In this paper we present an extension to the Bayesian formulation of multi-scale differential optical flow estimation by Simoncelli et. al.[1]. We exploit the observation that optical flow is consistent in consecutive time frames and thus propagating information over time should improve the quality of the flow estimation. This propagation is formulated via insertion of additional Kalman filters...

This paper addresses the problem of visual tracking under very general conditions: a possibly non-rigid target whose appearance may drastically change over time; general camera motion; a 3D scene; and no a priori information except initialization. This is in contrast to the vast majority of trackers which rely on some limited model in which, for example, the target's appearance is known a prior...

This paper describes a new model for extracting large-field optical flow patterns to generate distributed representations of neural activation to control complex visual tasks such as 3D egomotion. The neural mechanisms draw upon experimental findings about the response properties and specificities of cells in areas V1, MT and MSTd along the dorsal pathway. Model V1 cells detect local motion est...

This paper introduces a method for human action recognition based on optical flow motion features extraction. Automatic spatial and temporal alignments are combined together in order to encourage the temporal consistence on each action by an enhanced dynamic time warping (DTW) algorithm. At the same time, a fast method based on coarse-to-fine DTW constraint to improve computational performance ...

In describing the first-order properties of laser speckle under polarized illumination conditions, it is almost an article of faith that the contrast is unity. In many processing schemes, however, the contrast defined as the quotient of the standard deviation and the mean is calculated over a localized spatial region. In such cases, this local contrast displays a distribution of values that can...