Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
A fast probabilistic model for hypothesis rejection in SIFT-Based object recognition
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Improving SIFT-Based object recognition for robot applications
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
A Novel Retake Detection Using LCS and SIFT Algorithm
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
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Stellar image matching allows to verify if a given pair of images belongs to the same stellar object/area, or knowing that they correspond to the same sky area, to verify if there are some changes between them due to an stellar event (supernova event, changes in the object position, etc). Some applications are stellar photometry, telescope tracking and pointing, robot telescopes, and sky monitoring. However, the matching of stellar images is a hard problem because normally the images are taken using different telescopes, image sensors and settings, as well as from different places, which produces variability in the image's resolution, orientation, and field of view. In this context, the aim of this paper is to propose a robust SIFT-based wide baseline matching technique for stellar images. The proposed technique was evaluated in a new database composed by 100 pairs of galaxies, nebulas and star clusters images, achieving a true positive rate of 87% with a false positive rate of 1.7%.