Learning Motion Analysis
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| MERL Report: | TR2000-32 : William T. Freeman, John A. Haddon, Egon C. Pasztor |
Where Published: 'Statistical Theories of the Brain', MIT Press, edited by R. Rao, B. Olshausen and M. Lewicki, 2001
We seek a learning-based algorithm that applies to various low-level vision problems. For a given problem, we want to find the scene interpretation that best explains image data. Specializing to the optical flow problem, we may want to infer the projected velocities (scene) which best explain two consecutive image frames (image). We first present the results of this method for a toy world of irregularly shaped blobs. Then we extend the technique to function on more realistic images, showing reasonable results.
