首页> 外文会议>International conference on artificial intelligence and soft computing;ICAISC 2012 >Objects Auto-selection from Stereo-Images Realised by Self-Correcting Neural Network
【24h】

Objects Auto-selection from Stereo-Images Realised by Self-Correcting Neural Network

机译:自校正神经网络从立体图像中进行对象自动选择

获取原文

摘要

In the present thesis the author undertakes the problem of the objects selecting on pictures. The novel conception of using depth map as a base to objects marking was proposed here, objects separation can be done on the base of depth (disparity), corresponding to points that should be marked. This allows for elimination of textures, occurring in background and also on objects. The object selection process must be preceded by picture's depth analysis. This can be done by the novel neural structure: Self-Correcting Neural Network. This structure is working point-by-point with no picture's segmentation before.
机译:在本文中,作者承担了在图片上选择对象的问题。这里提出了使用深度图作为对象标记基础的新颖概念,可以在深度(视差)的基础上完成对象分离,该深度对应于应标记的点。这样可以消除背景和对象上出现的纹理。在对象选择过程之前必须进行图片的深度分析。这可以通过新颖的神经结构完成:自校正神经网络。这种结构是逐点工作的,以前没有图片的分割。

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