Image Fusion

This text provides a comprehensive introduction to the theories, techniques and applications of image fusion. It examines in detail many real-life examples of image fusion, including panchromatic sharpening and ensemble color image segmentation.

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 [1] 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.

Self-contained, easy accessible introduction to the hot area of image fusion Presents the most important topics in image fusion at a reasonable size Well-organized modern approach to theories and techniques, includes numerous case studies that illustrate the application of techniques for image fusion to real problems, providing hands-on knowledge Contains details of MATLAB software programs which are available for all the image fusion techniques used in the book Includes extensive modern bibliography

Inhalt
Image Sensors.- I: Theories.- Common Representational Format.- Spatial Alignment.- Semantic Equivalence.- Radiometric Calibration.- Pixel Fusion.- II: Techniques.- Multi-resolution Analysis.- Image Sub-space Techniques.- Ensemble Learning.- Re-sampling Methods.- Image Thresholding.- Image Key Points.- Image Similarity Measures.- Vignetting, White Balancing and Automatic Gain Control Effects.- Color Image Spaces.- Markov Random Fields.- Image Quality.- III: Applications.- Pan-sharpening.- Ensemble Color Image Segmentation.- STAPLE: Simultaneous Truth and Performance Level Estimation.- Biometric Technologies.