A novel probabilistic appearance model for cigarette detection under illumination change

Han Wang, Daviad K. Han, Quan Shi, Hanseok Ko

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

Abstract

An effective appearance model is proposed for detecting cigarettes individually in a tightly packed bundle based on an intensity variance probabilistic model. Conventional image intensity threshold based segmentation is brittle and susceptible to environmental variation such as an illumination change. To mitigate this problem, a geometrical snow model is designed to describe the image intensity variances of an individual cigarette. It is then combined with a Gaussian function to generate a probabilistic model of a cigarette detector. The experimental results show that the proposed appearance model provides accurate detection performances and that it is robust to illumination change compared with other conventional methods.

Original languageEnglish
Title of host publicationICEIC 2019 - International Conference on Electronics, Information, and Communication
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9788995004449
DOIs
Publication statusPublished - 2019 May 3
Event18th International Conference on Electronics, Information, and Communication, ICEIC 2019 - Auckland, New Zealand
Duration: 2019 Jan 222019 Jan 25

Publication series

NameICEIC 2019 - International Conference on Electronics, Information, and Communication

Conference

Conference18th International Conference on Electronics, Information, and Communication, ICEIC 2019
Country/TerritoryNew Zealand
CityAuckland
Period19/1/2219/1/25

Keywords

  • Appearance model
  • Cigarette detection
  • Illumination change

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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