Abstract
This paper explores the application of image processing techniques in recyclable waste paper sorting. In recycling, waste papers are segregated into various grades as they are subjected to different recycling processes. Highly sorted paper streams will facilitate high quality end products, and save processing chemicals and energy. Since 1932 to 2009, different mechanical and optical paper sorting methods have been developed to fill the demand of paper sorting. Still, in many countries including Malaysia, waste papers are sorted into different grades using manual sorting system. Due to inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems is increased. Automated paper sorting systems offer significant advantages over human inspection in terms of fatigue, throughput, speed, and accuracy. This research attempts to develop a smart vision sensing system that able to separate the different grades of paper using co-occurrence features. Finally, the rule based classifier is applied for paper object grade identification. The outcomes from the experiment in classification for White Paper, Old Newsprint Paper and Old Corrugated Cardboard are 92%, 89% and 91%, respectively.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 9th WSEAS International Conference on Applied Computer Science, ACS '09 |
| Pages | 157-162 |
| Number of pages | 6 |
| Publication status | Published - 2009 |
| Event | 9th WSEAS International Conference on Applied Computer Science, ACS '09 - Genova Duration: 17 Oct 2009 → 19 Oct 2009 |
Other
| Other | 9th WSEAS International Conference on Applied Computer Science, ACS '09 |
|---|---|
| City | Genova |
| Period | 17/10/09 → 19/10/09 |
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Keywords
- Co-occurrence matrix
- Grades of paper
- Waste paper recycling
- Waste paper sorting
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Segregating recyclable waste papers using co-occurrence features. / Rahman, Mohammad Osiur; Hussain, Aini; Scavino, Edgar; M A, Hannan; Basri, Hassan.
Proceedings of the 9th WSEAS International Conference on Applied Computer Science, ACS '09. 2009. p. 157-162.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Segregating recyclable waste papers using co-occurrence features
AU - Rahman, Mohammad Osiur
AU - Hussain, Aini
AU - Scavino, Edgar
AU - M A, Hannan
AU - Basri, Hassan
PY - 2009
Y1 - 2009
N2 - This paper explores the application of image processing techniques in recyclable waste paper sorting. In recycling, waste papers are segregated into various grades as they are subjected to different recycling processes. Highly sorted paper streams will facilitate high quality end products, and save processing chemicals and energy. Since 1932 to 2009, different mechanical and optical paper sorting methods have been developed to fill the demand of paper sorting. Still, in many countries including Malaysia, waste papers are sorted into different grades using manual sorting system. Due to inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems is increased. Automated paper sorting systems offer significant advantages over human inspection in terms of fatigue, throughput, speed, and accuracy. This research attempts to develop a smart vision sensing system that able to separate the different grades of paper using co-occurrence features. Finally, the rule based classifier is applied for paper object grade identification. The outcomes from the experiment in classification for White Paper, Old Newsprint Paper and Old Corrugated Cardboard are 92%, 89% and 91%, respectively.
AB - This paper explores the application of image processing techniques in recyclable waste paper sorting. In recycling, waste papers are segregated into various grades as they are subjected to different recycling processes. Highly sorted paper streams will facilitate high quality end products, and save processing chemicals and energy. Since 1932 to 2009, different mechanical and optical paper sorting methods have been developed to fill the demand of paper sorting. Still, in many countries including Malaysia, waste papers are sorted into different grades using manual sorting system. Due to inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems is increased. Automated paper sorting systems offer significant advantages over human inspection in terms of fatigue, throughput, speed, and accuracy. This research attempts to develop a smart vision sensing system that able to separate the different grades of paper using co-occurrence features. Finally, the rule based classifier is applied for paper object grade identification. The outcomes from the experiment in classification for White Paper, Old Newsprint Paper and Old Corrugated Cardboard are 92%, 89% and 91%, respectively.
KW - Co-occurrence matrix
KW - Grades of paper
KW - Waste paper recycling
KW - Waste paper sorting
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UR - http://www.scopus.com/inward/citedby.url?scp=78149291426&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:78149291426
SN - 9789604741274
SP - 157
EP - 162
BT - Proceedings of the 9th WSEAS International Conference on Applied Computer Science, ACS '09
ER -