SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Robust Image Corner Detection Through Curvature Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Camera Calibration and 3D Reconstruction Using Interval Analysis
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Real-Time Localisation and Mapping with Wearable Active Vision
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Visual Modeling with a Hand-Held Camera
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
Unsupervised 3D Object Recognition and Reconstruction in Unordered Datasets
3DIM '05 Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling
Combining Local and Global Image Features for Object Class Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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Image feature detection is a fundamental issue in computer vision. SIFT[1] and SURF[2] are very effective in scale-space feature detection, but their stabilities are not good enough because unstable features such as edges are often detected even if they use edge suppression as a post-treatment. Inspired by Harris function[3], we extend Harris to scale-space and propose a novel method - Harris-like Scale Invariant Feature Detector (HLSIFD). Different to Harris-Laplace which is a hybrid method of Harris and Laplace, HLSIFD uses Hessian Matrix which is proved to be more stable in scale-space than Harris matrix. Unlike other methods suppressing edges in a sudden way(SIFT) or ignoring it(SURF), HLSIFD suppresses edges smoothly and uniformly, so fewer fake points are detected by HLSIFD. The approach is evaluated on public databases and in real scenes. Compared to the state of arts feature detectors: SIFT and SURF, HLSIFD shows high performance of HLSIFD.