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.
|Number of pages||140|
|Publication status||Published - 01 Dec 2009|
|Event||Proceedings of the 9th WSEAS International Conference on Applied Computer Science, ACS '09 - |
Duration: 01 Dec 2009 → …
|Conference||Proceedings of the 9th WSEAS International Conference on Applied Computer Science, ACS '09|
|Period||01/12/09 → …|
All Science Journal Classification (ASJC) codes
- Building and Construction
- Mechanical Engineering
- Management, Monitoring, Policy and Law
Rahman, M. O., Hussain, A., Scavino, E., Hannan, M. A., & Basri, H. (2009). Segregating recyclable waste papers using co-occurrence features. 157-162. Paper presented at Proceedings of the 9th WSEAS International Conference on Applied Computer Science, ACS '09, .