Feature matching algorithm based on KAZE and fast approximate nearest neighbor search
- https://doi.org/10.2991/csss-14.2014.63How to use a DOI?
- Feature match; Nonlinear scale space; KAZE; The Gauge-SURF; FLANN
This paper proposed a feature matching algorithm based on KAZE and fast approximate nearest neighbor search for that SIFT and SURF feature detection algorithm,extracting features by Gaussian pyramid in the linear scale space has the problems of fuzzy boundaries, detail missing, and low feature points matching rate.First, this algorithm uses Additive Operator Splitting(AOS) method for nonlinear diffusion filtering,and structure nonlinear scale space.Then use Hessian matrix to detect feature points, and construct the Gauge-SURF(G-SURF) descriptions in the Gauge coordinate. Finally adopt fast approximate nearest neighbor search algorithm to match feature points, and use RANSAC algorithm to eliminate false matching points. Experiments show that this algorithm ensures the real-time nature and improves the feature matching rate.
- © 2014, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Cai Ze-Ping AU - Xiao De-Gui PY - 2014/06 DA - 2014/06 TI - Feature matching algorithm based on KAZE and fast approximate nearest neighbor search BT - Proceedings of the 3rd International Conference on Computer Science and Service System PB - Atlantis Press SP - 270 EP - 273 SN - 1951-6851 UR - https://doi.org/10.2991/csss-14.2014.63 DO - https://doi.org/10.2991/csss-14.2014.63 ID - Ze-Ping2014/06 ER -