A new accurate pill recognition system using imprint information

Zhiyuan Chen, Sei Ichiro Kamata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Great achievements in modern medicine benefit human beings. Also, it has brought about an explosive growth of pharmaceuticals that current in the market. In daily life, pharmaceuticals sometimes confuse people when they are found unlabeled. In this paper, we propose an automatic pill recognition technique to solve this problem. It functions mainly based on the imprint feature of the pills, which is extracted by proposed MSWT (modified stroke width transform) and described by WSC (weighted shape context). Experiments show that our proposed pill recognition method can reach an accurate rate up to 92.03% within top 5 ranks when trying to classify more than 10 thousand query pill images into around 2000 categories.

Original languageEnglish
Title of host publicationSixth International Conference on Machine Vision, ICMV 2013
PublisherSPIE
ISBN (Print)9780819499967
DOIs
Publication statusPublished - 2013 Jan 1
Event6th International Conference on Machine Vision, ICMV 2013 - London, United Kingdom
Duration: 2013 Nov 162013 Nov 17

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9067
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference6th International Conference on Machine Vision, ICMV 2013
CountryUnited Kingdom
CityLondon
Period13/11/1613/11/17

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Keywords

  • Feature extraction
  • Image retrieval
  • Modified Stroke Width Transform (MSWT)
  • Pill recognition
  • Weighted Shape Context (WSC)

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Chen, Z., & Kamata, S. I. (2013). A new accurate pill recognition system using imprint information. In Sixth International Conference on Machine Vision, ICMV 2013 [906711] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9067). SPIE. https://doi.org/10.1117/12.2051168