Share Email Print

Proceedings Paper

Image thresholding using standard deviation
Author(s): Jung-Min Sung; Dae-Chul Kim; Bong-Yeol Choi; Yeong-Ho Ha
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Threshold selection using the within-class variance in Otsu’s method is generally moderate, yet inappropriate for expressing class statistical distributions. Otsu uses a variance to represent the dispersion of each class based on the distance square from the mean to any data. However, since the optimal threshold is biased toward the larger variance among two class variances, variances cannot be used to denote the real class statistical distributions. Therefore, to express more accurate class statistical distributions, this paper proposes the within-class standard deviation as a criterion for threshold selection, and the optimal threshold is then determined by minimizing the within-class standard deviation. Experimental results confirm that the proposed method produced a better performance than existing algorithms.

Paper Details

Date Published: 7 March 2014
PDF: 7 pages
Proc. SPIE 9024, Image Processing: Machine Vision Applications VII, 90240R (7 March 2014); doi: 10.1117/12.2040990
Show Author Affiliations
Jung-Min Sung, Kyungpook National Univ. (Korea, Republic of)
Dae-Chul Kim, Kyungpook National Univ. (Korea, Republic of)
Bong-Yeol Choi, Kyungpook National Univ. (Korea, Republic of)
Yeong-Ho Ha, Kyungpook National Univ. (Korea, Republic of)

Published in SPIE Proceedings Vol. 9024:
Image Processing: Machine Vision Applications VII
Kurt S. Niel; Philip R. Bingham, Editor(s)

© SPIE. Terms of Use
Back to Top