A low complexity approximation of probabilistic appearance models

摘要

Appearance models yield a compact representation of shape, pose and illumination variations. The probabilistic appearance model, introduced by Moghaddam et al. (Proceedings of the International Conference on Computer Vision, Cambridge, MA, June 1995, p. 687; IEEE Trans. Pattern Anal. Mach. Intell. 19 (7) (1997) 696) has recently shown excellent performances in pattern detection and recognition, outperforming most other linear and non-linear approaches. Unfortunately, the complexity of this model remains high. In this paper, we introduce an efficient approximation of this model, which enables fast implementations in statistical estimation-based schemes. Gains in complexity and cpu time of more than 10 have been obtained, without any loss in the quality of the results.