• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Conference on Image and Signal Processing for Remote Sensing VIII, Sep 24-27, 2002, Agia Pelagia, Crete, Greece >Automated corresponding point candidate selection for image registration using wavelet transformation, neural network with rotation invariant inputs, and context information about neighbouring candidates
【24h】

Automated corresponding point candidate selection for image registration using wavelet transformation, neural network with rotation invariant inputs, and context information about neighbouring candidates

机译：使用小波变换，具有旋转不变输入的神经网络以及有关相邻候选对象的上下文信息，自动选择对应点的候选对象进行图像配准

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

An automated method that can select corresponding point candidates is developed. This method has the following three features: 1) employment of the RIN-net for corresponding point candidate selection; 2) employment of multi resolution analysis with Haar wavelet transformation for improvement of selection accuracy and noise tolerance; 3) employment of context information about corresponding point candidates for screening of selected candidates. Here, the "RIN-net" means the back-propagation trained feed-forward 3-layer artificial neural network that feeds rotation invariants as input data. In our system, pseudo Zernike moments are employed as the rotation invariants. The RIN-net has N x N pixels field of view (FOV). Some experiments are conducted to evaluate corresponding point candidate selection capability of the proposed method by using various kinds of remotely sensed images. The experimental results show the proposed method achieves fewer training pat terns, less training time, and higher selection accuracy than conventional method.
机译：开发了一种可以选择对应点候选的自动方法。该方法具有以下三个特征：1）利用RIN-net进行相应的候选点选择； 2）利用Haar小波变换进行多分辨率分析，以提高选择精度和噪声容忍度； 3）运用有关对应点候选者的上下文信息来筛选选定的候选者。在此，“ RIN-net”是指向后传播训练的前馈3层人工神经网络，其将旋转不变量作为输入数据。在我们的系统中，伪Zernike矩用作旋转不变量。 RIN网络具有N x N像素视场（FOV）。进行了一些实验，以通过使用各种遥感图像来评估所提出的方法的对应点候选者选择能力。实验结果表明，与传统方法相比，该方法具有更少的训练模式，更少的训练时间和更高的选择精度。

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