Enhanced Approximated SURF Model For Object Recognition

ABSTRACT Computer vision applications like camera calibration, 3D reconstruction, and object recognition and image registration are becoming widely popular now a day. In this paper an enhanced model for speeded up robust features (SURF) is proposed by which the object recognition process will become three times faster than common SURF model 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