A survey of thresholding techniques

Sahoo, P. K.<sup>1</sup>; Soltani, S.<sup>1</sup>; Wong, A. K. C.<sup>1</sup>

Affiliations

¹Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada N2L 361

Abstract

In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. Vision Graphics & Image Process7, 1978, 259–265) and Fu and Mu (Pattern Recognit.13, 1981, 3–16). We attempt to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures. The evaluation is based on some real world images.

Links

DOI: 10.1016/0734-189X(88)90022-9

WoS: A1988M058600007

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Cited By (20)

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1985	Kapur JN, Sahoo PK, Wong AKC. A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Graph Image Proc. 1985;29(3):273-285.
1990	Lee SU, Chung SY, Park RH. A comparative performance study of several global thresholding techniques for segmentation. Comput Vis Graph Image Proc. November 1990;52(2):171-190.
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2022	Luengo J, Moreno R, Sevillano I, Charte D, Peláez-Vegas A, Fernández-Moreno M, Mesejo P, Herrera F. A tutorial on the segmentation of metallographic images: taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges. Inf Fusion. February 1, 2022;78:232.
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