The Gaussian scale-space paradigm and the multiscale local jet
International Journal of Computer Vision
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Summed-area tables for texture mapping
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Robust Real-Time Face Detection
International Journal of Computer Vision
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Multi-Image Matching Using Multi-Scale Oriented Patches
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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Accurate and stable identification of feature points is a requirement for such varied applications as wide-baseline stereo, object recognition and simultaneous localisation and mapping. Although a wide variety of feature extraction methods exist, certain aspects remain active areas of research. In this paper, a feature model is proposed which makes use of the summed area images in achieving scale invariance at the loss of theoretical rotational invariance. By making use of approximations to first and second derivatives, as well as the Laplacian, a wide variety of features may be obtained. Additionally, the stability of this method is increased by an improved approach to ordering of features. Evaluation is performed versus other common approaches using tests on precision, recall and information content of the extracted points.