视觉分类识别系统设计

Visual classification and recognition system design

针对企业生产过程中零件混装问题,设计了使用基于高斯混合模型的背景分离算法,实现稳定且灵活的背景分离效果,使用基于等级灰度、形状相似和简单轮廓的特征提取算法,实现有效且稳定的特征数据提取效果,利用基于xml数据存储的多层神经网络算法,实现物品种类动态变更的效果。通过对图像进行分离、提取、识别后,达到分类的效果,结果表明,该系统不仅可以达到工业环境的稳定且智能的物品分类识别效果,而且在确保99%以上的高分类准确率的同时快速完成了分类任务,为市场多样化的需求提供了简洁有效的解决方案。

Aiming at the problem of mixed assembly of parts in the production process of enterprises,the background separation algorithm based on Gaussian mixture model is designed to achieve stable and flexible background separation effect.The feature extraction algorithm based on gray level,similar shape and simple contour is used to achieve effective and stable feature data extraction effect.The multi-layer neural network algorithm based on xml data storage is used to realize the background separation effect the effect of dynamic change of item type.Through the image separation,extraction,recognition,to achieve the effect of classification,the results show that the system can not only achieve the stable and intelligent classification and recognition effect of industrial environment,but also ensure more than 99%of the high classification accuracy at the same time,quickly complete the classification task,and provide a simple and effective solution for the demand of market diversification.