PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Fast Learning of Sprites using Invariant Features
Moray Allan, Michalis Titsias and Christopher Williams
In: British Machine Vision Conference 2005, 5-8 September 2005, Oxford, UK.


A popular framework for the interpretation of image sequences is the layers or sprite model of e.g. Wang and Adelson (1994), Irani et al. (1994). Jojic and Frey (2001) provide a generative probabilistic model framework for this task, but their algorithm is slow as it needs to search over discretized trans- formations (e.g. translations, or affines) for each layer. In this paper we show that by using invariant features (e.g. Lowe's SIFT features) and clustering their motions we can reduce or eliminate the search and thus learn the sprites much faster. We demonstrate our algorithm on two image sequences.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:1092
Deposited By:Moray Allan
Deposited On:22 September 2005