Texture-based classification of workpiece surface images using the support vector machine
Identifying the specific machining processes used to produce specific workpiece surfaces is very useful in materials inspection. Machine vision can be used to semi- or fully automate this identification process by firstly extracting features from captured workpiece images, followed by analysis using...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Science and Engineering Research Support Society
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/46515/ http://psasir.upm.edu.my/id/eprint/46515/1/Texture-based%20classification%20of%20workpiece%20surface%20images%20using%20the%20support%20vector%20machine.pdf |
| _version_ | 1848850676782727168 |
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| author | Ashour, Mohammed Waleed Abdul Halin, Alfian Khalid, Fatimah Abdullah, Lili Nurliyana Darwish, Samy H. |
| author_facet | Ashour, Mohammed Waleed Abdul Halin, Alfian Khalid, Fatimah Abdullah, Lili Nurliyana Darwish, Samy H. |
| author_sort | Ashour, Mohammed Waleed |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Identifying the specific machining processes used to produce specific workpiece surfaces is very useful in materials inspection. Machine vision can be used to semi- or fully automate this identification process by firstly extracting features from captured workpiece images, followed by analysis using machine learning algorithms. This enables inspection to be performed more reliably with minimal human intervention. In this paper, three visual texture features are investigated to classify machined workpiece surfaces into the six machining process classes of Turning, Grinding, Horizontal Milling, Vertical Milling, Lapping, and Shaping. These are the multi-directional Gabor filtered images, intensity histogram and edge features statistics. Support Vector Machines (SVM) applying different kernel functions are investigated for best classifier performance. Results indicate that the Gabor-based SVM-linear kernel provides superior performance. |
| first_indexed | 2025-11-15T10:10:05Z |
| format | Article |
| id | upm-46515 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:10:05Z |
| publishDate | 2015 |
| publisher | Science and Engineering Research Support Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-465152022-06-20T03:54:48Z http://psasir.upm.edu.my/id/eprint/46515/ Texture-based classification of workpiece surface images using the support vector machine Ashour, Mohammed Waleed Abdul Halin, Alfian Khalid, Fatimah Abdullah, Lili Nurliyana Darwish, Samy H. Identifying the specific machining processes used to produce specific workpiece surfaces is very useful in materials inspection. Machine vision can be used to semi- or fully automate this identification process by firstly extracting features from captured workpiece images, followed by analysis using machine learning algorithms. This enables inspection to be performed more reliably with minimal human intervention. In this paper, three visual texture features are investigated to classify machined workpiece surfaces into the six machining process classes of Turning, Grinding, Horizontal Milling, Vertical Milling, Lapping, and Shaping. These are the multi-directional Gabor filtered images, intensity histogram and edge features statistics. Support Vector Machines (SVM) applying different kernel functions are investigated for best classifier performance. Results indicate that the Gabor-based SVM-linear kernel provides superior performance. Science and Engineering Research Support Society 2015 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/46515/1/Texture-based%20classification%20of%20workpiece%20surface%20images%20using%20the%20support%20vector%20machine.pdf Ashour, Mohammed Waleed and Abdul Halin, Alfian and Khalid, Fatimah and Abdullah, Lili Nurliyana and Darwish, Samy H. (2015) Texture-based classification of workpiece surface images using the support vector machine. International Journal of Software Engineering and Its Applications, 9 (10). pp. 147-160. ISSN 1738-9984 https://www.earticle.net/Article/A255699 10.14257/ijseia.2015.9.10.15 |
| spellingShingle | Ashour, Mohammed Waleed Abdul Halin, Alfian Khalid, Fatimah Abdullah, Lili Nurliyana Darwish, Samy H. Texture-based classification of workpiece surface images using the support vector machine |
| title | Texture-based classification of workpiece surface images using the support vector machine |
| title_full | Texture-based classification of workpiece surface images using the support vector machine |
| title_fullStr | Texture-based classification of workpiece surface images using the support vector machine |
| title_full_unstemmed | Texture-based classification of workpiece surface images using the support vector machine |
| title_short | Texture-based classification of workpiece surface images using the support vector machine |
| title_sort | texture-based classification of workpiece surface images using the support vector machine |
| url | http://psasir.upm.edu.my/id/eprint/46515/ http://psasir.upm.edu.my/id/eprint/46515/ http://psasir.upm.edu.my/id/eprint/46515/ http://psasir.upm.edu.my/id/eprint/46515/1/Texture-based%20classification%20of%20workpiece%20surface%20images%20using%20the%20support%20vector%20machine.pdf |