Keypoint recognition using randomized trees

Keypoint recognition using randomized trees

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by Vincent Lepetit

Venue:	IEEE Trans. Pattern Anal. Mach. Intell
Citations:	87 - 15 self

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Datum	Value	Source
TITLE	Keypoint recognition using randomized trees	INFERENCE
AUTHOR NAME	Vincent Lepetit	SVM HeaderParse 0.2
AUTHOR AFFIL	Computer Vision Laboratory	SVM HeaderParse 0.2
AUTHOR ADDR	CH-1015 Lausanne, Switzerland	SVM HeaderParse 0.2
ABSTRACT	In many 3–D object-detection and pose-estimation problems, run-time performance is of critical importance. However, there usually is time to train the system, which we will show to be very useful. Assuming that several registered images of the target object are available, we developed a keypoint-based approach that is effective in this context by formulating wide-baseline matching of keypoints extracted from the input images to those found in the model images as a classification problem. This shifts much of the computational burden to a training phase, without sacrificing recognition performance. As a result, the resulting algorithm is robust, accurate, and fast-enough for frame-rate performance. This reduction in run-time computational complexity is our first contribution. Our second contribution is to show that, in this context, a simple and fast keypoint detector suffices to support detection and tracking even under large perspective and scale variations. While earlier methods require a detector that can be expected to produce very repeatable results in general, which usually is very time-consuming, we simply find the most repeatable object keypoints for the specific target object during the training phase. We have incorporated these ideas into a real-time system that detects planar, non-planar, and deformable objects. It then estimates the pose of the rigid ones and the deformations of the others.	SVM HeaderParse 0.2
VENUE	IEEE Trans. Pattern Anal. Mach. Intell	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	2006	INFERENCE
CITATIONS	4 found	ParsCit 1.0