Small: Statistical Measurement, Modeling, and Inference on Natural 3D Scenes

University Of Texas At Austin, Austin TX

Investigators

Abstract

This project investigates two deeply commingled and significant scientific questions on the statistical distributions of range, disparity, chrominance and luminance in natural 3D images of the world: (1) developing a comprehensive database of co-registered luminance, chrominance, range, and disparity images of natural scenes; and (2) conducting eye movement studies on stereoscopic images.. On the acquired database, the research team studies and models the bivariate statistics of luminance, chrominance, range, and disparity . In the eye movement studies, the locations of visual fixations are measured as they land in range space against where they land in luminance, chromatic, and disparity space, making it possible to develop gaze-contingent models of the statistics of luminance, chrominance, range, and disparity. The results of these studies have broad significance in vision science and image processing. To exemplify this, new approaches to computational stereo and to stereo image quality assessment are developed. New computational stereo algorithms are developed using appropriate prior and posterior distribution models on disparity. Further, new algorithms are developed for stereopair image quality assessment using the statistical models that we will develop. These new algorithms dramatically impact the emerging 3-D digital cinema, gaming, and television industries, allowing for automatic assessment of 3D presentations to human viewers. The developed 3D range-luminance databases are made available via public web portals, and the results of the work are published in the highest-profile vision science and image science journals.