TY  - EJOUR
DO  - 10.1007/s11263-010-0379-x
AB  - We propose two learning-based methods to patch rectification that are faster and more reliable than state-of-the-art affine region detection methods. Given a reference view of a patch, they can quickly recognize it in new views and accurately estimate the homography between the reference view and the new view. Our methods are more memory-consuming than affine region detectors, and are in practice currently limited to a few tens of patches. However, if the reference image is a fronto-parallel view and the internal parameters known, one single patch is often enough to precisely estimate an 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.
T1  - Learning Real-Time Perspective Patch Rectification
IS  - 1
DA  - 2011
AU  - Hinterstoisser, S.
AU  - Lepetit, V.
AU  - Benhimane, S.
AU  - Fua, P.
AU  - Navab, N.
JF  - International Journal of Computer Vision
SP  - 107--130
VL  - 91
EP  - 107--130
PB  - Springer Verlag
ID  - 151674
KW  - Computer Vision
KW  - Object Detection
KW  - 3D Object Detection
SN  - 0920-5691
UR  - http://infoscience.epfl.ch/record/151674/files/hinterstoisser_ijcv10.pdf
ER  -