Additive update predictors in active appearance models

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Active Appearance Model (AAM) provides an efficient method for localizing objects that vary in both shape and texture, and uses a linear regressor to predict updates to model parameters based on current image residuals. This study investigates using additive (or 'boosted') predictors, both linear and non-linear, as a substitute for the linear predictor in order to improve accuracy and efficiency. We demonstrate: (a) a method for training additive models that is several times faster than the standard approach without sacrificing accuracy; (b) that linear additive models can serve as an effective substitute for linear regression; (c) that linear models are as effective as non-linear models when close to the true solution. Based on these observations, we compare a 'hybrid' AAM to the standard AAM for both the XM2VTS and BioID datasets, including cross-dataset evaluations. © 2010. The copyright of this document resides with its authors.

Bibliographical metadata

Original languageEnglish
Title of host publicationBritish Machine Vision Conference, BMVC 2010 - Proceedings|Br. Mach. Vis. Conf., BMVC - Proc.
PublisherBMVA Press
DOIs
Publication statusPublished - 2010
Event2010 21st British Machine Vision Conference, BMVC 2010 - Aberystwyth
Event duration: 1 Jul 2010 → …

Conference

Conference2010 21st British Machine Vision Conference, BMVC 2010
CityAberystwyth
Period1/07/10 → …