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Comment on Using the Uniformity Measure for Performance Measure in Image Segmentation
September 1996 (vol. 18 no. 9)
pp. 933-934

Abstract—In this correspondence, we will point out that the image segmentation performance measure—the uniformity measure, which is suggested by Levine and Nazif [1]—is basically equivalent to the criterion measure proposed by Otsu [2].

[1] M.D. Levine and A.M. Nazif, "Dynamic Measurement of Computer Generated Image Segmentations," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 7, pp. 155-164, 1985.
[2] N. Otsu, "A Threshold Selection Method from Gray-Level Histogram," IEEE Trans. Systems, Man, and Cybernetics, vol. 9, pp. 62-66, 1979.
[3] J.S. Weszka and A. Rosenfeld, "Threshold Evaluation Techniques," IEEE Trans. Systems, Man, and Cybernetics, vol. 8, pp. 622-629, 1971.
[4] P.K. Sahoo, S. Soltani, A.K.C. Wong, and Y.C. Chen, “A Survey of Thresholding Techniques,” Computer Vision, Graphics, and Image Processing, vol. 41, pp. 233-260, 1988.
[5] K. Fukunaga, Introduction to Statistical Pattern Recognition, second edition. Academic Press, 1990.
[6] I. Cseke and Z. Fazekas, "Comments on Gray-Level Thresholding of Images Using a Correlation Criterion," Pattern Recognition Letters, vol. 11, pp. 709-710, 1990.
[7] A.D. Brink, "Grey-Level Thresholding of Images Using a Correlation Criterion," Pattern Recognition Letters, vol. 9, pp. 335-341, 1989.
[8] R.C. Gonzalez and R.E. Woods, Digital Image Processing, Addison-Wesley, New York, 1993.

Index Terms:
Thresholding, binarization, segmentation, evaluation, optimality measure.
Citation:
W.s. Ng, C.k. Lee, "Comment on Using the Uniformity Measure for Performance Measure in Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 933-934, Sept. 1996, doi:10.1109/34.537347
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