Fast Approximated SIFT
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.
This demo allows to make a live extraction of features from any camera plugged to your computer. Notice that this is still a non optimized implementation and that further speed-up is possible. Thanks to Joachim Pehserl for implementation.