Monocular and Stereo Methods for AAM Learning from video

Jason Sarigih, Roland Goecke

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

15 Citations (Scopus)
37 Downloads (Pure)


The active appearance model (AAM) is a powerful method for modeling deformable visual objects. One of the major drawbacks of the AAM is that it requires a training set of pseudo-dense correspondences over the whole database. In this work, we investigate the utility of stereo constraints for automatic model building from video. First, we propose a new method for automatic correspondence finding in monocular images which is based on an adaptive template tracking paradigm. We then extend this method to take the scene geometry into account, proposing three approaches, each accounting for the availability of the fundamental matrix and calibration parameters or the lack thereof. The performance of the monocular method was first evaluated on a pre-annotated database of a talking face. We then compared the monocular method against its three stereo extensions using a stereo database
Original languageEnglish
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
EditorsRamin Zabih
Place of PublicationAustralia
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Print)1424411807, 1424411793
Publication statusPublished - 2007
Externally publishedYes
EventCVPR 2007 - Minneapolis, United States
Duration: 18 Jun 200723 Jun 2007


ConferenceCVPR 2007
Country/TerritoryUnited States


Dive into the research topics of 'Monocular and Stereo Methods for AAM Learning from video'. Together they form a unique fingerprint.

Cite this