Fusion of  Low-level Feature for FOD Classification

Zhenqi Han; Yuchun Fang; Haoyu Xu

10th EAI International Conference on Communications and Networking in China

Research Article

Fusion of Low-level Feature for FOD Classification

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.4108/eai.15-8-2015.2260872,
    author={Zhenqi Han and Yuchun Fang and Haoyu Xu},
    title={Fusion of  Low-level Feature for FOD Classification},
    proceedings={10th EAI International Conference on Communications and Networking in China},
    publisher={IEEE},
    proceedings_a={CHINACOM},
    year={2015},
    month={9},
    keywords={foreign object debris; fod classification system; scale-invariant feature transform;mixed feature; support vector machine; nearest neighbor},
    doi={10.4108/eai.15-8-2015.2260872}
}

Zhenqi Han
Yuchun Fang
Haoyu Xu
Year: 2015
Fusion of Low-level Feature for FOD Classification
CHINACOM
IEEE
DOI: 10.4108/eai.15-8-2015.2260872

Zhenqi Han¹, Yuchun Fang², Haoyu Xu¹^,*

1: Shanghai Advanced Research Institute, Chinese Academy of Sciences
2: Shanghai University

*Contact email: xuhy@sari.ac.cn

Abstract

In this paper, we propose a novel framework of Foreign Object Debris (FOD) classification combining scale-invariant feature transform (SIFT) feature and color feature. This system contains FOD detection subsystem, image quality assessment, control center and FOD recognition subsystem. The system not only achieves the goal of FOD detection, but also fulfills the task of FOD classification. We propose a mixed feature method that combines SIFT feature and color feature to extract FOD feature and use Support vector machine (SVM) or nearest neighbor (NN) to classify FOD image. Experiment results show that the proposed framework is effective and accurate.

Keywords: foreign object debris; fod classification system; scale-invariant feature transform;mixed feature; support vector machine; nearest neighbor

Published: 2015-09-21
Publisher: IEEE

: http://dx.doi.org/10.4108/eai.15-8-2015.2260872