Adaptation of self-tuning spectral clustering and CNN texture model for detection of threats in volumetric CT images

We report on the performance improvement in Automatic Threat Recognition (ATR) algorithm through the incorporation of self-tuning spectral clustering and a convolutional neural network texture model (CNN). The self-tuning clustering algorithm shows the ability to vastly reduce the amount of bleedout in threat objects resulting in better segmentation and classification. The CNN texture model shows improved detection and classification of textured threats. These additions have markedly improved the ATR. The tests performed using actual CT data of passenger bags show excellent performance characteristics.