Comutations underlying the measuremnt of visual motion.
Artificial Intelligence
On the estimation of optical flow: relations between different approaches and some new results
Artificial Intelligence
Bayesian Estimation of Motion Vector Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital video processing
Computer Vision and Image Understanding
Handbook of Image and Video Processing
Handbook of Image and Video Processing
Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object-based estimation of dense motion fields
IEEE Transactions on Image Processing
Regularization of optic flow estimates by means of weighted vector median filtering
IEEE Transactions on Image Processing
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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.