PublicationDetail

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

S. Holzer, S. Ilic, D. J. Tan , N. Navab
Efficient Learning of Linear Predictors using Dimensionality Reduction (Oral)
Asian Conference on Computer Vision (ACCV), Korea, Daejeon, November 2012 (bib)

Using Linear Predictors for template tracking enables fast and reliable, real-time processing. However, not being able to learn new templates online limits their use in applications where the scene is not known a priori and multiple templates have to be added online, such as SLAM or SfM?. This especially holds for applications running on low-end hardware such as mobile devices. For previous approaches, Linear Predictors had to be either learned offine [1] or by starting with a small template and iteratively growing it over time [2]. We propose a fast and simple learning procedure which reduces the necessary training time by up to two orders of magnitude while also slightly improving the tracking robustness with respect to large motions and image noise. This is demonstrated in an exhaustive evaluation where we compare our approach with state-of-the-art. Additionally, we show the learning and tracking in mobile phone applications which demonstrates the effciency of the proposed approach.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.



Edit | Attach | Refresh | Diffs | More | Revision r1.11 - 19 Jul 2016 - 16:26 - NassirNavab

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif