Expectation-Maximization Technique and Spatial-Adaptation Applied to Pel-Recursive Motion Estimation
This work addresses motion estimation for video processing, but it appears incremental as it builds on existing pel-recursive methods with EM and spatial adaptation.
The paper tackled the ill-posed problem of pel-recursive motion estimation in noisy conditions by applying the Expectation-Maximization algorithm with a Gaussian data model and spatial adaptation, resulting in improved motion vector estimates as demonstrated in numerical experiments.
Pel-recursive motion estimation isa well-established approach. However, in the presence of noise, it becomes an ill-posed problem that requires regularization. In this paper, motion vectors are estimated in an iterative fashion by means of the Expectation-Maximization (EM) algorithm and a Gaussian data model. Our proposed algorithm also utilizes the local image properties of the scene to improve the motion vector estimates following a spatially adaptive approach. Numerical experiments are presented that demonstrate the merits of our method.