Performance of optical flow techniques
International Journal of Computer Vision
Determination of optical flow and its discontinuities using non-linear diffusion
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Reliable Estimation of Dense Optical Flow Fields with Large Displacements
International Journal of Computer Vision
A Probabilistic Approach to Optical Flow based Super-Resolution
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Constraints for the estimation of displacement vector fields from image sequences
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
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This paper deals with the computation of dense image correspondences and the detection of occlusion. We propose a Bayesian approach to the image registration problem. The images are regarded as noisy measurements of an underlying 'true' image-function. Additionally, the image data is considered incomplete, in the sense that we do not know which pixels from a particular image are occluded in the other images. We describe an EM-algorithm, which iterates between estimating values for all hidden quantities, and optimizing the optical flow by differential techniques. The Bayesian way of describing the problem leads to more insight in existing differential approaches, and offers some natural extensions to them. The resulting system involves less parameters and gives an interpretation to the remaining ones. An important feature is the photometric detection of occluded pixels.