Mosaicing Video Sequences

With the advent of cheap, but relatively low-resolution, video sensors, the importance of automatically mosaicing many small video images to one large image has increased. This is due mainly to the many useful applications that may be based on such technology, such as hand-held mobile scanning, multi-resolution imaging, panoramic spreads and video compression. The process of creating a mosaiced image consists of first finding the geometric registration of each small image from the sequence to a global image plane, and then combining all the registered images into one smooth, pleasing image. We review two existing methods for finding the registration: sequential and canvas mosaicing, and discuss the advantages and disadvantages of each of those algorithms. After suggesting two methods to improve the reliability and the accuracy of the basic sequential algorithm, we present our algorithm, which combines and enhances the sequential and canvas methods. We demonstrate how our algorithm overcomes most of the pitfalls of each of the two simpler algorithms. Finally, we present a novel approach to combining the registered images utilizing both geometrical and content information in order to improve the visual quality of the mosaic.