An effective multilevel thresholding approach using conditional probability entropy and genetic algorithm

Authors:
Yan Chang;Hong Yan
Affiliations:
School of Electrical and Information Engineering, The University of Sydney, NSW, Australia;School of Electrical and Information Engineering, The University of Sydney, NSW, Australia
Venue:
VIP '02 Selected papers from the 2002 Pan-Sydney workshop on Visualisation - Volume 22
Year:
2003

Citing 4
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Abstract

Entropy-based image thresholding are used widely in image processing. Conventional methods are efficient in the case of bi-level thresholding. But they are very computationally time consuming when extended to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. In this paper, we propose a conditional probability entropy (CPE) based on Bayesian theory and employ Genetic Algorithm (GA) to maximize the CPE for the multithresholds. The experimental results show that CPE is a good criterion of image thresholding and GA is a applicable fast algorithm for multi-level thresholding compared to the exhaustive searching method.