• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Photonic Devices and Algorithms for Computing >Detection of targets varying in fine details rotation and translation
【24h】

Detection of targets varying in fine details rotation and translation

机译：检测细节变化旋转和平移的目标

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

Abstract: Automatic target recognition (ATR) has been a topic of interest for many researchers because of its applications in the fields of defense, manufacturing, health sciences etc. The ability of massive parallelism and high-speed classification of neural network (NN) makes it a good choice for ATR. In this paper, we present a novel ATR approach for targets varying in fine details, rotation and translation using a Learning Vector Quantization (LVQ) NN. The algorithm includes two phases such as the feature extraction and the NN discrimination. The feature extraction algorithm obtains the features of the original and distorted targets in the Fourier-log-polar domain and clusters them into a set of centers. These centers are then applied as inputs to an LVQ NN for training. We explore two distinct discrimination algorithms. In the first algorithm, unrotated target features are applied as training vectors and the network is tested with features of rotated targets. In the second algorithm, the LVQ NN is trained using the rotated images and tested for unknown rotated target features. The algorithm is also applied for the cases of targets varying in fine details and translation and a combination of rotation, fine details and translation.!10
机译：摘要：自动目标识别（ATR）由于在国防，制造，健康科学等领域的应用而成为许多研究人员关注的话题。大规模并行性和神经网络（NN）的高速分类能力使得这是ATR的不错选择。在本文中，我们提出了一种新颖的ATR方法，用于使用学习矢量量化（LVQ）NN对细节进行细化，旋转和平移的目标。该算法包括两个阶段，例如特征提取和神经网络识别。特征提取算法获取傅立叶对数极域中原始目标和畸变目标的特征，并将它们聚类为一组中心。然后将这些中心用作LVQ NN的输入以进行培训。我们探索两种不同的判别算法。在第一种算法中，将未旋转的目标特征用作训练矢量，并使用旋转目标的特征对网络进行测试。在第二种算法中，使用旋转图像训练LVQ NN，并测试未知的旋转目标特征。该算法还适用于精细细节和平移以及旋转，精细细节和平移相结合的目标！10

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