Randomized Trees for Real-Time Keypoint Recognition

In earlier work, we proposed treating wide baseline matching of feature points as a classification problem, in which each class corresponds to the set of all possible views of such a point. We used a K-mean plus Nearest Neighbor classifier to validate our approach, mostly because it was simple to implement. It has proved effective but still too slow for real-time use. In this paper, we advocate instead the use of randomized trees as the classification technique. It is both fast enough for real-time performance and more robust. It also gives us a principled way not only to match keypoints but to select during a training phase those that are the most recognizable ones. This results in a real-time system able to detect and position in 3D planar, non-planar, and even deformable objects. It is robust to illuminations changes, scale changes and occlusions.

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Vincent Lepetit, Pascal Lagger, Pascal Fua
• Keywords: Computer Vision, CVPR 2005, Illuminations Changes, Nearest Neighbor Classifier, Real-time Performance, Real-time Use, Wide Baseline Matching