Adaptive Neuro-fuzzy Classifier for Weapon Detection in X-Ray Images of Luggage Using Zernike Moments and Shape Context Descriptor

Author

Lopez, Annet Deenu ; Kollialil, Eldho S. ; Gopan, K. Gopika

Author_Institution

Dept. Appl. Electron. & Instrum., Rajagiri Sch. of Eng. & Technol., Kochi, India

fYear

2013

fDate

29-31 Aug. 2013

Firstpage

46

Lastpage

49

Abstract

Weapon detection in luggages using X-ray machines at various security tight areas especially airports are inevitable. The process tends to be time consuming and requires the skills of a human operator to identify the weapon from the contents of the luggage. An automated weapon (gun) detection method for luggage screening systems utilizing connected component analysis, zernike moments and shape context descriptor based feature extraction methods for adaptive neuro-fuzzy classifier is proposed here. The proposed method showed an efficiency of 98% in detecting weapon (gun) with least false alarm rate.

Keywords

X-ray imaging; Zernike polynomials; airports; feature extraction; fuzzy neural nets; object detection; weapons; X-ray images; X-ray machines; adaptive neuro-fuzzy classifier; airports; automated weapon detection; connected component analysis; feature extraction; luggage screening systems; shape context descriptor; zernike moments; Adaptive systems; Context; Feature extraction; Polynomials; Shape; Weapons; X-ray imaging; Adaptive Neuro-Fuzzy Inference System; Connected Component Analysis; Shape Context Descriptor; Weapon Detection; Zernike Moments;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Computing and Communications (ICACC), 2013 Third International Conference on

Conference_Location

Cochin

Type

conf

DOI

10.1109/ICACC.2013.16

Filename

6686334