Abstract
Many vision-based applications require a robust feature descriptor that works well with image deformations such as compression, illumination, and blurring. It remains a challenge for a feature descriptor to work well with image deformation caused by viewpoint change. This paper introduces, first, a new binary feature descriptor called SYnthetic BAsis (SYBA) for feature point description and matching, and second, a method for removing non-affine features from the initial feature list to further improve the feature matching accuracy. This new approach has been tested on the Oxford dataset and a newly created dataset by comparing the feature matching accuracy using only affine features with the accuracy of using both affine and non-affine features. A statistical T-test was performed on the newly created dataset to demonstrate the advantages of using only affine feature points for matching. SYBA is less computationally complex than other feature descriptors and gives better feature matching results using affine features.
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Desai, A., Lee, DJ., Ventura, D. (2014). Matching Affine Features with the SYBA Feature Descriptor. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_43
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DOI: https://doi.org/10.1007/978-3-319-14364-4_43
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