Abstract-Millimeter waves can be used to detect concealed objects because they can penetrate clothing. Therefore, millimeter wave imaging draws increasing attention in security applications for the detection of objects under clothing. In such applications, it is critical to estimate the distances from objects concealed in open spaces. In this paper, we develop a segmentation-based stereo-matching method based on passive millimeter wave imaging to estimate the longitudinal distance from a concealed object. In this method, the concealed object area is segmented and extracted by a k-means algorithm with splitting initialization, which provides an iterative solution for unsupervised learning. The distance from a concealed object is estimated on the basis of discrepancy between corresponding centers of the segmented objects in the image pair. The conventional stereo-matching equation is modified according to the scanning properties of the passive millimeter wave imaging system. We experimentally demonstrate that the proposed method can accurately estimate distances from concealed objects.
Passive millimeter (MMW) imaging can penetrate clothing to create interpretable imagery of concealed objects. However, the image quality is often restricted by low signal to noise ratio and temperature contrast as well as low spatial resolution. In this paper, we explore a four-channel passive MMW imaging system operating in the 8 and 3 mm wavelength regimes with linear vertical and horizontal polarization directions. Both registration between different channel images and segmentation of concealed objects are addressed. Multichannel image registration is performed by geometric feature matching and affine transform, and then multi-level segmentation separates the human body region from the background, and concealed objects from the body region, sequentially. In the experiments, several metallic and non-metallic objects concealed under clothing are captured in indoors. It will be shown that our method can separate objects with higher accuracy than the conventional method.
Abstract-Millimeter wave (MMW) imaging has found rapid adoption in security applications such as concealed object detection under clothing. However, the imaging quality is often degraded due to resolution limit and low signal level. This study addresses shape feature analysis following concealed object detection. The object region is extracted by multi-level segmentation. Shape features are composed of several descriptors which are object area, perimeter, major and minor axes of the basic rectangle, rectangularity, compactness, and eccentricity. In the experiments, three objects (gun, hand ax, and plastic bottle containing liquid skin aid) concealed under clothing are captured by the passive MMW imaging system. The extracted shape features are compared with the true features from the object model showing good accuracy.
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