Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing
Author
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-08
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Lin, Tsun-Kuo. 2019. Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing. Journal of Sensors،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1191330
Modern Language Association (MLA)
Lin, Tsun-Kuo. Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing. Journal of Sensors No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1191330
American Medical Association (AMA)
Lin, Tsun-Kuo. Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing. Journal of Sensors. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1191330
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1191330