An Efficient Quantum Inspired Genetic Algorithm with Chaotic Map Model Based Interference and Fuzzy Objective Function for Gray Level Image Thresholding

2011 ◽  
Author(s):  
Siddhartha Bhattacharyya ◽  
Sandip Dey
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Chaotic Map ◽  
Gray Level ◽  
Image Thresholding ◽  
Model Based ◽  
Gray Level Image

Optimum Gray Level Image Thresholding using a Quantum Inspired Genetic Algorithm

2016 ◽  
pp. 349-377 ◽  
Author(s):  
Sandip Dey ◽  
Siddhartha Bhattacharyya ◽  
Ujjwal Maulik
Keyword(s):  
Genetic Algorithm ◽  
Chaotic Map ◽  
Population Diversity ◽  
Gray Level ◽  
The Novel ◽  
Image Thresholding ◽  
Gray Level Image ◽  
Quantum Operators ◽  
Pass Through ◽  
Gray Level Images

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.

Chaotic Map Model-Based Interference Employed in Quantum-Inspired Genetic Algorithm to Determine the Optimum Gray Level Image Thresholding

2015 ◽  
pp. 503-542
Author(s):  
Sandip Dey ◽  
Siddhartha Bhattacharyya ◽  
Ujjwal Maulik
Keyword(s):  
Genetic Algorithm ◽  
Quantum Computing ◽  
Chaotic Map ◽  
Threshold Value ◽  
Evaluation Metrics ◽  
Gray Level ◽  
Linear Index ◽  
Gray Level Image ◽  
Basic Features ◽  
Conventional Genetic Algorithm

In this chapter, a Quantum-Inspired Genetic Algorithm (QIGA) is presented. The QIGA adopted the inherent principles of quantum computing and has been applied on three gray level test images to determine their optimal threshold values. Quantum random interference based on chaotic map models and later quantum crossover, quantum mutation, and quantum shift operation have been applied in the proposed QIGA. The basic features of quantum computing like qubit, superposition of states, coherence and decoherence, etc. help to espouse parallelism and time discreteness in QIGA. Finally, the optimum threshold value has been derived through the quantum measurement phase. In the proposed QIGA, the selected evaluation metrics are Wu's algorithm, Renyi's algorithm, Yen's algorithm, Johannsen's algorithm, Silva's algorithm, and finally, linear index of fuzziness, and the selected gray level images are Baboon, Peppers, and Corridor. The conventional Genetic Algorithm (GA) and Quantum Evolutionary Algorithm (QEA) proposed by Han et al. have been run on the same set of images and evaluation metrics with the same parameters as QIGA. Finally, the performance analysis has been made between the proposed QIGA with the conventional GA and later with QEA proposed by Han et al., which reveals its time efficacy compared to GA along with the drawbacks in QEA.

Chaotic Map Model-Based Interference Employed in Quantum-Inspired Genetic Algorithm to Determine the Optimum Gray Level Image Thresholding

2014 ◽  
pp. 68-110 ◽  
Author(s):  
Sandip Dey ◽  
Siddhartha Bhattacharyya ◽  
Ujjwal Maulik
Keyword(s):  
Genetic Algorithm ◽  
Quantum Computing ◽  
Chaotic Map ◽  
Threshold Value ◽  
Evaluation Metrics ◽  
Gray Level ◽  
Linear Index ◽  
Gray Level Image ◽  
Basic Features ◽  
Conventional Genetic Algorithm

In this chapter, a Quantum-Inspired Genetic Algorithm (QIGA) is presented. The QIGA adopted the inherent principles of quantum computing and has been applied on three gray level test images to determine their optimal threshold values. Quantum random interference based on chaotic map models and later quantum crossover, quantum mutation, and quantum shift operation have been applied in the proposed QIGA. The basic features of quantum computing like qubit, superposition of states, coherence and decoherence, etc. help to espouse parallelism and time discreteness in QIGA. Finally, the optimum threshold value has been derived through the quantum measurement phase. In the proposed QIGA, the selected evaluation metrics are Wu’s algorithm, Renyi’s algorithm, Yen’s algorithm, Johannsen’s algorithm, Silva’s algorithm, and finally, linear index of fuzziness, and the selected gray level images are Baboon, Peppers, and Corridor. The conventional Genetic Algorithm (GA) and Quantum Evolutionary Algorithm (QEA) proposed by Han et al. have been run on the same set of images and evaluation metrics with the same parameters as QIGA. Finally, the performance analysis has been made between the proposed QIGA with the conventional GA and later with QEA proposed by Han et al., which reveals its time efficacy compared to GA along with the drawbacks in QEA.

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