  Detecting baggage threats is one of the most difficult tasks, even for expert
officers. Many researchers have developed computer-aided screening systems to
recognize these threats from the baggage X-ray scans. However, all of these
frameworks are limited in identifying the contraband items under extreme
occlusion. This paper presents a novel instance segmentation framework that
utilizes trainable structure tensors to highlight the contours of the occluded
and cluttered contraband items (by scanning multiple predominant orientations),
while simultaneously suppressing the irrelevant baggage content. The proposed
framework has been extensively tested on four publicly available X-ray datasets
where it outperforms the state-of-the-art frameworks in terms of mean average
precision scores. Furthermore, to the best of our knowledge, it is the only
framework that has been validated on combined grayscale and colored scans
obtained from four different types of X-ray scanners.
