Computer Vision, Graphics, and Image Processing
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image selective smoothing and edge detection by nonlinear diffusion
SIAM Journal on Numerical Analysis
Performance of optical flow techniques
International Journal of Computer Vision
Digital video processing
Hierarchical Estimation and Segmentation of Dense Motion Fields
International Journal of Computer Vision
Orthonormal Vector Sets Regularization with PDE's and Applications
International Journal of Computer Vision
Terrain Analysis Using Radar Shape-from-Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vector-Valued Image Regularization with PDEs: A Common Framework for Different Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Spatial Statistics of Optical Flow
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Highly Accurate Optic Flow Computation with Theoretically Justified Warping
International Journal of Computer Vision
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Efficient and reliable schemes for nonlinear diffusion filtering
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Optical flow estimation and moving object segmentation based on median radial basis function network
IEEE Transactions on Image Processing
Object classification in 3-D images using alpha-trimmed mean radial basis function network
IEEE Transactions on Image Processing
Prediction and tracking of moving objects in image sequences
IEEE Transactions on Image Processing
Stochastic differential equations and geometric flows
IEEE Transactions on Image Processing
An image model and segmentation algorithm for reflectance confocal images of in vivo cervical tissue
IEEE Transactions on Image Processing
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This paper proposes a new optical flow smoothing methodology combining vector diffusion and robust statistics. Vector smoothing using diffusion preserves moving object boundaries and the main motion discontinuities. According to a study provided in the paper, diffusion does not remove the outliers but spreads them out, introducing a bias in the neighbourhood. In this paper robust statistics operators such as the median and alpha-trimmed mean are considered for robustifying the diffusion kernels. The robust diffusion smoothing process is extended to 3-D lattices as well. The proposed algorithms are applied for smoothing artificially generated vector fields as well as the optical flow estimated from image sequences.