Abstract
This paper presents a method called Robust Edge Aware Descriptor (READ) to compute local gradient information. The proposed method measures the similarity of the underlying structure to an edge using the 1D Fourier transform on a set of points located on a circle around a pixel. It is shown that the magnitude and the phase of READ can well represent the magnitude and orientation of the local gradients and present robustness to imaging effects and artifacts. In addition, the proposed method can be efficiently implemented by kernels. Next, we define a robust region descriptor for image matching using the READ gradient operator. The presented descriptor uses a novel approach to define support regions by rotation and anisotropical scaling of the original regions. The experimental results on the Oxford dataset and on additional datasets with more challenging imaging effects such as motion blur and non-uniform illumination changes show the superiority and robustness of the proposed descriptor to the state-of-the-art descriptors.
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Notes
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Images downloaded from http://lear.inrialpes.fr/people/mikolajczyk/.
- 3.
Available at http://www.robots.ox.ac.uk/vgg/research/affine/.
- 4.
Accessible at http://lear.inrialpes.fr/people/mikolajczyk/Database/.
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Maani, R., Kalra, S., Yang, YH. (2015). Robust Edge Aware Descriptor for Image Matching. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision – ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9003. Springer, Cham. https://doi.org/10.1007/978-3-319-16865-4_36
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