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

该项目将开发一个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探测。 如本项目所述，使用与已知简易爆炸装置的形状、表面纹理和表面亮度有关的多个特征，而不是单个特征，可能更好地将这些物体与背景场景区分开来。 这项工作提供了一种替代更标准的图像变化检测。 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的研究生班开发一个图像监视模块。

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.