Statistical correlations between two-dimensional images and three-dimensional structures in natural scenes.
In spite of the recent surge in the popularity of statistical approaches to vision, the joint statistics of coregistered range and light-intensity images have gone relatively unexplored. We investigate statistical correlations between images and the surface shapes that produced them. We determine which linear properties of range images can be best predicted from simple computations on intensity information, and we determine those properties of intensity images that best predict range information. We find that significant (up to p = 0.45) and potentially exploitable correlations exist between linear properties of range and intensity images, and we explore the structure of these correlations.
Potetz
Brian
B
Department of Computer Science, Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA. bpotetz@cs.cmu.edu
Lee
Tai Sing
TS