Slow and Smooth: a Bayesian Theory for the Combination of Local Motion Signals in Human Vision

Slow and Smooth: a Bayesian theory for the ombination of of lo al motion signals in human vision

Bayesian modeling of dynamic motion integration.

The quality of the representation of an object's motion is limited by the noise in the sensory input as well as by an intrinsic ambiguity due to the spatial limitation of the visual motion analyzers (aperture problem). Perceptual and oculomotor data demonstrate that motion processing of extended objects is initially dominated by the local 1D motion cues, related to the object's edges and orthog...

Robot Motion Vision Pait I: Theory

A direct method called fixation is introduced for solving the general motion vision problem, arbitrary motion relative to an arbitrary environment. This method results in a linear constraint equation which explicitly expresses the rotational velocity in terms of the translational velocity. The combination of this constraint equation with the Brightness-Change Constraint Equation solves the gene...

Robot Motion Vision Part II: Implementation

The idea of Fixation introduced a direct method for general recovery of shape and motion from images without using either feature correspondence or optical flow [1,2]. There are some parameters which have important effects on the performance of fixation method. However, the theory of fixation does not say anything about the autonomous and correct choice of those parameters. This paper presents ...

Bayesian Motion Estimation and Segmentation Bayesian Motion Estimation and Segmentation

Estimating motion in scenes containing multiple moving objects remains a diicult problem in computer vision yet is solved eeortlessly by humans. In this thesis we present a computational investigation of this astonishing performance in human vision. The method we use throughout is to formulate a small number of assumptions and see the extent to which the optimal interpretation given these assum...