A novel technique for region based motion compensation  has been developed based on applying a robust motion compensation technque using an overlapping block-based tessellation of each frame and representing region boundaries with a piece-wise B-spline model. This motion compensation technique has been integrated into a wavelet-based video compression system.

This research is aimed at estimating a set of motion trajectories over a large number of frames in an image sequence. It could be regarded as being in between traditional dense optical flow estimation and feature point tracking, the latter producing a set of sparse displacement estimates typically at object edge and corner points. This work, uses the distinctiveness of spatial texture patterns and contextual contraints of the parameterised trajectories (integrated into a Markov Random Field formulation) in order to robustly compute the trajectories which are not limited simply opf object boundaries [Gibson-Spann 1999]. Applications of this work are in motion compensated predication for video compression and optical flow estimation.

Klaus Koester - A Robust Statistical Approach to  Image Segmentation

In this research we have applied robust regression estimation techniques to the problems of range image segmentation and to the segmentation of 3D seismic data. The segmentation algorithm applies an overlapping block strategy which adapts to the local structure of the image thus effectively increasing the % breakdown ratio of the robust estimator [Koester-Spann 2000]. A statistic known as the Mutual Inlier Ratio (MIR) is used to control the local pixel/voxel clustering. This statistic is compared favourably to the traditional KS statistic for comparing distributions. For the case of the range image segmentation, an extensive experimental comparison has been carried out with a number of previously published methods using a  software tool to compute segmentation errors and results show that our method compares favourably with all other methods used in the evaluation and being particularly effective on noisy data.

E. P. Ong - Robust Optical Flow Estimation

The problem of motion estimation is one of estimating a smoothly varying optical flow field whilst being able to take into account both structure and motion boundaries leading to disconitnuities in the optical flow. We have successfully solved this problem using robust regression techniques applied to computing flow over an overlapping block tessellation [Ong-Spann 1998]. A thorough experimental evaluation of this work  has been carried out to compare it with previously published techniques and we have found that, amongst optical flow estimation algorithms that result in a dense flow field  (defined as one resulting in more than 80% spatial coverage) our algorithm outperforms all other methods tested irrespective of the error metric used.