Learning to estimate scenes from images
|MERL Report: ||TR99-05: William T. Freeman, Egon C. Pasztor
Advances in Neural Information Processing Systems, Vol. 11, M. S. Kearns, S. A. Solla and D. A. Cohn, eds., MIT Press, 1999
We seek the scene interpretation that best explains image data. For example, we may want to infer the projected velocities (scene) which best explain two consecutive image frames (image). From synthetic data, we model the relationship between image and scene patches, and between a scene patch and neighboring scene patches. Given a new image, we propagate likelihoods in a Markov network (ignoring the effect of loops) to infer the underlying scene. This yields an efficient method to form low-level scene interpretations. We demonstrate the technique for motion analysis and estimating high resolution images from low-resolution ones.