MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Matching with PROSAC " Progressive Sample Consensus
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Faster and Better: A Machine Learning Approach to Corner Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the international conference on Multimedia
An evaluation of image feature detectors and descriptors for robot navigation
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part II
Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking
International Journal of Computer Vision
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
| Hi-index | 0.00 |
The detection, extraction, and matching of image features is a popular method for generating point-to-point correspondences for the estimation of scene and camera geometries. In this work we evaluate the performance of a variety of feature detection algorithms over two reference data sets and a set of aerial images which includes large changes in scene illumination. The evaluated detectors showed expected performance against the reference data sets, and aerial images with constant lighting conditions, but were unsuccessful in aligning image pairs showing strong changes in image exposure and illumination.