This paper describes an optic flow estimation method based on a discrete wavelet basis analysis. The differential optic flow equation is projected onto analytic wavelets. This gives small local linear systems (3-5 equations) that are solved to find the visual displacement. In this way, we solve the problems of time aliasing and aperture. Since the coefficients of the systems can be computed with filter banks, the estimation of a flow map costs O(N) operations (if one image of the sequence has N pixels). Our method also measures illumination changes. A convergence theorem is also stated and proved. 

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This paper deals with the combination of classical morphological tools and motion compensation techniques. Morphological operators have proven to be efficient for filtering and segmenting still images. For video sequences however, using motion information to modify the morphological processing is necessary. In previous work, iterative frame by frame segmentation using motion information has been developed in various forms. In this paper, motion is used at a very low level, by locally modifying the shape of the structuring element in a video sequence considered as a 3D data block. Motion adapted morphological tools are described and their use is demonstrated on video sequences. Moreover, the features of the motion model best suited to our purpose are also discussed.

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