Method for Structured Light Based 3D Vision InspectionBased on RBF Neural Network
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摘要: 研究了基于RBF(Redial Basis Function)神经网络的结构光三维视觉检测方法.该方法利用RBF网络良好的非线性映射能力以及学习、泛化能力,通过所获取的高精度的样本数据来训练RBF网络,最终建立起了用于结构光三维视觉检测的RBF网络模型.与常规方法相比,该方法不需要考虑视觉模型误差、光学调整误差等因素对视觉检测系统测量精度的影响,因而能够有效的克服常规建模方法的不足,保证了检测系统具有较高的精度.Abstract: Based on Radial Based Function (RBF) neural network, a method for structured light based 3D vision inspection is presented. The method uses RBF ANN (Artificial Neural Network) to establish the mapping relationship between a real object in the wold and its image captured by CCD camera, i.e., the mapping relationship between frame coodinate and its image coodinate. Compared with common methods, the preset approach ignores the vision model error, and allow the existence of optical adjust error. By overcoming disadvantages of common methods efficiently, higher measuring accuracy can be obtain.
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Key words:
- vision /
- three-dimension /
- neural networks /
- structured light /
- sample /
- training and testing
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