To the Graduate Council: I am submitting herewith a dissertation ...

Chapter 2: Related Work 16multi-sensor image registration tasks In the following section we briefly presentprevious work done in this field.2.2 Image RegistrationIntensively researched, image registration has a wide range of applications in areassuch as pattern recognition, medical imaging, and remote sensing. In the case of singlesensor registration the purpose is to combine several images in order to overcome thelimited view of the camera. Multimodal registration is mainly the step precedingfusion, where the fused information is exploited for recognition and decision. The twomain components that define registration methods are the measure of similaritybetween the images or between smaller areas of the images (mostly square windows),and the transformations modeling the mappings aligning them.In the case of single sensor registration similarity measures belonging to the class ofcorrelation measures such as normalized cross-correlation with its different variants,the sum of squared differences (SSD), and the sum of absolute differences (SAD),have been used for a long time. A method belonging to this class, but that was used formultimodal imagery, is the Correlation Ratio (CR). Roche et al [Roche98] give a goodcomparison of the CR with other multi-modal similarity measures. Some moresophisticated criteria can be built from correlation measures, such as in the work ofIrani et al [Irani98] on Infra-red and electro-optical image registration, where a globalcriterion was obtained by summing the local cross-correlation measures of smallpatches in extracted energy images. This approach does not require the globalstatistical correlation of the images, which violated in most cases of multimodalimagery, but just the local one. Beside the assumptions of the statistical relationsbetween the images correlation techniques may suffer from a flat similarity measure,requiring some sharpening through the use of edge of other feature maps.