Region and graph-based motion segmentation

Indexado ISIThis paper describes an approach for integrating motion estimation and region clustering techniques with the purpose of obtaining precise multiple motion segmentation. Motivated by the good results obtained in static segmentation we propose a hybrid approach where motion segmentation is achieved within a region-based clustering approach taken the initial result of a spatial pre-segmentation and extended to include motion information. Motion vectors are first estimated with a multiscale variational method applied directly over the input images and then refined by incorporating segmentation results into a region-based warping scheme. The complete algorithm facilitates obtaining spatially continuous segmentation maps which are closely related to actual object boundaries. A comparative study is made with some of the best known motion segmentation algorithms

Dense Image Point Matching through Propagation of Local Constraints

We present a conceptually simple algorithm for dense image point matching between two multi-modal (e.g. color) images. The algorithm is based on the assumption that correct image point matches satisfy locally a particular statistical distribution. Through an iterative evaluation of a local probability measure, global constraints are taken into account and the most likely set of image point matches is found. An advantage of this approach is that no information about the camera geometries, as for example the epipoles, has to be known. Therefore, the algorithm may be used for stereo matching and optic flow

Segmenting and tracking objects in video sequences based on graphical probabilistic models

Ph.DDOCTOR OF PHILOSOPH

Motion and emotion : Semantic knowledge for hollywood film indexing

Ph.DDOCTOR OF PHILOSOPH

Novel block-based motion estimation and segmentation for video coding

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Empirical Bayesian Motion Segmentation Abstract — We introduce an empirical Bayesian

procedure for the simultaneous segmentation of an observed motion field and estimation of the hyper-parameters of a Markov random field prior. The new approach approach exhibits the Bayesian appeal of incorporating prior beliefs, but requires only a qualitative description of the prior, avoiding the requirement for a quantitative specification of its parameters. This eliminates the need for trialand-error strategies for the determination of these parameters and leads to better segmentations. Index Terms: motion segmentation, layered representations, empirical Bayesian procedures, estimation of hyper-parameters, statistica