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Diffusion Constructs in Optical Flow Computation

Condell, J, Scotney, BW and Morrow, PJ (2005) Diffusion Constructs in Optical Flow Computation. Journal of Electronic Imaging, 14 (3:0330). [Journal article]

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URL: http://spie.org/x648.html?product_id=666318

DOI: 10.1117/1.2039091


We develop techniques for the implementation of motion estimation. Optical flow estimation has been proposed as a preprocessing step for many high-level vision algorithms. Gradient-based approaches compute the spatio-temporal derivatives, differentiating the image with respect to time and thus computing the optical flow field. Horn and Schunck's method in particular is considered a benchmarking algorithm of gradient-based differential methods, useful and powerful, yet simple and fast. They formulated an optical flow constraint equation from which to compute optical flow, which cannot fully determine the flow but can give the component of the flow in the direction of the intensity gradient. An additional constraint must be imposed, introducing a supplementary assumption to ensure a smooth variation in the flow across the image. The brightness derivatives involved in the equation system were estimated by Horn and Schunck using first differences averaging. Gradient-based methods for optical flow computation can suffer from unreliability of the image flow constraint equation in areas of an image where local brightness function is nonlinear or where there are rapid spatial or temporal changes in the intensity function. Little and Verri suggested regularization to help the numerical stability of the solution. Usually this takes the form of smoothing of the function or surface by convolving before the derivative is taken. Smoothing has the effects of suppressing noise and ensuring differentiability of discontinuities. The method proposed is a finite element method, based on a triangular mesh, in which diffusion is added into the system of equations.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Faculty of Computing & Engineering > School of Computing and Information Engineering
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Information and Communication Engineering
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:6814
Deposited By: Professor Bryan Scotney
Deposited On:20 Jan 2010 15:52
Last Modified:15 Jun 2011 10:07

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