title: Automatic image segmentation for concealed object detection using the expectation-maximization algorithm.
abstract: We address an image segmentation method to detect concealed objects captured by passive millimeter wave (MMW) imaging. Passive MMW imaging can create interpretable imagery on the objects concealed under clothing, which gives the great advantage to the security system. In this paper, we propose the multi-level expectation maximization (EM) method to separate the concealed objects from the other area in the image. We apply the EM method to obtain a Gaussian mixture model (GMM) of the acquired image. In the experiments, we evaluate the performance by the average probability of error. We will show that the consecutive EM processes separates the object area more accurately than the conventional EM method.
authors.affiliation: Department of Computer and Communication Engineering, Daegu University, Gyeongsan, Gyeongbuk 712-714, Korea.
authors.forename: Dong-Su
authors.initials: DS
authors.lastname: Lee
authors.forename: Seokwon
authors.initials: S
authors.lastname: Yeom
authors.forename: Jung-Young
authors.initials: JY
authors.lastname: Son
authors.forename: Shin-Hwan
authors.initials: SH
authors.lastname: Kim
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity