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Determining Optical Flow
 ARTIFICIAL INTELLIGENCE
, 1981
"... Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. A method for finding the optical flow pattern is presented which assumes that the apparent veloc ..."
Abstract

Cited by 1736 (7 self)
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Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. A method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image. An iterative implementation is shown which successfully computes the optical flow for a number of synthetic image sequences. The algorithm is robust in that it can handle image sequences that are quantized rather coarsely in space and time. It is also insensitive to quantization of brightness levels and additive noise. Examples are included where the assumption of smoothness is violated at singular points or along lines in the image.
Estimation of Image Motion in Scenes Containing Multiple Moving Objects
, 1995
"... This thesis is concerned primarily with the development of algorithms for estimating and segmenting image motion fields that contain discontinuities. An errorweighted regularization algorithm for image motion field estimation is proposed as a computationally attractive alternative to stochastic opt ..."
Abstract
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This thesis is concerned primarily with the development of algorithms for estimating and segmenting image motion fields that contain discontinuities. An errorweighted regularization algorithm for image motion field estimation is proposed as a computationally attractive alternative to stochastic optimization based schemes. Block matching errors in the local motion measurement process are used in the regularization functional in order to avoid oversmoothing across motion boundaries. A second algorithm, anisotropic regularization, improves on the local measurement process, by employing alternative matching criteria and matching window organization. A selective confidence measure derived from anisotropic local measurements is used to further improve the errorweighted regularization.