We propose two learning-based methods to patch rectification that are faster and more reliable than state-ofthe- 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.Ourmethods aremorememoryconsuming than affine region detectors, and are in practice currently limited to a few ten 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. The first method favors fast run-time performance while the second one is designed for fast real-time learning and robustness, however they follow the same general approach: First, a classifier provides for every keypoint a first estimate of its transformation. Then, the estimate allows carrying out an accurate perspective rectification using linear predictors. The last step is a fast verification—made possible by the accurate perspective rectification—of the patch identity and its sub-pixel precision position estimation.We demonstrate the advantages of our approach on real-time 3D object detection and tracking applications.     «

We propose two learning-based methods to patch rectification that are faster and more reliable than state-ofthe- 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.Ourmethods aremorememoryconsuming than affine region detectors, and are in practice currently limited to a few ten patches. However, if the reference image is a fronto-parallel view...     »