Abstract
We propose a considerably faster approximation of the well known SIFT method. The main idea is to use efficient data structures for both, the detector and the descriptor. The detection of interest regions is considerably speed-up by using an integral image for scale space computation. The descriptor which is based on orientation histograms, is accelerated by the use of an integral orientation histogram. We present an analysis of the computational costs comparing both parts of our approach to the conventional method. Extensive experiments show a speed-up by a factor of eight while the matching and repeatability performance is decreased only slightly.
The project results have been developed in the MISTRAL Project which is financed by the Austrian Research Promotion Agency (www.ffg.at). This work has been sponsored in part by the Austrian Federal Ministry of Transport, Innovation and Technology under P-Nr. I2-2-26p VITUS2 and by the Austrian Joint Research Project Cognitive Vision under projects S9103-N04 and S9104-N04, the EC funded NOE MUSCLE IST 507572
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Grabner, M., Grabner, H., Bischof, H. (2006). Fast Approximated SIFT. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_92
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DOI: https://doi.org/10.1007/11612032_92
Publisher Name: Springer, Berlin, Heidelberg
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