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)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Finding paths through the world's photos
ACM SIGGRAPH 2008 papers
Vision-based global localization and mapping for mobile robots
IEEE Transactions on Robotics
Adaptive coded aperture photography
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Reliable image matching with recursive tiling
PIA'11 Proceedings of the 2011 ISPRS conference on Photogrammetric image analysis
Feature grouping and local soft match for mobile visual search
Pattern Recognition Letters
Binary SIFT: towards efficient feature matching verification for image search
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Bag of spatio-visual words for context inference in scene classification
Pattern Recognition
Evaluation of feature detectors for registering aerial images
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
A Hierarchical Tracking Strategy for Vision-Based Applications On-Board UAVs
Journal of Intelligent and Robotic Systems
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Recent years have seen an explosion in the use of invariant keypoint methods across nearly every area of computer vision research. Since its introduction, the scale-invariant feature transform (SIFT) has been one of the most effective and widely-used of these methods and has served as a major catalyst in their popularization. In this paper, I present an open-source SIFT library, implemented in C and freely available at http://eecs.oregonstate.edu/~hess/sift.html, and I briefly compare its performance with that of the original SIFT executable released by David Lowe.