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Optimum Gray Level Image Thresholding using a Quantum Inspired Genetic Algorithm

Optimum Gray Level Image Thresholding using a Quantum Inspired Genetic Algorithm

Sandip Dey, Siddhartha Bhattacharyya, Ujjwal Maulik
Copyright: © 2016 |Pages: 29
ISBN13: 9781466694743|ISBN10: 1466694742|EISBN13: 9781466694750
DOI: 10.4018/978-1-4666-9474-3.ch012
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MLA

Dey, Sandip, et al. "Optimum Gray Level Image Thresholding using a Quantum Inspired Genetic Algorithm." Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications, edited by Siddhartha Bhattacharyya, et al., IGI Global, 2016, pp. 349-377. https://doi.org/10.4018/978-1-4666-9474-3.ch012

APA

Dey, S., Bhattacharyya, S., & Maulik, U. (2016). Optimum Gray Level Image Thresholding using a Quantum Inspired Genetic Algorithm. In S. Bhattacharyya, P. Banerjee, D. Majumdar, & P. Dutta (Eds.), Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications (pp. 349-377). IGI Global. https://doi.org/10.4018/978-1-4666-9474-3.ch012

Chicago

Dey, Sandip, Siddhartha Bhattacharyya, and Ujjwal Maulik. "Optimum Gray Level Image Thresholding using a Quantum Inspired Genetic Algorithm." In Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications, edited by Siddhartha Bhattacharyya, et al., 349-377. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9474-3.ch012

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Abstract

In this article, a genetic algorithm inspired by quantum computing is presented. The novel algorithm referred to as quantum inspired genetic algorithm (QIGA) is applied to determine optimal threshold of two gray level images. Different random chaotic map models exhibit the inherent interference operation in collaboration with qubit and superposition of states. The random interference is followed by three different quantum operators viz., quantum crossover, quantum mutation and quantum shifting produce population diversity. Finally, the intermediate states pass through the quantum measurement for optimization of image thresholding. In the proposed algorithm three evaluation metrics such as Brinks's, Kapur's and Pun's algorithms have been applied to two gray level images viz., Lena and Barbara. These algorithms have been applied in conventional GA and Han et al.'s QEA. A comparative study has been made between the proposed QIGA, Han et al.'s algorithm and conventional GA that indicates encouraging avenues of the proposed QIGA.

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