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

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S. Hinterstoisser, S. Benhimane, V. Lepetit, P. Fua, N. Navab
Simultaneous Recognition and Homography Extraction of Local Patches with a Simple Linear Classifier
British Machine Vision Conference (BMVC), Leeds (UK), September 1-4, 2008 (bib)

We show that the simultaneous estimation of keypoint identities and poses is more reliable than the two separate steps undertaken by previous approaches. A simple linear classifier coupled with linear predictors trained during a learning phase appears to be sufficient for this task. The retrieved poses are subpixel accurate due to the linear predictors. We demonstrate the advantages of our approach on real-time 3D object detection and tracking applications. Thanks to the high accuracy, one single keypoint is often enough to precisely estimate the object pose. As a result, we can deal in real-time with objects that are significantly less textured than the ones required by state-of-the-art methods.
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