Object Recognition in Multi-View Dual Energy X-ray Images

Object recognition in X-ray images is an interesting application of machine vision that can help reduce the workload of human operators of X-ray scanners at security checkpoints. However, automatic inspection systems using machine vision techniques are not yet commonplace for generic threat detection in X-ray images. Moreover, this problem has not been well explored by machine vision community due to the lack of publicly available X-ray image datasets. This paper aims to fill in this gap. We first present a comprehensive evaluation of image classification and object detection in X-ray images using standard local features in a BoW framework with (structural) SVMs. Then, we extend the features to utilize the extra information available in dual energy X-ray images. Finally, we propose a multi-view branch-and-bound algorithm for multiview object detection. Through extensive experiments on three object categories (laptops, guns, bottles), we show that the classification and detection performance substantially improves with the extended features and multiple views.