IEEE Transactions on Pattern Analysis and Machine Intelligence
On the estimation of optical flow: relations between different approaches and some new results
Artificial Intelligence
Mathematical Programming: Series A and B
The computation of optical flow
ACM Computing Surveys (CSUR)
Fractional differentiation for edge detection
Signal Processing - Special issue: Fractional signal processing and applications
Highly Accurate Optic Flow Computation with Theoretically Justified Warping
International Journal of Computer Vision
A second-order accurate numerical method for the two-dimensional fractional diffusion equation
Journal of Computational Physics
A comparison of three total variation based texture extraction models
Journal of Visual Communication and Image Representation
Convergence of the Grünwald-Letnikov scheme for time-fractional diffusion
Journal of Computational and Applied Mathematics
Fractional Variational Model and Algorithm for Image Denoising
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 05
Advances in Fractional Calculus: Theoretical Developments and Applications in Physics and Engineering
α scale spaces on a bounded domain
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
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We introduce variational optical flow computation involving priors with fractional order differentiations. Fractional order differentiations are typical tools in signal processing and image analysis. The zero-crossing of a fractional order Laplacian yields better performance for edge detection than the zero-crossing of the usual Laplacian. The order of the differentiation of the prior controls the continuity class of the solution. Therefore, using the square norm of the fractional order differentiation of optical flow field as the prior, we develop a method to estimate the local continuity order of the optical flow field at each point. The method detects the optimal continuity order of optical flow and corresponding optical flow vector at each point. Numerical results show that the Horn-Schunck type prior involving the n + *** order differentiation for 0 *** n is suitable for accurate optical flow computation.