Image registration is the process of establishing correspondences between two or more images taken at different times, from different viewpoints, under different lighting conditions, and/or by different sensors, and aligning them with respect to a coordinate system that is coherent with the three dimensional structure of the scene. Once feature correspondences have been established and the geometric alignment has been performed, the images are combined to provide a representation of the scene that is both geometrically and photometrically consistent. This last process is known as image mosaicking. The primary contribution of this research is the development of computational frameworks that tackle in a general and principled way the problems arising in the construction of an image registration and mosaicking system. Specifically, we present a general theory to detect image point features that are suitable for matching. Our theory generalizes and extends much of the previous work on detecting feature locations. We introduce a novel, physically motivated curve/region descriptor suitable to establish image correspondences in a geometrically invariant fashion. New methods to estimate robustly the image transformation parameters in presence of large quantities of outliers and of multiple models are also presented. Finally we present a fully automated registration and mosaicking system that can produce seamless mosaics from image pairs. Extensive experimental results with biological images, satellite images and consumer photographs are presented.