Feature matching algorithm based on KAZE and fast approximate nearest neighbor search

DOI

10.2991/csss-14.2014.63How to use a DOI?

Keywords

Feature match; Nonlinear scale space; KAZE; The Gauge-SURF; FLANN

Abstract

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.

Copyright

© 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/).

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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  - 10.2991/csss-14.2014.63
ID  - Ze-Ping2014/06
ER  -