Thesis evans surf

Hessian matrix-based interest points Surf uses the Hessian matrix because of its good performance in computation time and accuracy. Rather than using a different measure for selecting the location and the scale (Hessian-Laplace detector), surf relies on the determinant of the Hessian matrix for both.

In order to be invariant to rotation, surf tries to identify a reproducible orientation for the interest points. For achieving this: Surf first calculate the Haar-wavelet responses in x and y-direction, and this in a circular neighborhood of radius 6s around the keypoint, with s the scale at which the keypoint was detected.

The SURF framework described in this paper is based on the Ph.D. thesis of H. Bay [ETH Zurich, 2009], and more specifically on the paper co-written by H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool. This is part of a 7-series Feature Detection and Matching.

Introduction to SURF (Speeded-Up Robust Features) The SURF method (Speeded Up Robust Features) is a fast and robust algorithm for local, similarity invariant representation and comparison of images. The main interest of the SURF approach lies in its fast computation of operators using box filters, thus enabling real-time applications such as ...