Fast pyramidal algorithms for image thresholding

doi:10.1016/0031-3203(94)00147-E

Pattern Recognition

Volume 28, Issue 6, June 1995, Pages 901-906

https://doi.org/10.1016/0031-3203(94)00147-E Get rights and content

Abstract

In this paper we present fast pyramidal versions of three sequential algorithms for the thresholding of images consisting of n grey levels. For each of the sequential algorithms, having complexities between O(n) time and O(n²) time, we propose a parallel version which runs in O(lg) n) time on a pyramidal machine with an n × n base.

References (11)

J.S. Weszka
A survey of threshold selection techniques
Comput. Graphics Image Process
(1978)
P.K. Sahoo et al.
A survey of thresholding techniques
Comput. Vision, Graphics Image Process.
(1988)
J.N. Kapur et al.
A new method for gray-level picture thresholding using the entropy of the histogram
Comput. Vision Graphics Image Process.
(1985)
J. Kittler et al.
Minimum error thresholding
Pattern Recognition
(1986)
S.K. Fu et al.
A survey of image segmentation
Pattern Recognition
(1981)

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Image segmentation is an important and fundamental task in many digital image processing systems. Image segmentation by thresholding is the simplest technique and involves the basic assumption that objects and background in the digital image have distinct gray-level distributions. In this paper, we present a general technique for thresholding of digital images based on Renyi's entropy. Our method includes two of the previously proposed well known global thresholding methods. The effectiveness of the proposed method is demonstrated by using examples from the real-world and synthetic images.
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Fast pyramidal algorithms for image thresholding

Abstract

Comput. Graphics Image Process

Comput. Vision, Graphics Image Process.

Comput. Vision Graphics Image Process.

Pattern Recognition

A survey of image segmentation

Pattern Recognition