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Intelligent computer vision system for segregating recyclable waste papers
- Source :
- Expert Systems with Applications. 38:10398-10407
- Publication Year :
- 2011
- Publisher :
- Elsevier BV, 2011.
-
Abstract
- This article 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 facilitate high quality end products and save processing chemicals and energy. From 1932 to 2009, different mechanical and optical paper sorting methods have been developed to fill the paper sorting demand. Still, in many countries including Malaysia, waste papers are sorted into different grades using a manual sorting system. Because of inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. Automated paper sorting systems offer significant advantages over human inspection in terms of worker fatigue, throughput, speed, and accuracy. This research attempts to develop a smart vision sensing system that is able to separate the different grades of paper using first-order features. To construct a template database, a statistical approach with intra-class and inter-class variation techniques are applied to the feature selection process. Finally, the K-nearest neighbor (KNN) algorithm is applied for paper object grade identification. The remarkable achievement obtained with the method is the accurate identification and dynamic sorting of all grades of papers using simple image processing techniques.
- Subjects :
- Sorting algorithm
Process (engineering)
Computer science
business.industry
media_common.quotation_subject
General Engineering
Sorting
Feature selection
Machine learning
computer.software_genre
Computer Science Applications
Identification (information)
Artificial Intelligence
Quality (business)
Artificial intelligence
Data mining
business
computer
Throughput (business)
media_common
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 38
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi...........654c1d27b8eee838dea1f44cdc3eb51a