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Journal of Inforamtion Science and Engineering, Vol.5 No.2, pp.143-155 (April 1989)
Statistical Detection and Estimation of Signal
Changes with Applications to Image Processing

Jun S. Huang and Dong H. Tseng
Institute of Information Science
Academia Sinica
Taipei, Taiwan, Republic of China

Given a static or dynamic series of noise corrupted observations {Xt, t is time}, the main problem is the detection of whether some meaningful parameters associated with {Xt} have changed, and if these parameters have changed, then estimate the time of the change and the size of the change. A typical example is the regression update (or the least square estimation) where the values of regression coefficients may have been changed during the time of collecting data. In this paper we discuss several statistical methods for solving this main problem. The Bayesian approach and the maximum likelihood method are emphasized in the discussion. Experiments of edge detection and detection of singular points on smooth bent surfaces in image processing by using some of the discussed methods have been run and some good results are shown here.

Keywords: change point, regression, test of shift, statistics, edge detection, singular point

Received May 16, 1988; revised February 13, 1989.
Communicated by Lin-Shan Lee.