Detecting concealed threats

Potential threat items may be concealed inside objects, such as portable electronic devices, that are subject to imaging for example, at a security checkpoint. Data from an imaged object can be compared to pre-determined object data to determine a class for the imaged object. Further, an object can be identified inside a container (e.g., a laptop inside luggage). One-dimensional Eigen projections can be used to partition the imaged object into partitions, and feature vectors from the partitions and the object image data can be used to generate layout feature vectors. One or more layout feature vectors can be compared to training data for threat versus non-threat-containing items from the imaged object's class to determine if the imaged object contains a potential threat item.