Hierarchical hybrid multi-scale feature match
| Autor: | Zeng-Shun Zhao, Ji-Zhen Wang, Maoyong Cao, Qing-Ji Tian, Xue-Zhen Cheng |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
business.industry
Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image registration Kanade–Lucas–Tomasi feature tracker Pattern recognition RANSAC Rectification Robustness (computer science) Computer Science::Computer Vision and Pattern Recognition Frequency domain Computer vision Artificial intelligence Invariant (mathematics) business Mathematics |
| Zdroj: | 2010 3rd International Congress on Image and Signal Processing. |
| DOI: | 10.1109/cisp.2010.5647734 |
| Popis: | Finding reliable corresponding points between two images of a scene is a fundamental problem in computer vision. In this paper, a hybrid scheme is proposed, which combines invariant spatial feature and frequency domain based methods in a hierarchical multi-scale way. The Fourier-Mellin Transform is applied to obtain the transformation parameters at the coarse level between the two images; then, the parameters can serve as the initial guess, to guide the following feature matching step at the original scale, where the correspondences are restricted in a search window determined by the warp between the reference image and the current image; Finally, the transformation parameters are refined by a RANSAC procedure. This in return provides a more accurate result for feature correspondence. Experiments show that our approach provides satisfactory feature matching performance. This method also makes precise geometric rectification to remote sensing imagery. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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