首页> 外文会议>Detection and sensing of mines, expolosive objects, and obscured targets XVIII >A vehicle threat detection system using correlation analysis and synthesized X-ray images
【24h】

A vehicle threat detection system using correlation analysis and synthesized X-ray images

机译:利用相关分析和合成X射线图像的车辆威胁检测系统

获取原文
获取原文并翻译 | 示例

摘要

The goal of the proposed research is to automate the vehicle threat detection with X-ray images when a vehicle crosses the country border or the gateway of a secured facility (military base). The proposed detection system requires two inputs: probe images (from X-ray machine) and gallery images (from database). For each vehicle, the gallery images include the X-ray images of fully-loaded (with typical cargo) and unloaded (empty) vehicle. The proposed system produces two types of outputs for threat detection: the detected anomalies and the synthesized images (e.g., grayscale fusion, color fusion, and differential images). The anomalies are automatically detected with the block-wise correlation analysis between two temporally aligned images (probe versus gallery). The locations of detected anomalies can be marked with small rectangles on the probe X-ray images. The several side-view images can be combined into one fused image in gray scale and in colors (color fusion) that provides more comprehensive information to the operator. The fused images are suitable for human analysis and decision. We analyzed a set of vehicle X-ray images, which consists of 4 images generated from AS&E OmniView Gantry™. The preliminary results of detected anomalies and synthesized images are very promising; meanwhile the processing speed is very fast.
机译:拟议研究的目的是当车辆越过边界或安全设施(军事基地)的入口时,使用X射线图像自动检测车辆威胁。提议的检测系统需要两个输入:探针图像(来自X射线机)和画廊图像(来自数据库)。对于每辆车,图库图像包括满载(带有典型货物)和满载(空)车辆的X射线图像。所提出的系统产生用于威胁检测的两种类型的输出:检测到的异常和合成图像(例如,灰度融合,颜色融合和差分图像)。异常是通过两个时间对齐的图像(探针与图库)之间的逐块相关分析自动检测到的。探测到的异常的位置可以在探头X射线图像上用小矩形标记。可以将几个侧视图图像组合成一个灰度和彩色的融合图像(颜色融合),从而为操作员提供更全面的信息。融合的图像适合于人类分析和决策。我们分析了一组车辆X射线图像,其中包括从AS&E OmniView Gantry™生成的4张图像。检测到的异常和合成图像的初步结果非常有前景;同时处理速度非常快。

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