首页> 外文会议>Applied Imagery Pattern Recognition Workshop (AIPR), 2011 IEEE >Fusion of anomaly algorithm decision maps and spectrum features for detecting buried explosive Hazards in forward looking infrared imagery
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Fusion of anomaly algorithm decision maps and spectrum features for detecting buried explosive Hazards in forward looking infrared imagery

机译:融合异常算法决策图和光谱特征以检测前瞻性红外图像中的埋藏爆炸危险

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Remediation of the threat of explosive hazards is an extremely important goal. Such hazards are responsible for an unacceptable number of deaths and injuries to civilians as well as soldiers throughout the world. In this article, we put forth a new method for aggregating image space anomaly algorithm decisions across time (multi-look) as well as across disparate algorithms in Universal Transverse Mercator (UTM) space for forward looking vehicle mounted (FL) long-wave infrared (LWIR) imagery. We also explore the utility of fast Fourier transform (FFT) spectrum features, which were previously used for FL ground penetrating radar (FLGPR), on aggregated UTM anomaly algorithm decision (UTMAAD) maps. On a final note, we also discuss modifications to our pre-screener, an ensemble of trainable size contrast filters, for UTMAAD maps. Targets not detected at the moment are also not found by a human under visual inspection. Preliminary lane-based cross validation (CV) experiments are reported using field data measurements from a U.S. Army test site.
机译:补救爆炸危险的威胁是一个极其重要的目标。这种危害是造成全世界平民和士兵死亡和受伤的原因。在本文中,我们提出了一种新的方法,用于汇总跨时间(多视)以及跨通用墨卡托(UTM)空间中不同算法的图像空间异常算法决策,以用于前瞻性车载(FL)长波红外(LWIR)图像。我们还探讨了快速傅里叶变换(FFT)频谱特征的实用性,该特征以前用于FL探地雷达(FLGPR),用于汇总的UTM异常算法决策(UTMAAD)地图。最后,我们还讨论了针对UTMAAD地图的预筛选器(可训练的尺寸对比滤镜)的修改。在目视检查下,人类也无法找到当前未检测到的目标。使用来自美国陆军测试站点的现场数据测量报告了基于车道的初步交叉验证(CV)实验。

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