EXP-SA: Wavelet Feature Extraction and Pattern Recognition in Imagery Surveillance for Detecting Roadside Explosives

该项目将开发一种基于小波特征提取的图像分析系统，用于基于二维小波变换的路边炸弹检测。本项目中描述的多个特征的使用，而不是与已知IED伪装的形状、表面纹理和表面亮度相关的单个特征的使用，可能会更好地将这些对象与背景场景区分开来。这项工作为更标准的图像变化检测提供了一种替代方案。将为加州大学圣地亚哥分校结构工程系与洛斯阿拉莫斯国家实验室联合设立的一个新的硕士学位课程开发一个关于图像监视的模块。

The project will develop an imagery analysis system based on wavelet feature extraction for roadside bomb detection based on two-dimensional wavelet transforms. The use of multiple features as described in this project, rather than individual ones, related to the shape, the surface texture and the surface brightness of known IED camouflages, are likely to better distinguish these objects from background scenes. This work offers an alternative to more standard imagery change detection. A module on imagery surveillance will be developed for a graduate level class for a new Master's Degree program established at UCSD's Structural Engineering Department jointly with the Los Alamos National Laboratory.