title: Non-Gaussian velocity distributions integrated over space, time, and scales.
abstract: Velocity distributions are an enhanced representation of image velocity containing more velocity information than velocity vectors. In particular, non-Gaussian velocity distributions allow for the representation of ambiguous motion information caused by the aperture problem or multiple motions at motion boundaries. To resolve motion ambiguities, discrete non-Gaussian velocity distributions are suggested, which are integrated over space, time, and scales using a joint Bayesian prediction and refinement approach. This leads to a hierarchical velocity-distribution representation from which robust velocity estimates for both slow and high speeds as well as statistical confidence measures rating the velocity estimates can be computed.
authors.affiliation: Darmstadt University of Technology, Institute for Automatic Control, Control Theory and Robotics Lab, Darmstadt D-64283, Germany. volker@rtr.tu-darmstadt.de
authors.forename: Volker
authors.initials: V
authors.lastname: Willert
authors.forename: Julian
authors.initials: J
authors.lastname: Eggert
authors.forename: Jürgen
authors.initials: J
authors.lastname: Adamy
authors.forename: Edgar
authors.initials: E
authors.lastname: Körner
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008954:Models, Biological; D015233:Models, Statistical; D009068:Movement; D016011:Normal Distribution; D016008:Statistical Distributions; D013997:Time Factors; D014741:Video Recording / Q000379:methods*