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 language | English |
|---|---|
| Title of host publication | ICEIC 2019 - International Conference on Electronics, Information, and Communication |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9788995004449 |
| DOIs | |
| Publication status | Published - 2019 May 3 |
| Event | 18th International Conference on Electronics, Information, and Communication, ICEIC 2019 - Auckland, New Zealand Duration: 2019 Jan 22 → 2019 Jan 25 |
Publication series
| Name | ICEIC 2019 - International Conference on Electronics, Information, and Communication |
|---|
Conference
| Conference | 18th International Conference on Electronics, Information, and Communication, ICEIC 2019 |
|---|---|
| Country | New Zealand |
| City | Auckland |
| Period | 19/1/22 → 19/1/25 |
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Keywords
- Appearance model
- Cigarette detection
- Illumination change
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering
Cite this
A novel probabilistic appearance model for cigarette detection under illumination change. / Wang, Han; Han, Daviad K.; Shi, Quan; Ko, Hanseok.
ICEIC 2019 - International Conference on Electronics, Information, and Communication. Institute of Electrical and Electronics Engineers Inc., 2019. 8706404 (ICEIC 2019 - International Conference on Electronics, Information, and Communication).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A novel probabilistic appearance model for cigarette detection under illumination change
AU - Wang, Han
AU - Han, Daviad K.
AU - Shi, Quan
AU - Ko, Hanseok
PY - 2019/5/3
Y1 - 2019/5/3
N2 - 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.
AB - 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.
KW - Appearance model
KW - Cigarette detection
KW - Illumination change
UR - http://www.scopus.com/inward/record.url?scp=85065882210&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065882210&partnerID=8YFLogxK
U2 - 10.23919/ELINFOCOM.2019.8706404
DO - 10.23919/ELINFOCOM.2019.8706404
M3 - Conference contribution
AN - SCOPUS:85065882210
T3 - ICEIC 2019 - International Conference on Electronics, Information, and Communication
BT - ICEIC 2019 - International Conference on Electronics, Information, and Communication
PB - Institute of Electrical and Electronics Engineers Inc.
ER -