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 implem...

One of the main technique used to recover motion analysis from two images or to register them is variational optical flow, where the pixels of one image are matched to the pixels of the second image by minimizing an energy functional. In the standard formulation of variational optical flow, the estimated motion vector field depends on the reference image and is asymmetric. However, in most appl...

Given image data of a fluid flow, the flow field, 〈u, v〉, governing the evolution of the system can be estimated using a variational approach to optical flow. Assuming that the flow field governing the advection is the symplectic gradient of a stream function or the gradient of a potential function – both falling under the category of a potential flow – it is natural to re-frame the optical flo...

We address the theoretical problems of optical ow estimation and image registration in a multi-scale framework in any dimension. We start by showing, in the translation case, that convergence to the global minimum is made easier by applying a low pass lter to the images hence making the energy \convex enough". In order to keep convergence to the global minimum in the general case, we introduce ...

Assumptions of brightness constancy and spatial smoothness underlie most optical flow estimation methods. In contrast to standard heuristic formulations, we learn a statistical model of both brightness constancy error and the spatial properties of optical flow using image sequences with associated ground truth flow fields. The result is a complete probabilistic model of optical flow. Specifical...

Abstract We perform a qualitative and quantitative analysis of various multi-frame color optical flow methods for synthetic and real panning and zooming image sequences. We show that optical flow accuracy improvement can be slightly improved if color images are available instead of gray value or saturation images. We show the usefulness of a directional regularization constraint for computing o...

Scale variation commonly arises in images/videos, which cannot be naturally dealt with by optical flow. Invariant feature matching, on the contrary, provides sparse matching and could fail for regions without conspicuous structures. We aim to establish dense correspondence between frames containing objects in different scales and contribute a new framework taking pixel-wise scales into consider...

Existing approaches to super-resolution are not applicable to videos of faces because faces are non-planar, non-rigid, non-lambertian, and are subject to self occlusion. We present super-resolution optical ow as a solution to these problems. Super-resolution optical ow takes as input a conventional video stream, and simultaneously computes both optical ow and a super-resolution version of the e...

Evaluating the performance of optical flow algorithms has been difficult because of the lack of ground truth data sets for complex scenes. We present a new method for generating motion fields from real sequences containing polyhedral objects and present a test suite for benchmarking optical flow algorithms consisting of complex synthetic sequences and real scenes with ground truth. We provide a...

This paper introduces a new algorithm for computing multiresolution optical flow, and compares this new hierarchical method with the traditional combination of the Lucas-Kanade method with a pyramid transform. The paper shows that the new method promises convergent optical flow computation. Aiming at accurate and stable computation of optical flow, the new method propagates results of computati...