Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of optical flow techniques
International Journal of Computer Vision
Digital video processing
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Orthonormal Vector Sets Regularization with PDE's and Applications
International Journal of Computer Vision
Vector-valued image regularization with PDE's: a common framework for different applications
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Image Processing
Optical flow estimation and moving object segmentation based on median radial basis function network
IEEE Transactions on Image Processing
Simultaneous structure and texture image inpainting
IEEE Transactions on Image Processing
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This paper provides a comparison study among a set of robust diffusion algorithms for processing optical flows. The proposed algorithms combine the smoothing ability of the heat kernel, modelled by the local Hessian, and the outlier rejection mechanisms of robust statistics algorithms. Smooth optical flow variation can be modelled very well using heat kernels. The diffusion kernel is considered Gaussian, where the covariance matrix implements the inverse of the local Hessian. Robust statistics operators improve the results provided by the heat kernel based diffusion, by rejecting outliers and by avoiding optical flow oversmoothing. Alpha-trimmed mean and median statistics are considered for robustifying diffusion kernels. The robust diffusion smoothing is applied onto multiple frames and is extended to 3D lattices.