The Experts below are selected from a list of 14598 Experts worldwide ranked by ideXlab platform
Siddhartha Bhattacharyya - One of the best experts on this subject based on the ideXlab platform.
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MultiLevel Quantum Sperm Whale Metaheuristic for Gray-Level Image Thresholding
Advances in Intelligent Systems and Computing, 2020Co-Authors: Siddhartha Bhattacharyya, Sandip Dey, Jan Platos, Vaclav Snasel, Tulika DuttaAbstract:Image thresholding is a fundamental step in Image segmentation. A clever selection of thresholds is a vital step to achieve effective segmentation of Images. In this article, we present a new quantum metaheuristic algorithm inspired by the behavior of sperm whales for optimal thresholding of Gray-Level Images. The algorithm is built using many-valued quantum computing principles which offer greater computational advantages. Results are demonstrated on four test Images with three threshold Levels. The performance of the proposed algorithm has been compared with the qubit encoded quantum-inspired simulated annealing algorithm and the classical sperm whale algorithm with respect to the optimal fitness values and the computational time. Friedman test has been carried out with the competing algorithms to establish the supremacy of the proposed technique. Experimental results indicate the superiority of the proposed method in comparison with the competing methods.
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Optimum Gray Level Image Thresholding using a Quantum Inspired Genetic Algorithm
Advances in Computational Intelligence and Robotics, 2016Co-Authors: Sandip Dey, Siddhartha Bhattacharyya, Ujjwal MaulikAbstract: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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quantum inspired genetic algorithm and particle swarm optimization using chaotic map model based interference for Gray Level Image thresholding
Swarm and evolutionary computation, 2014Co-Authors: Sandip Dey, Siddhartha Bhattacharyya, Ujjwal MaulikAbstract:Abstract In this paper, two meta-heuristics techniques have been employed to introduce two new quantum inspired meta-heuristic methods, namely quantum inspired genetic algorithm and quantum inspired particle swarm optimization for bi-Level thresholding. The proposed methods use Otsu's method, Ramesh's method, Li's method, Shanbag's method and also correlation coefficient as evaluation functions to determine optimal threshold values of Gray-Level Images. They exploit the trivial concepts of quantum computing such as qubits and superposition of states. These properties help to exhibit the feature of parallelism that in turn utilizes the time discreteness of quantum mechanical systems. The proposed methods have been compared with their classical counterparts and later with the quantum evolutionary algorithm (QEA) proposed by Han et al. to evaluate the performance among all participating algorithms for three test Images. The optimal threshold value with the corresponding fitness value, standard deviation of fitness and finally the computational time of each method for each test Image have been reported. The results prove that the proposed methods are time efficient while compared to their conventional counterparts. Another comparative study of the proposed methods with the quantum evolutionary algorithm (QEA) proposed by Han et al. also reveals the weaknesses of the latter.
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Chaotic Map Model-Based Interference Employed in Quantum-Inspired Genetic Algorithm to Determine the Optimum Gray Level Image Thresholding
Global Trends in Intelligent Computing Research and Development, 2014Co-Authors: Sandip Dey, Siddhartha Bhattacharyya, Ujjwal MaulikAbstract: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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An Efficient Quantum Inspired Genetic Algorithm with Chaotic Map Model Based Interference and Fuzzy Objective Function for Gray Level Image Thresholding
2011 International Conference on Computational Intelligence and Communication Networks, 2011Co-Authors: Siddhartha Bhattacharyya, Sandip DeyAbstract:A genetic algorithm inspired by the inherent features of parallelism and time discreteness exhibited by quantum mechanical systems, is presented in this article. The predominant interference operator in the proposed quantum inspired genetic algorithm (QIGA) is influenced by time averages of different random chaotic map models derived from the randomness of quantum mechanical systems. Subsequently, QIGA uses quantum inspired crossover and mutation on the trial solutions, followed by a quantum measurement on the intermediate states, to derive sought results. Application of QIGA to determine optimum threshold intensities is demonstrated on two real life Gray Level Images. The efficacy of QIGA is adjudged w.r.t. a convex combination of two fuzzy thresholding evaluation metrics in a multiple criterion scenario. Comparative study of its performance with the classical counterpart indicates encouraging avenues.
Ujjwal Maulik - One of the best experts on this subject based on the ideXlab platform.
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Optimum Gray Level Image Thresholding using a Quantum Inspired Genetic Algorithm
Advances in Computational Intelligence and Robotics, 2016Co-Authors: Sandip Dey, Siddhartha Bhattacharyya, Ujjwal MaulikAbstract: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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quantum inspired genetic algorithm and particle swarm optimization using chaotic map model based interference for Gray Level Image thresholding
Swarm and evolutionary computation, 2014Co-Authors: Sandip Dey, Siddhartha Bhattacharyya, Ujjwal MaulikAbstract:Abstract In this paper, two meta-heuristics techniques have been employed to introduce two new quantum inspired meta-heuristic methods, namely quantum inspired genetic algorithm and quantum inspired particle swarm optimization for bi-Level thresholding. The proposed methods use Otsu's method, Ramesh's method, Li's method, Shanbag's method and also correlation coefficient as evaluation functions to determine optimal threshold values of Gray-Level Images. They exploit the trivial concepts of quantum computing such as qubits and superposition of states. These properties help to exhibit the feature of parallelism that in turn utilizes the time discreteness of quantum mechanical systems. The proposed methods have been compared with their classical counterparts and later with the quantum evolutionary algorithm (QEA) proposed by Han et al. to evaluate the performance among all participating algorithms for three test Images. The optimal threshold value with the corresponding fitness value, standard deviation of fitness and finally the computational time of each method for each test Image have been reported. The results prove that the proposed methods are time efficient while compared to their conventional counterparts. Another comparative study of the proposed methods with the quantum evolutionary algorithm (QEA) proposed by Han et al. also reveals the weaknesses of the latter.
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Chaotic Map Model-Based Interference Employed in Quantum-Inspired Genetic Algorithm to Determine the Optimum Gray Level Image Thresholding
Global Trends in Intelligent Computing Research and Development, 2014Co-Authors: Sandip Dey, Siddhartha Bhattacharyya, Ujjwal MaulikAbstract: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.
Sandip Dey - One of the best experts on this subject based on the ideXlab platform.
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MultiLevel Quantum Sperm Whale Metaheuristic for Gray-Level Image Thresholding
Advances in Intelligent Systems and Computing, 2020Co-Authors: Siddhartha Bhattacharyya, Sandip Dey, Jan Platos, Vaclav Snasel, Tulika DuttaAbstract:Image thresholding is a fundamental step in Image segmentation. A clever selection of thresholds is a vital step to achieve effective segmentation of Images. In this article, we present a new quantum metaheuristic algorithm inspired by the behavior of sperm whales for optimal thresholding of Gray-Level Images. The algorithm is built using many-valued quantum computing principles which offer greater computational advantages. Results are demonstrated on four test Images with three threshold Levels. The performance of the proposed algorithm has been compared with the qubit encoded quantum-inspired simulated annealing algorithm and the classical sperm whale algorithm with respect to the optimal fitness values and the computational time. Friedman test has been carried out with the competing algorithms to establish the supremacy of the proposed technique. Experimental results indicate the superiority of the proposed method in comparison with the competing methods.
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Optimum Gray Level Image Thresholding using a Quantum Inspired Genetic Algorithm
Advances in Computational Intelligence and Robotics, 2016Co-Authors: Sandip Dey, Siddhartha Bhattacharyya, Ujjwal MaulikAbstract: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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quantum inspired genetic algorithm and particle swarm optimization using chaotic map model based interference for Gray Level Image thresholding
Swarm and evolutionary computation, 2014Co-Authors: Sandip Dey, Siddhartha Bhattacharyya, Ujjwal MaulikAbstract:Abstract In this paper, two meta-heuristics techniques have been employed to introduce two new quantum inspired meta-heuristic methods, namely quantum inspired genetic algorithm and quantum inspired particle swarm optimization for bi-Level thresholding. The proposed methods use Otsu's method, Ramesh's method, Li's method, Shanbag's method and also correlation coefficient as evaluation functions to determine optimal threshold values of Gray-Level Images. They exploit the trivial concepts of quantum computing such as qubits and superposition of states. These properties help to exhibit the feature of parallelism that in turn utilizes the time discreteness of quantum mechanical systems. The proposed methods have been compared with their classical counterparts and later with the quantum evolutionary algorithm (QEA) proposed by Han et al. to evaluate the performance among all participating algorithms for three test Images. The optimal threshold value with the corresponding fitness value, standard deviation of fitness and finally the computational time of each method for each test Image have been reported. The results prove that the proposed methods are time efficient while compared to their conventional counterparts. Another comparative study of the proposed methods with the quantum evolutionary algorithm (QEA) proposed by Han et al. also reveals the weaknesses of the latter.
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Chaotic Map Model-Based Interference Employed in Quantum-Inspired Genetic Algorithm to Determine the Optimum Gray Level Image Thresholding
Global Trends in Intelligent Computing Research and Development, 2014Co-Authors: Sandip Dey, Siddhartha Bhattacharyya, Ujjwal MaulikAbstract: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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An Efficient Quantum Inspired Genetic Algorithm with Chaotic Map Model Based Interference and Fuzzy Objective Function for Gray Level Image Thresholding
2011 International Conference on Computational Intelligence and Communication Networks, 2011Co-Authors: Siddhartha Bhattacharyya, Sandip DeyAbstract:A genetic algorithm inspired by the inherent features of parallelism and time discreteness exhibited by quantum mechanical systems, is presented in this article. The predominant interference operator in the proposed quantum inspired genetic algorithm (QIGA) is influenced by time averages of different random chaotic map models derived from the randomness of quantum mechanical systems. Subsequently, QIGA uses quantum inspired crossover and mutation on the trial solutions, followed by a quantum measurement on the intermediate states, to derive sought results. Application of QIGA to determine optimum threshold intensities is demonstrated on two real life Gray Level Images. The efficacy of QIGA is adjudged w.r.t. a convex combination of two fuzzy thresholding evaluation metrics in a multiple criterion scenario. Comparative study of its performance with the classical counterpart indicates encouraging avenues.
Chin-chen Chang - One of the best experts on this subject based on the ideXlab platform.
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a difference expansion oriented data hiding scheme for restoring the original host Images
Journal of Systems and Software, 2006Co-Authors: Chin-chen ChangAbstract:This paper proposes a lossless data embedding scheme that exploits the difference expansion of the pixels to conceal large amount of message data in a digital Image. The proposed scheme takes into consideration the correlation between the pixel and its surrounding pixels to determine the degree of the difference expansion for message data embedding. The performance has been evaluated in terms of Image distortion, payload capacity, as well as embedding rate. The experimental results show that the scheme is capable of providing a great payload capacity, and the Image quality of the embedded Image is better than that of Tian's and Celik's schemes for a Gray-Level Image. What is more, for a color Image, the proposed scheme outperforms Alattar's scheme at low PSNR. In addition, the proposed scheme can completely restore the original Image after data extraction.
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A Novel Image Ownership Protection Scheme Based on Rehashing Concept and Vector Quantization
Fundamenta Informaticae, 2006Co-Authors: Chia-chen Lin, Chin-chen ChangAbstract:A novel watermarking scheme that is capable of hiding authorization data in a Gray-Level Image is proposed in this letter. The proposed scheme generates a key stream to be the key to connect the authorized Image and its watermark. The key stream is extra data rather than data being embedded into the authorized Image. Such arrangement can guarantee the integrity of the Image. To reduce the complexity in computing the key stream for the authorized Gray-Level Image, two techniques are employed in the proposed scheme. One is the concept of rehashing, and the other is the vector quantization. The benefits of the proposal are to provide a key stream to prove the Gray-Level's ownership, and to keep the authorized Image in its original state without any modification.
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Hiding a halftone secret Image in two camouflaged halftone Images
Pattern Recognition and Image Analysis, 2006Co-Authors: Chin-chen Chang, Chi-shiang Chan, W. L. TaiAbstract:In this paper, we shall propose a method to hide a halftone secret Image into two other camouflaged halftone Images. In our method, we adjust the Gray-Level Image pixel value to fit the pixel values of the secret Image and two camouflaged Images. Then, we use the halftone technique to transform the secret Image into a secret halftone Image. After that, we make two camouflaged halftone Images at the same time out of the two camouflaged Images and the secret halftone Image. After overlaying the two camouflaged halftone Images, the secret halftone Image can be revealed by using our eyes. The experimental results included in this paper show that our method is very practicable.
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embedding robust Gray Level watermark in an Image using discrete cosine transformation
Distributed multimedia databases, 2002Co-Authors: Chweishyong Tsai, Chin-chen Chang, Tungshou Chen, Minghuang ChenAbstract:Digital watermarking is an effective technique to protect the intellectual property rights of digital Images. In general, a Gray-Level Image can provide more perceptual information; moreover, the size of each pixel in the Gray-Level Image is bigger. Commonly, Gray-Level digital watermarks are more robust. In this chapter, the proposed watermarking scheme adopts a Gray-Level Image as the watermark. In addition, discrete cosine transformation (DCT) technique and quantization method are applied to strengthen the robustness of the watermarking system. Both original Image and digital watermark, processed by DCT transformation, can build a quantization table to reduce the information size of the digital watermark. After quantized watermark is embedded into the middle frequency bands of the transformed original Image, the quality of the watermarked Image is always visually acceptable because of the effectiveness of the quantization technique. The experimental results show that the embedded watermark can resist Image cropping, JPEG lossy compression, and destructive processes such as Image blurring and sharpening.
Leon O Chua - One of the best experts on this subject based on the ideXlab platform.
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a piecewise linear simplicial coupling cell for cnn Gray Level Image processing
IEEE Transactions on Circuits and Systems I-regular Papers, 2002Co-Authors: P Julian, Radu Dogaru, Leon O ChuaAbstract:In this paper, we propose a universal piecewise-linear (PWL) CNN coupling cell, the simplicial cell, which is intended to work with binary as well as Gray-Level inputs. The construction of the cell is based on the theory of canonical simplicial PWL representations. As a consequence, the coupling function is endowed with important numerical features, namely: the representation of the characteristic cell function is sparse; the family of coupling functions constitutes a Hilbert space; powerful solution algorithms have been developed for the approximation of nonlinear functions, which is particularly useful when the CNN parameters need to be tuned from examples; the parameters can be extracted from a truth table when the CNN is specified analytically.
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a piecewise linear simplicial coupling cell for cnn Gray Level Image processing
International Symposium on Circuits and Systems, 2001Co-Authors: P Julian, Radu Dogaru, Leon O ChuaAbstract:We propose a universal piecewise-linear coupling cell, the Simplicial Cell, which satisfies the guidelines provided for a universal cell, and is intended to work with binary as well as Gray-Level inputs. The performance of the cell is one of the salient features of the approach; in fact, in the case of a 3/spl times/3 sphere of influence, it only needs to evaluate a linear combination of 10 terms to produce the output.
