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Image fusion
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Image fusion
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Image fusionby Gang Xiao ... [et al.].
其他作者:
Xiao, Gang.
出版者:
Singapore :Springer Singapore :2020.
面頁冊數:
xviii, 404 p. :ill., digital ;24 cm.
Contained By:
Springer Nature eBook
標題:
Optical data processing.
電子資源:
https://doi.org/10.1007/978-981-15-4867-3
ISBN:
9789811548673$q(electronic bk.)
Image fusion
Image fusion
[electronic resource] /by Gang Xiao ... [et al.]. - Singapore :Springer Singapore :2020. - xviii, 404 p. :ill., digital ;24 cm.
Preface -- Author introduction -- Acknowledgement -- Part I: Image Fusion Theories -- Chapter 1: Introduction to Image Fusion -- Chapter 2: Pixel-level Image Fusion -- Chapter 3: Feature-level Image Fusion -- Chapter 4: Decision-level Image Fusion -- Chapter 5: Multi-sensor Dynamic Image Fusion -- Chapter 6: Objective Fusion Metrics -- Chapter 7: Image Fusion Based on Machine Learning and Deep Learning -- Part II: Experimental Examples -- Chapter 8: Example 1: Medical Image Fusion -- Chapter 9: Example 2: Night Vision image Fusion -- Chapter 10: Simulation Platform of Image Fusion.
This book systematically discusses the basic concepts, theories, research and latest trends in image fusion. It focuses on three image fusion categories - pixel, feature and decision - presenting various applications, such as medical imaging, remote sensing, night vision, robotics and autonomous vehicles. Further, it introduces readers to a new category: edge-preserving-based image fusion, and provides an overview of image fusion based on machine learning and deep learning. As such, it is a valuable resource for graduate students and scientists in the field of digital image processing and information fusion.
ISBN: 9789811548673$q(electronic bk.)
Standard No.: 10.1007/978-981-15-4867-3doiSubjects--Topical Terms:
185609
Optical data processing.
LC Class. No.: TA1634
Dewey Class. No.: 006.37
Image fusion
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Preface -- Author introduction -- Acknowledgement -- Part I: Image Fusion Theories -- Chapter 1: Introduction to Image Fusion -- Chapter 2: Pixel-level Image Fusion -- Chapter 3: Feature-level Image Fusion -- Chapter 4: Decision-level Image Fusion -- Chapter 5: Multi-sensor Dynamic Image Fusion -- Chapter 6: Objective Fusion Metrics -- Chapter 7: Image Fusion Based on Machine Learning and Deep Learning -- Part II: Experimental Examples -- Chapter 8: Example 1: Medical Image Fusion -- Chapter 9: Example 2: Night Vision image Fusion -- Chapter 10: Simulation Platform of Image Fusion.
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This book systematically discusses the basic concepts, theories, research and latest trends in image fusion. It focuses on three image fusion categories - pixel, feature and decision - presenting various applications, such as medical imaging, remote sensing, night vision, robotics and autonomous vehicles. Further, it introduces readers to a new category: edge-preserving-based image fusion, and provides an overview of image fusion based on machine learning and deep learning. As such, it is a valuable resource for graduate students and scientists in the field of digital image processing and information fusion.
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