ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Recursive implementation of the Gaussian filter
Signal Processing
Scale-Space Theory in Computer Vision
Scale-Space Theory in 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
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Evaluation of Features Detectors and Descriptors based on 3D Objects
International Journal of Computer Vision
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A sparse curvature-based detector of affine invariant blobs
Computer Vision and Image Understanding
Hessian-Based Affine Adaptation of Salient Local Image Features
Journal of Mathematical Imaging and Vision
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Local feature detectors that make use of derivative based saliency functions to locate points of interest typically require adaptation processes after initial detection in order to achieve scale and affine covariance. Affine adaptation methods have previously been proposed that make use of the second moment matrix to iteratively estimate the affine shape of local image regions. This paper shows that it is possible to use the Hessian matrix to estimate local affine shape in a similar fashion to the second moment matrix. The Hessian matrix requires significantly less computation effort to compute than the second moment matrix, allowing more efficient affine adaptation. It may also be more convenient to use the Hessian matrix, for example, when the Determinant of Hessian detector is used. Experimental evaluation shows that the Hessian matrix is very effective in increasing the efficiency of blob detectors such as the Determinant of Hessian detector, but less effective in combination with the Harris corner detector.