Abstract for williams_cvpr06

Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2006


Oliver Williams, Andrew Blake, Roberto Cipolla


This paper is about mapping images to continuous output spaces using powerful Bayesian learning techniques. A sparse, semi-supervised Gaussian process regression model (S³GP) is introduced which learns a mapping using only partially labelled training data. We show that sparsity bestows efficiency on the S³GP which requires minimal CPU utilization for real-time operation; the predictions of uncertainty made by the S³GP are more accurate than those of other models leading to considerable performance improvements when combined with a probabilistic filter; and the ability to learn from semi-supervised data simplifies the process of collecting training data. The S³GP uses a mixture of different image features: this is also shown to improve the accuracy and consistency of the mapping. A major application of this work is its use as a gaze tracking system in which images of a human eye are mapped to screen coordinates: in this capacity our approach is efficient, accurate and versatile.

(ftp:) williams_cvpr06.pdf (http:) williams_cvpr06.pdf

If you have difficulty viewing files that end '.gz', which are gzip compressed, then you may be able to find tools to uncompress them at the gzip web site.

If you have difficulty viewing files that are in PostScript, (ending '.ps' or '.ps.gz'), then you may be able to find tools to view them at the gsview web site.

We have attempted to provide automatically generated PDF copies of documents for which only PostScript versions have previously been available. These are clearly marked in the database - due to the nature of the automatic conversion process, they are likely to be badly aliased when viewed at default resolution on screen by acroread.