An Algorithm for Motion Parameter Direct Estimate
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An Algorithm for Motion Parameter Direct Estimate Roberto Caldelli Dipartimento di Elettronica e Telecomunicazioni, Universit`a di Firenze, Via S. Marta 3, 50139 Firenze, Italy Email: [email protected]
Franco Bartolini Dipartimento di Elettronica e Telecomunicazioni, Universit`a di Firenze, Via S. Marta 3, 50139 Firenze, Italy Email: [email protected]
Vittorio Romagnoli Dipartimento di Elettronica e Telecomunicazioni, Universit`a di Firenze, Via S. Marta 3, 50139 Firenze, Italy Email: [email protected] Received 29 January 2003; Revised 5 September 2003 Motion estimation in image sequences is undoubtedly one of the most studied research fields, given that motion estimation is a basic tool for disparate applications, ranging from video coding to pattern recognition. In this paper a new methodology which, by minimizing a specific potential function, directly determines for each image pixel the motion parameters of the object the pixel belongs to is presented. The approach is based on Markov random fields modelling, acting on a first-order neighborhood of each point and on a simple motion model that accounts for rotations and translations. Experimental results both on synthetic (noiseless and noisy) and real world sequences have been carried out and they demonstrate the good performance of the adopted technique. Furthermore a quantitative and qualitative comparison with other well-known approaches has confirmed the goodness of the proposed methodology. Keywords and phrases: motion parameter estimation, MAP criterion, Markov random fields, iterated conditional mode, motion models.
1.
INTRODUCTION
Estimation of motion fields and their segmentation are still an important task to be solved; in disparate applications ranging from pattern recognition to image sequence analysis, passing through object tracking and video coding, determining trajectories and positions of objects composing the scene is mandatory, and much effort has been spent in researching and devising a robust solution to adequately and satisfactory address this problem. Though for human visual system (HVS), motion recognition is effortless, the same thing cannot be assessed for computer-aided estimation. This is mainly due to the complex relationship existing between the movements of objects in a 3D scene and the apparent motion of brightness pattern in a sequence of 2D projections of the scene. Information about depth is lost and what appears as motion in the image plane can actually be determined by other phenomena, such as changes in scene illumination and shadowing effects. Furthermore, motion recognition is also hard to obtain because of some application hurdles, as the aperture problem [1] and regions occlusion; and although
many algorithms and valuable approaches have been developed, this issue cannot be considered as completely investigated yet [2, 3, 4]. Different are the approaches to motion estimation task. One of the most well-known consists of representing motion fields by assigning independent motion vectors to each image
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