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Proceedings Paper

Experimental methodology for performance characterization of a line detection algorithm
Author(s): Tapas Kanungo; Mysore Y. Jaisimha; Robert M. Haralick; John Palmer
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Paper Abstract

We present a general methodology for designing experiments to quantitatively characterize lowlevel computer vision algorithms. The methodology can be applied to any vision problem that can be posed as a detection task. It provides a convenient framework to measure the sensitivity of an algorithm to various factors that affect the performance. The methodology is illustrated by applying it to a line detection algorithm consisting of the second directional derivative edge detector followed by a Hough transform. In particular we measure the selectivity of the algorithm in the presence of an interfering oriented grating and additive Gaussian noise. The final result is a measure of the detectors'' performance as a function of the orientation of the interfering grating.

Paper Details

Date Published: 1 March 1991
PDF: 9 pages
Proc. SPIE 1385, Optics, Illumination, and Image Sensing for Machine Vision V, (1 March 1991); doi: 10.1117/12.25352
Show Author Affiliations
Tapas Kanungo, Univ. of Washington (United States)
Mysore Y. Jaisimha, Univ. of Washington (United States)
Robert M. Haralick, Univ. of Washington (United States)
John Palmer, Univ. of Washington (United States)


Published in SPIE Proceedings Vol. 1385:
Optics, Illumination, and Image Sensing for Machine Vision V
Donald J. Svetkoff; Kevin G. Harding; Gordon T. Uber; Norman Wittels, Editor(s)

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