Research on feature extraction for chip resistors defect based on PCA

M. Q. Pan; L. G. Chen; T. Chen; M. X. Zhao; Z. H. Wang

doi:10.1117/12.918965

15 November 2011 Research on feature extraction for chip resistors defect based on PCA

M. Q. Pan, L. G. Chen, T. Chen, M. X. Zhao, Z. H. Wang

Proceedings Volume 8335, 2012 International Workshop on Image Processing and Optical Engineering; 833520 (2011) https://doi.org/10.1117/12.918965
Event: 2012 International Workshop on Image Processing and Optical Engineering, 2012, Harbin, China

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.

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M. Q. Pan, L. G. Chen, T. Chen, M. X. Zhao, and Z. H. Wang "Research on feature extraction for chip resistors defect based on PCA", Proc. SPIE 8335, 2012 International Workshop on Image Processing and Optical Engineering, 833520 (15 November 2011); https://doi.org/10.1117/12.918965

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KEYWORDS

Principal component analysis

Resistors

Image compression

Feature extraction

Facial recognition systems

Pattern recognition

Resistance

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