Title: Quantum genetic algorithm for adaptive image multi-thresholding segmentation

Authors: Jian Zhang; Huanzhou Li; Zhangguo Tang; Chang Liu

Addresses: School of Physics and Electronics Engineering, Sichuan Normal University, Chengdu City, Sichuan Province 610066, China ' School of Physics and Electronics Engineering, Sichuan Normal University, Chengdu City, Sichuan Province 610066, China ' School of Physics and Electronics Engineering, Sichuan Normal University, Chengdu City, Sichuan Province 610066, China ' DISP Laboratory, University Lumiere of Lyon 2, Lyon City, Rhone Province 69005, France

Abstract: An adaptive image multilevel thresholding segmentation algorithm is presented in this paper. The proposed algorithm introduces a parallel quantum genetic algorithm (PQGA) for histogram-based image segmentation. Quantum genetic algorithm (QGA) has the advantages of fast convergence speed and strong global search capabilities. And PQGA can improve the computational efficiency of the QGA further. Without predetermining the number of the thresholds, the proposed algorithm that chooses the automatic thresholding criterion as its objective function can obtain the number of the thresholds and the corresponding thresholds accurately. The experimental results demonstrate good performance of the PQGA in solving adaptive multilevel thresholding segmentation problems by comparing with other methods for several test images.

Keywords: quantum genetic algorithms; multi-thresholding segmentation; image segmentation; PQGA; parallel QGA; ATC; automatic thresholding criterion; image processing.

DOI: 10.1504/IJCAT.2015.069334

International Journal of Computer Applications in Technology, 2015 Vol.51 No.3, pp.203 - 211

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 11 May 2015 *

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