首页> 外文会议>2011 International Conference on Materials for Renewable Energy Environment >Identification method of waste based on gray level co-occurrence matrix and neural network
【24h】

Identification method of waste based on gray level co-occurrence matrix and neural network

机译:基于灰度共生矩阵和神经网络的废物识别方法

获取原文
获取外文期刊封面目录资料

摘要

According gray level co-occurrence matrix (glcm) has a good ability to express the characteristics of texture, a waste identification method based on glcm and probabilistic neural network was proposed. The method obtains waste images from the refuse conveyor belt by high-speed camera system, after image preprocessing, extracts the texture features -glcm, then trains the neural network with the glcm as samples, and waste intelligent identification was realized, lays the foundation for automatic classification of waste and provides a new harmless, reduction and resource way for the garbage disposal.
机译:针对灰度共生矩阵(glcm)具有良好的纹理表达能力,提出了一种基于glcm和概率神经网络的垃圾识别方法。该方法通过高速摄像系统从垃圾输送带中获取垃圾图像,对其进行图像预处理,提取纹理特征-glcm,然后以glcm作为样本训练神经网络,为垃圾的智能识别奠定了基础。废物的自动分类,为垃圾处理提供了一种新的无害化,减少污染和资源化的方式。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号