Statictics of gabor features for coin recognition

Linlin Shen, Sen Jia, Zhen Ji, Wen Sheng Chen

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

11 Citations (Scopus)

Abstract

We present an image based approach for coin classification. Gabor wavelets are used to extract features for local texture representation. To achieve rotation-invariance, concentric ring structure is used to divide the coin image into a number of small sections. Statistics of Gabor coefficients within each section is then concatenated into a feature vector for whole image representation. Matching between two coin images are done via Euclidean distance measurement and the nearest neighbor classifier. The public MUSCLE database consisting of over 10,000 images is used to test our algorithm, results show that significant improvements over edge distance based methods have been achieved.

Original languageEnglish
Title of host publication2009 IEEE International Workshop on Imaging Systems and Techniques, IST 2009 - Proceedings
Pages299-302
Number of pages4
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event2009 IEEE International Workshop on Imaging Systems and Techniques, IST 2009 - Hong Kong, China
Duration: 11 May 200912 May 2009

Publication series

Name2009 IEEE International Workshop on Imaging Systems and Techniques, IST 2009 - Proceedings

Conference

Conference2009 IEEE International Workshop on Imaging Systems and Techniques, IST 2009
Country/TerritoryChina
CityHong Kong
Period11/05/0912/05/09

Keywords

  • Coin classification
  • Edge distance
  • Gabor wavelet

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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