In computer vision and image processing, Otsu's method, named after Nobuyuki Otsu (大津展之, Ōtsu Nobuyuki), is used to perform automatic image thresholding.[1] In the simplest form, the algorithm returns a single intensity threshold that separate pixels into two classes, foreground and background. This threshold is determined by minimizing intra-class intensity variance, or equivalently, by maximizing inter-class variance.[2] Otsu's method is a one-dimensional discrete analogue of Fisher's discriminant analysis, is related to Jenks optimization method, and is equivalent to a globally optimal k-means[3] performed on the intensity histogram. The extension to multi-level thresholding was described in the original paper,[2] and computationally efficient implementations have since been proposed.[4][5]
- ^ M. Sezgin & B. Sankur (2004). "Survey over image thresholding techniques and quantitative performance evaluation". Journal of Electronic Imaging. 13 (1): 146–165. Bibcode:2004JEI....13..146S. doi:10.1117/1.1631315.
- ^ a b Nobuyuki Otsu (1979). "A threshold selection method from gray-level histograms". IEEE Transactions on Systems, Man, and Cybernetics. 9 (1): 62–66. doi:10.1109/TSMC.1979.4310076. S2CID 15326934.
- ^ Liu, Dongju (2009). "Otsu method and K-means". Ninth International Conference on Hybrid Intelligent Systems IEEE. 1: 344–349.
- ^ Liao, Ping-Sung (2001). "A fast algorithm for multilevel thresholding" (PDF). J. Inf. Sci. Eng. 17 (5): 713–727. doi:10.6688/JISE.2001.17.5.1. S2CID 9609430. Archived from the original (PDF) on 2019-06-24.
- ^ Huang, Deng-Yuan (2009). "Optimal multi-level thresholding using a two-stage Otsu optimization approach". Pattern Recognition Letters. 30 (3): 275–284. Bibcode:2009PaReL..30..275H. doi:10.1016/j.patrec.2008.10.003.