Edge Detection Using Maximum Likelihood Estimate Of Change Point: The One-Dimensional Case

C. C. Li; M. Mazumdar; R. J. Chao

doi:10.1117/12.937865

18 May 1987 Edge Detection Using Maximum Likelihood Estimate Of Change Point: The One-Dimensional Case

C. C. Li, M. Mazumdar, R. J. Chao

Proceedings Volume 0730, Automated Inspection and Measurement; (1987) https://doi.org/10.1117/12.937865
Event: Cambridge Symposium_Intelligent Robotics Systems, 1986, Cambridge, MA, United States

Abstract

This paper presents a method of edge detection incorporating maximum likelihood estimates of intensity change points in the noisy digital data. The method is developed in the context of the line scan where a sliding window of a reasonable size is used, and is applicable to edge detection in 2-D images by scanning both horizontally and vertically. With the proper choice of detector parameters, i.e., contrast threshold across an edge and count threshold of each estimate, the method can provide an accurate determination of edge locations for dimensional measurement in automated inspection.

Citation Download Citation

C. C. Li, M. Mazumdar, and R. J. Chao "Edge Detection Using Maximum Likelihood Estimate Of Change Point: The One-Dimensional Case", Proc. SPIE 0730, Automated Inspection and Measurement, (18 May 1987); https://doi.org/10.1117/12.937865

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