Hyperplane Approximation for Template Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transformation-Invariant Clustering Using the EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Randomized Trees for Real-Time Keypoint Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Comparison of Affine Region Detectors
International Journal of Computer Vision
International Journal of Computer Vision
Geometric Hashing with Local Affine Frames
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Homography-based 2D Visual Tracking and Servoing
International Journal of Robotics Research
Surface Deformation Models for Nonrigid 3D Shape Recovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Feature harvesting for tracking-by-detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Augmented Reality: Handheld Augmented Reality involving gravity measurements
Computers and Graphics
A convolutional treelets binary feature approach to fast keypoint recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Segmentation-based tracking by support fusion
Computer Vision and Image Understanding
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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.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.