One of the fundamental problems in modeling human vision is understanding the visual cues and computations that underlie the perception of natural visual scenes. Recent studies have suggested that there exist important aspects of scene perception which do not depend on the recognition of objects in the scene and are more global or holistic in nature. The objective of this proposal is to use integrated theoretical and experimental approaches to gain insight into the information processing that underlies the representation of natural scenes and the computation of their global and spatial layout properties. The research will be driven by the theoretical hypothesis that visual system representations at both a local and global level are adapted to the statistical structure of the natural images and scenes. This project will investigate local structure of natural images by developing hierarchical statistical models of local textures and testing to what extent human observers are sensitive to the same statistical features. The spatial structure of natural images will be investigated by developing statistical models that identify scene regions over which there are smooth changes in the local texture distribution and comparing the resulting segmentation to that of human observers. The global structure of natural scenes will be investigated by developing a statistical model that learns holistic, statistical representations, with the aim to evaluate scene depth and spatial layout information as human observers do. The broader impact of this work is that it will develop theoretical models that can be directly tested at a perceptual level and are also sufficiently detailed that they could lead to testable models of the underlying neural mechanisms. Furthermore, it will be essential to understand the computational principles underlying human perception in order to emulate their behavior in machines and also to better understand our own visual experience.
