Share Email Print
cover

Proceedings Paper

Research on feature extraction for chip resistors defect based on PCA
Author(s): M. Q. Pan; L. G. Chen; T. Chen; M. X. Zhao; Z. H. Wang
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Principal component analysis (PCA) is the common method of compressing data for extracting sample statistical feature under the condition of meeting the optimal standard deviation. In this paper, it is to improve the recognition speed that the PCA was used to extract the image features of the chip resistors surface defects when the image is as much as possible to compress image data under the condition of retain the image defect information as much as possible. The result shows that PCA can greatly compress the images data during recognizing the chip resistor defect and improve the recognition accuracy, recognition rate is improved by increasing the training samples under the condition of not affect the recognition time, and the number of principal components has a suitable value. The defect recognition rate is the best when the main component number is the 78.57% of the eigenvectors of the training set covariance matrix.

Paper Details

Date Published: 15 November 2011
PDF: 7 pages
Proc. SPIE 8335, 2012 International Workshop on Image Processing and Optical Engineering, 833520 (15 November 2011); doi: 10.1117/12.918965
Show Author Affiliations
M. Q. Pan, Soochow Univ. (China)
State Key Lab. of Fluid Power Transmission and Control (China)
State Key Laboratory of Transducer Technology (China)
L. G. Chen, Soochow Univ. (China)
State Key Lab. of Fluid Power Transmission and Control (China)
T. Chen, Soochow Univ. (China)
State Key Lab. of Fluid Power Transmission and Control (China)
State Key Laboratory of Transducer Technology (China)
M. X. Zhao, Harbin Institute of Technology (China)
Z. H. Wang, Soochow Univ. (China)


Published in SPIE Proceedings Vol. 8335:
2012 International Workshop on Image Processing and Optical Engineering
Hai Guo; Qun Ding, Editor(s)

© SPIE. Terms of Use
Back to Top