• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International conference on advances in computing, communications and informatics >Sign language recognition through kinect based depth images and neural network
【24h】

Sign language recognition through kinect based depth images and neural network

机译：通过基于Kinect的深度图像和神经网络手语识别

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Sign language is the language of the people with hearing and speaking disabilities. In it mostly hands are moved in a particular way which along with some facial expression produces a meaningful thought which the speaker would like to convey to others. Using the sign language people with speaking and hearing disabilities can communicate with others who know the language very easily but it becomes difficult when it comes to interacting with a normal person. As a result there is a requirement of an intermediate system which will help in improving the interaction between people with the hearing disabilities as well as with the normal people. In this paper we present a sign language recognition technique using kinect depth camera and neural network. Using the kinect camera we obtain the image of the person standing in front of the camera and then we crop the hand region from the depth image and pre-process that image using the morphological operations to remove unwanted region from the hand image and find the contour of the hand sign and from the particular contour position of the hand we generate a signal on which Discrete Cosine Transform (DCT) is applied and first 200 DCT coefficient of the signal are feed to the neural network for training and classification and finally the network classify and recognize the sign. A data set of sign from 0 to 9 are formed using kinect camera and we tested on 1236 images in the database on which training is applied and we achieved 98% training and an average accuracy for all the sign recognition as 83.5%.
机译：手语是人们的听力和口语障碍的语言。在它大部分的手在与一些面部表情以及产生一个有意义的思想，扬声器想传达给别人一种特殊的方式移动。使用手语与人说话和听力障碍可以与其他人谁很容易知道的语言交流，但是当它涉及到一个正常的人互动变得困难。其结果是有一个中间系统，这将在改善与听力残疾的人之间的互动，以及与正常的人帮助的要求。在本文中，我们使用Kinect的深度相机和神经网络提出了一种手语识别技术。使用Kinect的摄像头，我们得站在镜头前面的人的图像，然后我们从深度图像裁剪，手区域和预处理，使用形态学操作的图像，从手的图像删除不需要的区域，找到轮廓手征和从手的特定轮廓位置我们产生在其上的离散余弦变换（DCT）被应用，并且信号的第一200的DCT系数被馈送到神经网络的训练和分类，最后是网络分类的信号和识别标志。从0到9的正负号数据集使用超高动力学相机形成，我们在其上施加训练数据库上测试1236倍的图像和我们实现了98％的训练和对所有的标志识别的平均精度为83.5％。

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