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Proceedings Paper

Corner detection for identification of man-made objects in noisy aerial images
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Paper Abstract

Corner detection is an essential feature extraction step in many image understanding applications including aerial image analysis and manufactured part inspection. Available corner detectors require the user to set critical manual thresholds, degrade under significant noise levels, or introduce high computational complexity. We present a nonlinear corner detection algorithm that does not require prior image information or any threshold to be set by the user. It provides 100% correct corner detection and fewer than 1 false positive corner per image when the contrast to noise ratio of the image is 6 or more, under Gaussian white noise.

Paper Details

Date Published: 25 July 2002
PDF: 6 pages
Proc. SPIE 4726, Automatic Target Recognition XII, (25 July 2002); doi: 10.1117/12.477038
Show Author Affiliations
Isaac N. Bankman, Johns Hopkins Univ. (United States)
Eric W. Rogala, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 4726:
Automatic Target Recognition XII
Firooz A. Sadjadi, Editor(s)

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