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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 879675, 12 pages
http://dx.doi.org/10.1155/2015/879675
Research Article

Automatic Finger Interruption Detection in Electroluminescence Images of Multicrystalline Solar Cells

1Department of Computer Science and Information Engineering, National Central University, No. 300, Jhongda Road, Jhongli 32001, Taiwan
2Department of Electronic Engineering, Chien Hsin University of Science and Technology, No. 229, Jianxing Road, Jhongli 32097, Taiwan

Received 12 December 2014; Accepted 11 February 2015

Academic Editor: Mo Li

Copyright © 2015 Din-Chang Tseng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This study provides an automatic method for detecting finger interruptions in electroluminescence (EL) images of multicrystalline solar cells. The proposed method is a supervised classification method. We obtain regions of interest (ROI) by separating the EL image to several regions. The fingers within each ROI are candidates for defect detection. We horizontally scan each ROI region and extract features from each finger pixel. In the training stage, we record a set of features which are extracted from interrupted fingers and noninterrupted fingers. These features are represented as points in a spectral embedding space produced by spectral clustering method. These points will be classified into two clusters: interrupted fingers and noninterrupted fingers. In the classification stage, we firstly detect the position of fingers in an EL image and obtain features from each finger. The set of features in each finger combined with known features in the training stage will be represented as points in the spectral embedding space and then will be classified to the cluster with nearer cluster centroid of known features. Experimental results show that the proposed method can effectively detect finger interruptions on a set of EL images of various solar cells.