scholarly journals A New Palm-Print Image Feature Extraction Method Based on Wavelet Transform and Principal Component Analysis

2011 ◽  
pp. 39-46
Author(s):  
Jia wei Li ◽  
Ming Sun
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Extraction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Feature Extraction Method ◽  
Palm Print

A feature extraction method for lung nodules based on a multichannel principal component analysis network (PCANet)

2019 ◽  
Vol 78 (13) ◽  
pp. 17317-17335 ◽  
Author(s):  
Xiaojiao Xiao ◽  
Zilin Qiang ◽  
Juanjuan Zhao ◽  
Yan Qiang ◽  
Pan Wang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Extraction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Lung Nodules ◽  
Feature Extraction Method

scholarly journals Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Tsun-Kuo Lin
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
High Performance ◽  
Principal Component ◽  
Component Analysis ◽  
Image Features ◽  
Image Feature ◽  
Support Vector ◽  
Feature Identification ◽  
Adaptive Pca

This paper developed a principal component analysis (PCA)-integrated algorithm for feature identification in manufacturing; this algorithm is based on an adaptive PCA-based scheme for identifying image features in vision-based inspection. PCA is a commonly used statistical method for pattern recognition tasks, but an effective PCA-based approach for identifying suitable image features in manufacturing has yet to be developed. Unsuitable image features tend to yield poor results when used in conventional visual inspections. Furthermore, research has revealed that the use of unsuitable or redundant features might influence the performance of object detection. To address these problems, the adaptive PCA-based algorithm developed in this study entails the identification of suitable image features using a support vector machine (SVM) model for inspecting of various object images; this approach can be used for solving the inherent problem of detection that occurs when the extraction contains challenging image features in manufacturing processes. The results of experiments indicated that the proposed algorithm can successfully be used to adaptively select appropriate image features. The algorithm combines image feature extraction and PCA/SVM classification to detect patterns in manufacturing. The algorithm was determined to achieve high-performance detection and to outperform the existing methods.

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