Semantic recognition of workpiece using computer vision for shape feature extraction and classification based on learning databases
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- 作者：Wei Ding ; ddyangzicheng@163.com ; 694703086@qq.com ; Jinan Gu ; Zhenyang Shang ; Shixi Tang ; Qian Wu ; Enock A. Duodu ; Zhi Yang
- 关键词：Computer vision ; Semantic recognition ; Shape features ; Knowledge discovery database (KDD)
- 刊名：Optik - International Journal for Light and Electron Optics
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：130
- 期：Complete
- 页码：1426-1437
- 全文大小：1142 K
- 卷排序：130
文摘
A new computer vision algorithm is proposed specifically for the analysis of mechanical workpieces’ image. KDD is selected to realize the adaptive selection and reduction of shape features which improves its speed and accuracy. The generated semantic knowledge assists to distinguish between discoidal-shaped parts and rod-shaped parts automatically. The selected parts can be classified accurately at 100% efficiency. The algorithm proposed in this paper is much more effective than template matching algorithm.