Optimal small kernels for edge detection

Stephen E Reichenbach, Stephen K. Park, Rachel Alter-Gartenberg

Research output: Contribution to journalConference article

4 Citations (Scopus)

Abstract

An algorithm is developed for defining small kernels that are conditioned on the important components of the imaging process: the nature of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and post-filter interpolation. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernal values that minimize the expected mean-square error of the estimate of the scene characteristic. This development is consistent with the derivation of the spatially unconstrained Wiener characteristic filter, but leads to a small, spatially constrained convolution kernel. Simulation experiments demonstrate that the algorithm is more flexible than traditional small-kernel techniques and yields more accurate estimates.

Original languageEnglish (US)
Pages (from-to)57-63
Number of pages7
JournalProceedings - International Conference on Pattern Recognition
Volume2
StatePublished - Dec 1 1990
EventProceedings of the 10th International Conference on Pattern Recognition - Atlantic City, NJ, USA
Duration: Jun 16 1990Jun 21 1990

Fingerprint

Edge detection
Optical transfer function
Convolution
Mean square error
Interpolation
Sampling
Imaging techniques
Experiments

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Optimal small kernels for edge detection. / Reichenbach, Stephen E; Park, Stephen K.; Alter-Gartenberg, Rachel.

In: Proceedings - International Conference on Pattern Recognition, Vol. 2, 01.12.1990, p. 57-63.

Research output: Contribution to journalConference article

Reichenbach, Stephen E ; Park, Stephen K. ; Alter-Gartenberg, Rachel. / Optimal small kernels for edge detection. In: Proceedings - International Conference on Pattern Recognition. 1990 ; Vol. 2. pp. 57-63.
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