The present paper extends a known e cient t e c hnique for rigid 3D motion estimation so as to make it applicable to motion estimation problems occuring in image sequence coding applications. The known technique estimates 3D motion using previously evaluated 3D correspondence. However, in image sequence coding applications 3D correspondence is unknown and only 2D motion vectors are usually initially available. The novel neural network (NN) introduced in this paper uses initially estimated 2D motion vectors to estimate 3D rigid motion and is therefore suitable for image sequence coding applications. Moreover, it is shown that the NN introduced in this paper performs extremely well even in cases where 3D correspondence is known with accuracy. Experimental results are presented for the evaluation of the proposed scheme.
I IntroductionObject based coding has long attracted considerable attention as a promising alternative to block-based encoding, achieving excellent performance, and producing fewer blocking artifacts than those commonly occuring in block-based hybrid DCT coders at moderate and low bit rates 1, 2]. In addition, the ability o f object-based coding techniques to describe a scene in a structural way, i n c o n trast to traditional waveformbased coding techniques, opens new areas of applications 3]. Video production, realistic computer graphics, multimedia interfaces and medical visualization are some of the applications that may b e n e t by exploiting the potential of object based schemes.Object based codecs consist of an analysis part in which image pixels are grouped into objects described by their shape (2D silhouette and 3D structure), 3D motion and texture. Objects are then synthesized at the decoder using the transmitted parameters 1].In all model-based and object-based image sequence coding schemes, motion estimation and motion This work was partly supported by the EU CEC ACTS projects VIDAS and PANORAMA.
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