RBRIEF: a robust descriptor based on random binary comparisons
| Autor: | Ling-Da Wu, Wei Huang, Han-Chen Song, Ying-Mei Wei |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Similarity (geometry)
Local binary patterns business.industry GLOH ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Binary number Pattern recognition Hamming distance Computer Science::Computer Vision and Pattern Recognition Computer Science::Multimedia Computer vision Computer Vision and Pattern Recognition Artificial intelligence business Rotation (mathematics) Software Image restoration Mathematics Data compression |
| Zdroj: | IET Computer Vision. 7:29-35 |
| ISSN: | 1751-9640 |
| DOI: | 10.1049/iet-cvi.2012.0087 |
| Popis: | The authors propose a robust descriptor based on BRIEF, called RBRIEF. Unlike the original BRIEF, the proposed descriptor is also robust to scale and in-plane rotation transformations. Furthermore, the authors use first derivative as sample function to do binary comparisons which has proven to be better compared against the function of intensity used in BRIEF. In the feature matching stage, the authors use Hamming distance to evaluate the descriptor similarity. As a result, the performance of the proposed descriptor outperforms SURF, BRIEF and ORB using standard benchmarks. In particular, the experiments demonstrate the proposed descriptor's superior performance in the presence of image blur, JPEG compression and light changes. Furthermore, the descriptor exhibits robust performance using only relatively few bits compared to other descriptors. |
| Databáze: | OpenAIRE |
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