Study on Improving Image Feature Points Detection and Matching Accuracy in Binocular Vision System

DOI

10.2991/iiicec-15.2015.150How to use a DOI?

Keywords

Image Matching; Feature Points Detection; SIFT Algorithm; Binocular Vision

Abstract

Image feature points detection and matching is the key to binocular vision system performance. The paper is to improve its matching accuracy. In the experiments, Harris algorithm, Susan algorithm and CSS algorithm were used on the same image to extract feature points. Compared with each other, three methods showed different advantages in terms of extracting feature points. And two methods were carried out in the feature points matching process, one method was based on Harris feature points detection while another method was based on SIFT algorithm. The results showed that SIFT algorithm had better matching effect, but matching accuracy remained to be further improved. As a result, we extended the search scope of the extreme points in DoG scale space of the SIFT algorithm and removed feature points around image boundary. Though the number of the detected points changed little, but its detecting accuracy was more reliable. Compared with the effect of traditional SIFT algorithm, the matching accuracy has been significantly improved.

Copyright

© 2015, 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/).

Download article (PDF)

TY  - CONF
AU  - Gang Tong
AU  - Changcheng Wang
AU  - Pan Wang
PY  - 2015/03
DA  - 2015/03
TI  - Study on Improving Image Feature Points Detection and Matching Accuracy in Binocular Vision System
BT  - Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference
PB  - Atlantis Press
SP  - 660
EP  - 663
SN  - 2352-538X
UR  - https://doi.org/10.2991/iiicec-15.2015.150
DO  - 10.2991/iiicec-15.2015.150
ID  - Tong2015/03
ER  -