Segmentation in the loop: an iterative object-based algorithm for motion estimation

Abstract

Motion estimation algorithms are a key component for multimedia systems and optimization of these algorithms is still a topic of current research. Promising approaches try to integrate into the motion estimation process besides pure grey level similarities further types of information, contained in the image. Due to the moderate quality of this additional information the integration has to be performed rather conservatively in order to reduce the risk of an even dramatic degradation of the vector field quality in some cases. Up to now there is no robust algorithm available, which yields a noticeable improvement for all types of motion and image scenes, without causing a loss of quality in critical situations. Within the scope of this contribution the application of high performance segmentation for the enhancement of motion vector fields is analyzed. Starting from these results a new iterative concept for object based motion estimation is developed, which combines the results of a classic motion estimation with the information of image segmentation and features a high robustness against segmentation errors. The results of this new algorithm are analyzed on the basis of different objective evaluation criterions and compared to classic motion estimation algorithms.