Segmentation-based motion estimation for video processing using object-based detection of motion types
Aishy Amer, Éric Dubois · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1998
In this paper, novel techniques for image segmentation and explicit object-matching-based motion estimation are presented. The principal aims of this work are to reconstruct motion-compensated images without introducing significant artifacts and to introduce an explicit object-matching and noise-robust segmentation technique which shows low computational costs and regular operations. A main feature of the new motion estimation technique is its tolerance against image segmentation errors such as the fusion or separation of objects. In addition, motion types inside recognized objects are detected. Depending on the detected object motion types either 'object/unique motion-vector' relations or 'object/several motion-vectors' relations are established. For example, in the case of translation and rotation, objects are divided into different regions and a 'region/one motion vector' relation is achieved using interpolation techniques. Further, suitability (computational cost) of the proposed methods for online applications (e.g. image interpolation) is shown. Experimental results are used to evaluate the performance of the proposed methods and to compare with block- based motion estimation techniques. In this stage of our work, the segmentation part is based on intensity and contour information (scalar segmentation). For further stabilization of the segmentation and hence the estimation process, the integration other statistical properties of objects (e.g. texture) (vector segmentation) is our current research.