Image Fusion: Theories, Techniques and Applications
The purpose of this book is to provide a practical introduction to the th- ries, techniques and applications of image fusion. The present work has been designed as a textbook for a one-semester ?nal-year undergraduate, or ?r- year graduate, course in image fusion. It should also be useful to practising engineers who wish to learn the concepts of image fusion and apply them to practical applications. In addition, the book may also be used as a supp- mentary text for a graduate course on topics in advanced image processing. The book complements the author’s previous work on multi-sensor data  fusion by concentrating exclusively on the theories, techniques and app- cations of image fusion. The book is intended to be self-contained in so far as the subject of image fusion is concerned, although some prior exposure to the ?eld of computer vision and image processing may be helpful to the reader. Apart from two preliminary chapters, the book is divided into three parts.
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Anal base image binary images biometric calculate camera chapter classifier Cmax column vector common representational format Comp corresponding decision map defined denote detail images detection distance measure distribution eigenvectors ensemble learning estimate face recognition feature maps following example illustrates function fused image Gaussian Geosci given Hausdorff distance histogram matching IEEE IEEE Trans image fusion applications Image Process image registration image segmentation input image Intell iteration K-means algorithm label linear linear discriminant analysis Mach majority-vote rule matlab matrix method multi-spectral image multiple mutual information nearest neighbor non-negative matrix factorization number of pixels obtained optimal panchromatic image parameters Patt performance pixel gray-levels pixel location pixel m,n pixel x,y principal component Proc quality measures radiometric calibration Remote Sensing robust sampling semantically equivalent sensor Shows spatial alignment spatial resolution spectral statistical sub-images sub-space techniques thresholding algorithms values weighted