Trainable Structure Tensors for Autonomous Baggage Threat Detection Under Extreme Occlusion

Taimur Hassan, Naoufel Werghi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


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.

Original languageBritish English
Title of host publicationComputer Vision – ACCV 2020 - 15th Asian Conference on Computer Vision, 2020, Revised Selected Papers
EditorsHiroshi Ishikawa, Cheng-Lin Liu, Tomas Pajdla, Jianbo Shi
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Print)9783030695439
StatePublished - 2021
Event15th Asian Conference on Computer Vision, ACCV 2020 - Virtual, Online
Duration: 30 Nov 20204 Dec 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12627 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th Asian Conference on Computer Vision, ACCV 2020
CityVirtual, Online


  • Instance segmentation
  • Object detection
  • Structure tensors
  • X-ray imagery


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