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Proceedings Paper

Detection probability evaluation of an automated inspection system
Author(s): Wenyuan Xu; Steven P. Floeder
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Paper Abstract

Detection probability is an extremely important performance metric for Automated Inspection (AI) systems. Using the detection of false connection points in patterned images as an example, this paper presents a novel method to estimate the detection capability of an AI system. One is concerned with how wide of a false connection can be reliably detected by a given AI system. One possible approach for evaluating detection probability is to compare automatic detection results with the results from manual human inspection. Unfortunately, this method is tedious, time consuming, and inspector-dependent. Moreover, an inspector's tiredness or oversight easily results in missing detection. In this paper, the Modulation Transfer Function (MTF) is used to determine the functional resolution of the system and generate theoretical profiles around false connection defects. The algorithm used for detecting the defects contains an auto- thresholding method for binarization. The statistical properties of these thresholds can be derived from the on-line record of thresholds of the system and essentially determine the detection results. Based on the statistical properties of the thresholds and their bounds, as well as the shapes of theoretical profiles, the detection probability of the AI system is evaluated.

Paper Details

Date Published: 21 March 2000
PDF: 11 pages
Proc. SPIE 3966, Machine Vision Applications in Industrial Inspection VIII, (21 March 2000); doi: 10.1117/12.380067
Show Author Affiliations
Wenyuan Xu, 3M Co. (United States)
Steven P. Floeder, 3M Co. (United States)

Published in SPIE Proceedings Vol. 3966:
Machine Vision Applications in Industrial Inspection VIII
Kenneth W. Tobin Jr.; John C. Stover, Editor(s)

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