This project is a multi-disciplinary investigation of the computational principles and neural mechanisms underlying robust visual inference in primate systems and the exploitation of these principles to develop new statistics-based computer vision approaches for inferring 3D scene structures in visual images. An image of a real 3D scene is highly ambiguous and difficult to interpret because it could be generated by many possible combinations of the different physical causes, such as lighting, texture and shapes. Classical approaches in computer vision attempt 3D scene inference by modeling these image formation processes with simplified assumptions and then inverting these models. The PI proposes a statistical approach to better solve these problems by learning and exploiting the statistical priors on 3D shapes in the natural environment and their correlational structures with 2D images. The PI plans to develop efficient Bayesian belief propagation algorithms within the framework of probabilistic graphical models that allow flexible incorporation of rich statistical scene priors. The computational work will guide his investigation of the neural encoding of scene priors and the mechanisms of probabilistic inference in the primate early visual cortex using advanced electrophysiological techniques. A better understanding of the neural representations of priors and mechanisms of inference will represent a fundamental scientific advance in neuroscience and will also provide new insights for improving the statistics-based computational approaches for visual inference.
