Abstract
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.
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Yu, Y., Huang, K., Tan, T. (2010). A Harris-Like Scale Invariant Feature Detector. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5995. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12304-7_55
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DOI: https://doi.org/10.1007/978-3-642-12304-7_55
Publisher Name: Springer, Berlin, Heidelberg
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