Wood defect classification based on image analysis and support vector machines
| Autor: | Irene Yu-Hua Gu, Raúl Vicen, Henrik Andersson |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Engineering
business.industry Order statistic ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Forestry Image processing Pattern recognition Plant Science Filter (signal processing) computer.software_genre Industrial and Manufacturing Engineering Constant false alarm rate Support vector machine Data set Knot (unit) Feature (computer vision) General Materials Science Artificial intelligence Data mining business computer ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | Wood Science and Technology. 44:693-704 |
| ISSN: | 1432-5225 0043-7719 |
| DOI: | 10.1007/s00226-009-0287-9 |
| Popis: | This paper addresses the issue of automatic wood defect classification. A tree-structure support vector machine (SVM) is proposed to classify four types of wood knots by using images captured from lumber boards. Simple and effective features are proposed and extracted by partitioning the knot images into three distinct areas, followed by utilizing a novel order statistic filter to yield an average pseudo color feature in each area. Excellent results have been obtained for the proposed SVM classifier that is trained by 800 wood knot images. Performance evaluation has shown that the proposed SVM classifier resulted in an average classification rate of 96.5% and false alarm rate of 2.25% over 400 test knot images. Future work will include more extensive tests on large data set and the extension of knot types. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink