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An auto‐classification procedure for concealed weapon detection in millimeter‐wave radiometric imaging systems
Author(s) -
Işiker Hakan,
Ünal İlhami,
Tekbaş Mustafa,
Özdemir Caner
Publication year - 2018
Publication title -
microwave and optical technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.304
H-Index - 76
eISSN - 1098-2760
pISSN - 0895-2477
DOI - 10.1002/mop.31005
Subject(s) - extremely high frequency , artificial intelligence , computer vision , computer science , segmentation , millimeter , radar imaging , raw data , remote sensing , radar , pattern recognition (psychology) , geology , optics , telecommunications , physics , programming language
In this work, we developed and proposed an auto‐classification technique for concealed weapon detection (CWD) for passive millimeter‐wave (PMMW) imaging systems. This technique has the ability to detect, classify and image hidden objects beneath the cloth of human targets. The algorithm was based on segmentation of normalized gray‐scale images for raw passive millimeter‐wave captured images and employing a decisive criterion for the classification of the targets. First, we tested our algorithm with the simulated data that were created numerically. Next, we examined our CWD technique with the measured data that were collected with PMMW radiometric imaging system at Marmara Research Center of TÜBİTAK. After producing the raw passive radar images, we have applied our passive CWD technique to the measured raw images to assess the performance of the algorithm. Produced edge‐detected final images of the concealed objects provide successful operation of the proposed technique.