Modeling object recognition as a Markov decision process

The field of computer vision has made significant advances over the past twenty years, yet we still have not developed a theoretical or practical understanding of how the many components of vision are combined into coherent, functioning systems. As a result, there are few applications of computer vision technology in the real world, even though the library of available computer vision techniques keeps growing. This paper models the control of visual procedures as a Markov decision problem, and presents a version of the schema learning system (SLS) that uses this model to assemble object recognition programs from existing computer vision algorithms. An example of SLS learning to recognize rooftops in aerial images is presented