Modified Adaptive Implicit Shape Model for Object Detection

  • Ziyan Xu
  • , Shujing Lyu*
  • , Weiping Jin
  • , Yue Lu
  • *Corresponding author for this work

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

2 Scopus citations

Abstract

Automated threat object detection in X-ray images is needed urgently in baggage inspection at airports, railway stations and other public places. However, the works on object detection are still very limited to meet the needs of practical application. In this paper, we propose a modified adaptive implicit shape model (MAISM) to detect threat objects in X-ray images, in which the triangle patches are used to compute occurrence of the centroid of object instead of keypoints. This model is adaptive for object detection in images of variable scales through triangle patch matching. Experiments on three different threat objects images (razor blades, shuriken, handguns) of various scales demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationNeural Information Processing - 26th International Conference, ICONIP 2019, Proceedings
EditorsTom Gedeon, Kok Wai Wong, Minho Lee
PublisherSpringer
Pages144-151
Number of pages8
ISBN (Print)9783030368012
DOIs
StatePublished - 2019
Event26th International Conference on Neural Information Processing, ICONIP 2019 - Sydney, Australia
Duration: 12 Dec 201915 Dec 2019

Publication series

NameCommunications in Computer and Information Science
Volume1143 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference26th International Conference on Neural Information Processing, ICONIP 2019
Country/TerritoryAustralia
CitySydney
Period12/12/1915/12/19

Keywords

  • MAISM
  • Object detection
  • Triangle patch matching
  • X-ray images

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