORDS

Electronic filtering

Digital filtering

Edge detection

Image filtering

Optical filters

Gaussian filters

Signal to noise ratio

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