Minimum cross-entropy threshold selection

Thresholding is a common and easily implemented form of image segmentation. Many methods of automatic threshold selection based on the optimization of some discriminant function have been proposed. Such functions often take the form of a metric distance or similarity measure between the original image and the segmented result. A non-metric measure, the cross-entropy, is used here to determine the optimum threshold. It is shown that this measure is related to other commonly used measures of distance or similarity under special conditions, although it is in some senses more general. Some typical results using this method are presented, together with results using a metric form of the cross-entropy.

[1]  B. Frieden.  Statistical models for the image restoration problem , 1980 .

[2]  J. N. Kapur,et al.  A Comprative Assessment of Entropic and Non-Entropic Methods of Estimation , 1990 .

[3]  Julius T. Tou,et al.  Pattern Recognition Principles , 1974 .

[4]  William H. Press,et al.  Numerical recipes in C. The art of scientific computing , 1987 .

[5]  Azriel Rosenfeld,et al.  Threshold Evaluation Techniques , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  A. D. Brink.  Comments on grey-level thresholding of images using a correlation criterion , 1991, Pattern Recognit. Lett..

[7]  B. Frieden.  Restoring with maximum likelihood and maximum entropy. , 1972, Journal of the Optical Society of America.

[8]  Linda G. Shapiro,et al.  Image Segmentation Techniques , 1984, Other Conferences.

[9]  S. Gull,et al.  Image reconstruction from incomplete and noisy data , 1978, Nature.

[10]  Kannan,et al.  ON IMAGE SEGMENTATION TECHNIQUES , 2022 .

[11]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[12]  John Skilling,et al.  Theory of Maximum Entropy Image Reconstruction , 1986 .

[13]  S. Kullback,et al.  Information Theory and Statistics , 1959 .

[14]  Nobuyuki Otsu,et al.  ATlreshold Selection Method fromGray-Level Histograms , 1979 .

[15]  S E Reichenbach,et al.  Evaluation of automated threshold selection methods for accurately sizing microscopic fluorescent cells by image analysis , 1989, Applied and environmental microbiology.

[16]  Z. Fazekas,et al.  Comments on gray-level thresholding of images using a correlation criterion , 1990, Pattern Recognit. Lett..

[17]  William H. Press,et al.  Numerical Recipes in Fortran 77: The Art of Scientific Computing 2nd Editionn - Volume 1 of Fortran Numerical Recipes , 1992 .

[18]  A. D. Brink,et al.  Grey-level thresholding of images using a correlation criterion , 1989, Pattern Recognit. Lett..

[19]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[20]  John Skilling,et al.  Maximum Entropy and Bayesian Methods , 1989 .

[21]  William H. Press,et al.  Book-Review - Numerical Recipes in Pascal - the Art of Scientific Computing , 1989 .

[22]  Rodney W. Johnson,et al.  Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy , 1980, IEEE Trans. Inf. Theory.