| ชื่อเรื่อง | : | Electrofused magnesium oxide classification using digital image processing and machine learning techniques |
| นักวิจัย | : | Ali, Shawkat. , Pun, W. K. |
| คำค้น | : | Machine learning. , Image processing , Magnesium oxide. , Pattern recognition systems. , Applied research. , 890202 Application Tools and System Utilities. , 080109 Pattern Recognition and Data Mining. , Data mining. , 890399 Information Services not elsewhere classified. , 080106 Image Processing. , 080199 Artificial Intelligence and Image Processing not elsewhere classified. , Electrofused magnesia -- Machine learning algorithms |
| หน่วยงาน | : | Central Queensland University, Australia |
| ผู้ร่วมงาน | : | - |
| ปีพิมพ์ | : | 2552 |
| อ้างอิง | : | http://hdl.cqu.edu.au/10018/38639 , http://dx.doi.org/10.1109/ICIT.2009.4939738 |
| ที่มา | : | Ali, ABMS & Pun, WKD 2009, 'Electrofused magnesium oxide classification using digital image processing and machine learning techniques', Proceedings of The IEEE International Conference on Industrial Technology, 10-13 February 2009, IEEE, pp. 1376-1381.http://dx.doi.org/10.1109/ICIT.2009.4939738 (viewed 19/11/09) |
| ความเชี่ยวชาญ | : | - |
| ความสัมพันธ์ | : | Proceedings of the 2009 IEEE International Conference on Industrial Technology (ICIT'09), 10-13 February, 2009, Monash University, Gippsland, Victoria, Australia. USA. : IEEE, 2009. p. 1376-1381 6 pages Refereed 9781424435067 1424435064 , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository. |
| ขอบเขตของเนื้อหา | : | - |
| บทคัดย่อ/คำอธิบาย | : | This research is focused on using digital image processing and machine learning techniques to classify Electrofused Magnesia for industry automation. We generate the data from dfferent images by using a modern digital image process. This research proposes a new method to construct the digital image database. The propose new method is based on simple histogram mode and intensity deviation. A group of six popular machinel earning algorithms has been tested to build up an automatic system for industry. We have concluded that the best suited algorithm for magnesia industry automation from this group is the PART algorithm. |
| บรรณานุกรม | : |
Ali, Shawkat. , Pun, W. K. . (2552). Electrofused magnesium oxide classification using digital image processing and machine learning techniques.
กรุงเทพมหานคร : Central Queensland University, Australia. Ali, Shawkat. , Pun, W. K. . 2552. "Electrofused magnesium oxide classification using digital image processing and machine learning techniques".
กรุงเทพมหานคร : Central Queensland University, Australia. Ali, Shawkat. , Pun, W. K. . "Electrofused magnesium oxide classification using digital image processing and machine learning techniques."
กรุงเทพมหานคร : Central Queensland University, Australia, 2552. Print. Ali, Shawkat. , Pun, W. K. . Electrofused magnesium oxide classification using digital image processing and machine learning techniques. กรุงเทพมหานคร : Central Queensland University, Australia; 2552.
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