Abstract
This article proposes a Robust E-hog for Feature Analysis (REFA) to describe interest points and their neighborhood. Initially the two most used methods: SIFT and SURF are studied and various advantages (invariances, repeatability) are extracted to create a new approach (detection, description and matching). First, the Fast-Hessian detector is used because it gives the best repeatability rate, however it will be optimized. Secondly the local neighborhood description is based on a histogram of oriented gradients on an elliptical shape. Finally a decision tree, validation threshold and deletion duplicates are used to match interest points. This method must also be as robust as possible for image transformations (rotations, scales, viewpoints for example). All tool parameters (orientations, thresholds, analysis shape) will be also detailed in this article.
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Grand-brochier, M., Tilmant, C., Dhome, M. (2013). REFA: A Robust E-HOG for Feature Analysis for Local Description of Interest Points. In: Csurka, G., Kraus, M., Mestetskiy, L., Richard, P., Braz, J. (eds) Computer Vision, Imaging and Computer Graphics. Theory and Applications. VISIGRAPP 2011. Communications in Computer and Information Science, vol 274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32350-8_14
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DOI: https://doi.org/10.1007/978-3-642-32350-8_14
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