On Modelling Location Uncertainty in Images: A Coding Perspective

Friday, February 2, 2001 - 8:30am - 9:30am
Lind 400
Michael Orchard (Rice University)
Virtually all image processing algorithms assume an underlying probability distribution on the space of images which guides it in transforming an original (e.g. uncoded, noisy, degraded, etc.) image into the target (e.g. coded, denoised, restored, etc.) image. The unknown locations of events in the scene are perhaps the most important form of uncertainty characterized by this underlying probability distribution. The importance of location uncertainty can be recognized both in the processes by which natural images are generated, and in the way humans perceive those images.

This talk uses the image coding perspective to explore how to accurately model image probability in light of the importance of location uncertainty. Through examples and thought experiments we show that current image coding algorithms lack tools to efficiently characterize location uncertainty. We show that location uncertainty requires that the probability of natural images be aligned to nonlinear manifolds in image space. This talk describes work in progress, and audience discussion and participation is encouraged.