Statistical Tests for Edg Detection

에지 검출을 위한 통계적 검정법

  • 임동훈 (경상대학교 통계정보학과) ;
  • 성신희 (경상대학교 대학원 통계학과)
  • Published : 2000.03.01

Abstract

In this paper we describe a nonparametric Wilcoxon test and a parametric Z test based on statistical hypothesis testing for the detection of edges. We use the threshold determined by specifying significance level $\alpha$, while Bovik, Huang and Munson[4] consider the range of possible values of test statistics for the threshold. From the experimental results of edge detection, the Z method performs sensitively to the noisy image, while the Wilcoxon method is robust over both noisy nd noise-free images. Comparison with our statistical tests and Sobel operator shows that our tests perform more effectively in both noisy and noise-free images.

Keywords

References

  1. Anil K, Jain. Fundamentals of Digital Image Processing, Prentice-Hall International Inc. 1989
  2. R. M. Haralick, 'Edge and Region Analysis for Digital Image Data.' Computer Vision, Graphics, and Image Processing 12, pp.60-73, 1980 https://doi.org/10.1016/0146-664X(80)90004-0
  3. Jun S. Huang and Dong H. Tseng, 'Statistical Theory of Edge Detection,' Computer Vision, Graphics, and Image Processing 43, pp.337-346, 1988 https://doi.org/10.1016/0734-189X(88)90087-4
  4. Alan C. Bovik, Thomas S. Huang and David C. Munson, 'Nonparametric Tests for Edge Detection in Noise,' Pattern Recognition, Vol.19, No.3, pp.209-219, 1986 https://doi.org/10.1016/0031-3203(86)90011-7
  5. F. Wilcoxon, 'Individual Comparisons by Ranking Methods,' Biometrics 1, pp.80-83, 1945 https://doi.org/10.2307/3001968
  6. R. V. Hogg and E. A. Tanis. Probability and Statistical Inference. Macmillan Publishing Company. 1993