Algorithms for Enhanced Image Fusion Performance - Tapa blanda

Omar, Zaid

 
9783659914430: Algorithms for Enhanced Image Fusion Performance
Tapa blanda
ISBN 10: 3659914436 ISBN 13: 9783659914430
Editorial: LAP LAMBERT Academic Publishing, 2016

Sinopsis

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.

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Reseña del editor

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.

Biografía del autor

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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Editorial
LAP LAMBERT Academic Publishing
Año de publicación
2016
Idioma
Inglés
ISBN 10 
3659914436
ISBN 13 
9783659914430
Encuadernación
Tapa blanda
Número de páginas
160
Contacto del fabricante
no disponible
Persona responsable
no disponible