Abstract
Passive millimeter wave imaging is useful for security applications since it can detect objects concealed under clothing. However, because of the diffraction limit and low signal level, the automatic image analysis is very challenging. The multi-level segmentation of passive millimeter wave images is discussed as a way to detect concealed objects under clothing. Our passive millimeter wave imaging system is equipped with a Cassegrain dish antenna and a receiver channel operating around 3 mm wavelength. The expectation-maximization algorithm is adopted to cluster pixels on the basis of a Gaussian mixture model. The multi-level segmentation is investigated with more than two clusters to recognize the hidden object in different parts. The performance is evaluated by the average probability error. Experiments confirm that the presented method is able to detect the wood grip of a hand ax as well as the metal part concealed under clothing.
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