Concealed object detection and segmentation over millimetric waves images

Millimetric Waves Images (MMW) are becoming more and more useful in the passive detection of threaten objects based on plastic substances as explosives or sharp/cutting weapons. Our goal is to achieve segmentation of the body and concealed threats dealing with the inherent problems of this type of images: noise, low resolution and intensity inhomogeneity. In this work we present the results of applying Iterative Steering Kernel Regression (ISKR) method for denoising and Local Binary Fitting (LBF) for segmentation in order to correctly segment bodies and threats over a database of 29 MMW images. These methods, which had not been tested in the literature with these type of images, are compared with previously applied state of the art methods. Experimental results show that the use of the proposed methods in MMW images improve the results that had been obtained before.