NDLI: Histogram of gradient features for buried threat detection in ground penetrating radar data

Content Provider	IEEE Xplore Digital Library
Author	Torrione, P. Morton, K.D. Sakaguchi, R. Collins, L.M.
Copyright Year	2012
Description	Author affiliation: Department of Electrical and Computer Engineering, Duke University, USA (Torrione, P.; Morton, K.D.; Sakaguchi, R.; Collins, L.M.)
Abstract	Detection of buried explosive threats is a challenging problem. GPR has recently become a powerful tool for achieving robust subsurface target detection, but novel target types, and large numbers of subsurface objects in rural environments significantly complicate accurate discrimination of explosive threats from harmless false alarms. Significant research in feature extraction from GPR data has previously shown the capability for improved performance. Similarly, many techniques from the computer vision literature have made significant strides in recent years in for improvements in object class recognition. This work studies the relationships between and application of feature descriptor techniques from the computer vision community in application to target detection in GPR data. Relationships between a very successful computer vision technique (Histogram of Oriented Gradients) and a related powerful technique from subsurface sensing (Edge Histogram Descriptors) are explored, and preliminary results suggest that techniques from the computer vision literature may provide robust target detection performance in GPR.
Starting Page	3182
Ending Page	3185
File Size	135620
Page Count	4
File Format	PDF
ISBN	9781467311601
ISSN	21536996
e-ISBN	9781467311595
e-ISBN	9781467311588
DOI	10.1109/IGARSS.2012.6350748
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-22
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Ground penetrating radar Histograms Computer vision Vectors Robustness Object detection machine learning histogram of oriented gradients HOG computer vision
Content Type	Text
Resource Type	Article

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