Learning Low-Level Vision

"Learning Low-Level Vision," Freeman, IJCV, 2000.

The paper describes a learning-based method for low-level vision problems, such as motion analysis, inferring shape and reflectance from a photograph, or extrapolating image detail, that is, how to estimate scenes from images is the goal. Given training sets, they succeed in enumerating a coarse sampling of all input patch values by preprocessing or restricting to some classes. Breaking the scenes into a Markov network, the algorithm can find the optimal scene explanation if given any image data. It shows that applying machine learning methods has the benefits to problems of visual interpretations.