Using machine learning to detect and localize concealed objects in passive millimeter-wave images. (January 2018) - López-Tapia, Santiago; Molina, Rafael; Pérez de la Blanca, Nicolás
Abstract: The detection and location of objects concealed under clothing is a very challenging task that has crucial applications in security. In this domain, passive millimeter-wave images (PMMWIs) can be used. However, the quality of the acquired images, and the unknown position, shape, and size of hidden objects render this task difficult. In this paper, we propose a machine learning-based solution to this detection/localization problem. Our method outperforms currently used approaches. The effect of non-stationary noise on different classification algorithms is analyzed and discussed, and a detailed experimental comparative study of classification techniques is presented using a new and comprehensive PMMWI database. The low computational testing cost of this solution allows for its use in real-time applications. Highlights: A new approach to the detection of hidden objects in PMMWI based on machine learning. A comparative experimental study between two type of features and six classifiers. A new database of Passive Millimeter Wave Images (PMMWI).