Learning Real-Time Perspective Patch Rectification

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.


Published in:
International Journal of Computer Vision, 91, 1, 107--130
Year:
2011
Publisher:
Springer Verlag
ISSN:
0920-5691
Keywords:
Laboratories:




 Record created 2010-09-16, last modified 2018-01-28

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