Fast millimeter wave threat detection algorithm

Millimeter Wave (MMW) imaging systems are currently being used to detect hidden threats. Unfortunately the current performance of detection algorithms is very poor due to the presence of severe noise, the low resolution of MMW images and, in general, the poor quality of the acquired images. In this paper we present a new real time MMW threat detection algorithm based on a tailored de-noising, body and threat segmentation, and threat detection process that outperforms currently existing detection procedures. A complete comparison with a state of art threat detection algorithm is presented in the experimental section.

[1]  Aggelos K. Katsaggelos,et al.  Passive millimeter-wave imaging with compressive sensing , 2012 .

[2]  Gabriel M. Rebeiz,et al.  Design and Characterization of $W$-Band SiGe RFICs for Passive Millimeter-Wave Imaging , 2010, IEEE Transactions on Microwave Theory and Techniques.

[3]  Thomas E. Hall,et al.  Active millimeter-wave standoff and portal imaging techniques for personnel screening , 2009, 2009 IEEE Conference on Technologies for Homeland Security.

[4]  Kunio Sawaya,et al.  Development of 77 GHz millimeter wave passive imaging camera , 2009, 2009 IEEE Sensors.

[5]  Nobuyuki Otsu,et al.  ATlreshold Selection Method fromGray-Level Histograms , 1979 .

[6]  Xavier Binefa,et al.  Concealed object detection and segmentation over millimetric waves images , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[7]  Jin Koo Rhee,et al.  Development of a passive millimeter-wave imaging system , 2009, 2009 International Waveform Diversity and Design Conference.

[8]  V. Krozer,et al.  THz Active Imaging Systems With Real-Time Capabilities , 2011, IEEE Transactions on Terahertz Science and Technology.

[9]  Ieee Staff 2017 25th European Signal Processing Conference (EUSIPCO) , 2017 .

[10]  Naomi E. Alexander,et al.  Multispectral mm-wave imaging: materials and images , 2008, SPIE Defense + Commercial Sensing.

[11]  Payam Heydari,et al.  A 94-GHz passive imaging receiver using a balanced LNA with embedded Dicke switch , 2010, 2010 IEEE Radio Frequency Integrated Circuits Symposium.

[12]  V Krozer,et al.  Terahertz Imaging Systems With Aperture Synthesis Techniques , 2010, IEEE Transactions on Microwave Theory and Techniques.

[13]  Andrew R. Harvey,et al.  Image analysis for object detection in millimetre-wave images , 2004, SPIE Security + Defence.

[14]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Aggelos K. Katsaggelos,et al.  Framework for efficient optimal multilevel image thresholding , 2009, J. Electronic Imaging.

[16]  Emanuele Trucco,et al.  Image processing techniques for metallic object detection with millimetre-wave images , 2006, Pattern Recognit. Lett..

[17]  Nuria Llombart,et al.  Confocal Ellipsoidal Reflector System for a Mechanically Scanned Active Terahertz Imager , 2010, IEEE Transactions on Antennas and Propagation.

[18]  Y. J. Yoon,et al.  Passive Millimeter-Wave Imaging Module With Preamplified Zero-Bias Detection , 2008, IEEE Transactions on Microwave Theory and Techniques.

[19]  H.B. Wallace,et al.  Standoff Detection of Weapons and Contraband in the 100 GHz to 1 THz Region , 2007, IEEE Transactions on Antennas and Propagation.

[20]  Min-Kyoo Jung,et al.  Development of passive millimeter wave imaging system at W-band , 2009, 2009 34th International Conference on Infrared, Millimeter, and Terahertz Waves.

[21]  Nuria Llombart,et al.  THz Imaging Radar for Standoff Personnel Screening , 2011, IEEE Transactions on Terahertz Science and Technology.

[22]  J. J. Lynch,et al.  W-Band Sb-Diode Detector MMICs for Passive Millimeter Wave Imaging , 2008, IEEE Microwave and Wireless Components Letters.