This work is motivated by the potential and promise of image fusion technologies in the multi-sensor image fusion system. The aim of this research is to focus on multi-sensor pixel level image fusion for medical application due to its significance of medical field. Medical fusion methods are only a possible way that able to combine correlating information of multiple images into a single image to explore the possibility of data reduction and improvement of information density. The dissertation explores the possibility of using Stationary Wavelet approach in image fusion and further optimization using Genetic Algorithm and Particle Swarm Optimization. The comparative analysis of Stationary wavelet transform combined with optimization algorithm has been performed with several sets of computed Tomography (CT) scan and Magnetic Resonance imaging (MRI) images using MATLAB. Improve statistics results are obtained in terms of peak signal to noise ratio (PSNR), entropy, root mean square error (RMSE), edge strength, mutual information.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
This work is motivated by the potential and promise of image fusion technologies in the multi-sensor image fusion system. The aim of this research is to focus on multi-sensor pixel level image fusion for medical application due to its significance of medical field. Medical fusion methods are only a possible way that able to combine correlating information of multiple images into a single image to explore the possibility of data reduction and improvement of information density. The dissertation explores the possibility of using Stationary Wavelet approach in image fusion and further optimization using Genetic Algorithm and Particle Swarm Optimization. The comparative analysis of Stationary wavelet transform combined with optimization algorithm has been performed with several sets of computed Tomography (CT) scan and Magnetic Resonance imaging (MRI) images using MATLAB. Improve statistics results are obtained in terms of peak signal to noise ratio (PSNR), entropy, root mean square error (RMSE), edge strength, mutual information.
Dr. Reecha Sharma had her Ph.D in Image Processing. She is having ten years of teaching experience. At present she is working as Assistant Professor in Department of ECE, Punjabi University Patiala, India. She has more than 40 papers in International Journals and International Conferences. She had guided 17 M.tech students.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This work is motivated by the potential and promise of image fusion technologies in the multi-sensor image fusion system. The aim of this research is to focus on multi-sensor pixel level image fusion for medical application due to its significance of medical field. Medical fusion methods are only a possible way that able to combine correlating information of multiple images into a single image to explore the possibility of data reduction and improvement of information density. The dissertation explores the possibility of using Stationary Wavelet approach in image fusion and further optimization using Genetic Algorithm and Particle Swarm Optimization. The comparative analysis of Stationary wavelet transform combined with optimization algorithm has been performed with several sets of computed Tomography (CT) scan and Magnetic Resonance imaging (MRI) images using MATLAB. Improve statistics results are obtained in terms of peak signal to noise ratio (PSNR), entropy, root mean square error (RMSE), edge strength, mutual information. 88 pp. Englisch. N° de réf. du vendeur 9783659959080
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sharma ReechaDr. Reecha Sharma had her Ph.D in Image Processing. She is having ten years of teaching experience. At present she is working as Assistant Professor in Department of ECE, Punjabi University Patiala, India. She has more t. N° de réf. du vendeur 159148421
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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Taschenbuch. Etat : Neu. Neuware -This work is motivated by the potential and promise of image fusion technologies in the multi-sensor image fusion system. The aim of this research is to focus on multi-sensor pixel level image fusion for medical application due to its significance of medical field. Medical fusion methods are only a possible way that able to combine correlating information of multiple images into a single image to explore the possibility of data reduction and improvement of information density. The dissertation explores the possibility of using Stationary Wavelet approach in image fusion and further optimization using Genetic Algorithm and Particle Swarm Optimization. The comparative analysis of Stationary wavelet transform combined with optimization algorithm has been performed with several sets of computed Tomography (CT) scan and Magnetic Resonance imaging (MRI) images using MATLAB. Improve statistics results are obtained in terms of peak signal to noise ratio (PSNR), entropy, root mean square error (RMSE), edge strength, mutual information.Books on Demand GmbH, Überseering 33, 22297 Hamburg 88 pp. Englisch. N° de réf. du vendeur 9783659959080
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This work is motivated by the potential and promise of image fusion technologies in the multi-sensor image fusion system. The aim of this research is to focus on multi-sensor pixel level image fusion for medical application due to its significance of medical field. Medical fusion methods are only a possible way that able to combine correlating information of multiple images into a single image to explore the possibility of data reduction and improvement of information density. The dissertation explores the possibility of using Stationary Wavelet approach in image fusion and further optimization using Genetic Algorithm and Particle Swarm Optimization. The comparative analysis of Stationary wavelet transform combined with optimization algorithm has been performed with several sets of computed Tomography (CT) scan and Magnetic Resonance imaging (MRI) images using MATLAB. Improve statistics results are obtained in terms of peak signal to noise ratio (PSNR), entropy, root mean square error (RMSE), edge strength, mutual information. N° de réf. du vendeur 9783659959080
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