Sinossi
This book presents several signal processing algorithms for image fusion in noisy multimodal conditions, such as medical, surveillance and satellite imaging. It first introduces a novel image fusion method - Chebyshev polynomial analysis (CPA), which performs well for image sets heavily corrupted by noise. CPA's fast convergence and smooth approximation renders it ideal for indiscriminate denoising fusion tasks. The concept is then further extended by incorporating the advantages of CP with those of a state-of-the-art fusion technique named independent component analysis (ICA), to create a hybrid fusion scheme based on region saliency. Further, the book focuses on the development of a new metric for image fusion evaluation that is specifically based on texture. The conservation of background textural details is considered important in many fusion applications as they help define the image depth and structure, which may prove crucial in many surveillance and remote sensing applications. For this, gray-level co-occurrence matrix (GLCM) is utilised. Tests performed on established fusion methods verify that the proposed metric is viable, especially for multimodal scenarios.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
L'autore
Dr Zaid Omar is a lecturer with the Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM). He obtained his PhD from Imperial College London in 2012, and his MSc at Sheffield in 2008. His research interests are digital image processing and analysis methods, mainly for medical and surveillance tasks. He is a member of IEEE and IET.
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Algorithms for Enhanced Image Fusion Performance
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LAP LAMBERT Academic Publishing Jul 2016, 2016
ISBN 10: 3659914436
ISBN 13: 9783659914430
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents several signal processing algorithms for image fusion in noisy multimodal conditions, such as medical, surveillance and satellite imaging. It first introduces a novel image fusion method - Chebyshev polynomial analysis (CPA), which performs well for image sets heavily corrupted by noise. CPA's fast convergence and smooth approximation renders it ideal for indiscriminate denoising fusion tasks. The concept is then further extended by incorporating the advantages of CP with those of a state-of-the-art fusion technique named independent component analysis (ICA), to create a hybrid fusion scheme based on region saliency. Further, the book focuses on the development of a new metric for image fusion evaluation that is specifically based on texture. The conservation of background textural details is considered important in many fusion applications as they help define the image depth and structure, which may prove crucial in many surveillance and remote sensing applications. For this, gray-level co-occurrence matrix (GLCM) is utilised. Tests performed on established fusion methods verify that the proposed metric is viable, especially for multimodal scenarios. 160 pp. Englisch. Codice articolo 9783659914430
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Omar ZaidDr Zaid Omar is a lecturer with the Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM). He obtained his PhD from Imperial College London in 2012, and his MSc at Sheffield in 2008. His research interests a. Codice articolo 158877748
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Algorithms for Enhanced Image Fusion Performance
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LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659914436
ISBN 13: 9783659914430
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Paperback. Condizione: Brand New. 160 pages. 8.66x5.91x0.37 inches. In Stock. Codice articolo 3659914436
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Algorithms for Enhanced Image Fusion Performance
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LAP LAMBERT Academic Publishing Jul 2016, 2016
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ISBN 13: 9783659914430
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents several signal processing algorithms for image fusion in noisy multimodal conditions, such as medical, surveillance and satellite imaging. It first introduces a novel image fusion method - Chebyshev polynomial analysis (CPA), which performs well for image sets heavily corrupted by noise. CPA's fast convergence and smooth approximation renders it ideal for indiscriminate denoising fusion tasks. The concept is then further extended by incorporating the advantages of CP with those of a state-of-the-art fusion technique named independent component analysis (ICA), to create a hybrid fusion scheme based on region saliency. Further, the book focuses on the development of a new metric for image fusion evaluation that is specifically based on texture. The conservation of background textural details is considered important in many fusion applications as they help define the image depth and structure, which may prove crucial in many surveillance and remote sensing applications. For this, gray-level co-occurrence matrix (GLCM) is utilised. Tests performed on established fusion methods verify that the proposed metric is viable, especially for multimodal scenarios.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 160 pp. Englisch. Codice articolo 9783659914430
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Taschenbuch. Condizione: Neu. Algorithms for Enhanced Image Fusion Performance | Zaid Omar | Taschenbuch | 160 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783659914430 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 103469254
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Algorithms for Enhanced Image Fusion Performance
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LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659914436
ISBN 13: 9783659914430
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents several signal processing algorithms for image fusion in noisy multimodal conditions, such as medical, surveillance and satellite imaging. It first introduces a novel image fusion method - Chebyshev polynomial analysis (CPA), which performs well for image sets heavily corrupted by noise. CPA's fast convergence and smooth approximation renders it ideal for indiscriminate denoising fusion tasks. The concept is then further extended by incorporating the advantages of CP with those of a state-of-the-art fusion technique named independent component analysis (ICA), to create a hybrid fusion scheme based on region saliency. Further, the book focuses on the development of a new metric for image fusion evaluation that is specifically based on texture. The conservation of background textural details is considered important in many fusion applications as they help define the image depth and structure, which may prove crucial in many surveillance and remote sensing applications. For this, gray-level co-occurrence matrix (GLCM) is utilised. Tests performed on established fusion methods verify that the proposed metric is viable, especially for multimodal scenarios. Codice articolo 9783659914430
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