Computational Technology Resources - CCP

I. Yani¹, E. Scavino², M.A. Hannan², D.A. Wahab³ and H. Basri¹

¹Department of Civil and Structural Engineering
²Department of Electrical, Electronic and System Engineering
³Department of Mechanical and Materials Engineering
Faculty of Engineering and Built Environment, National University of Malaysia, Bangi, Malaysia

doi:10.4203/ccp.103.21

purchase the full-text of this paper

I. Yani, E. Scavino, M.A. Hannan, D.A. Wahab, H. Basri, "An Automatic Sorting System for Recycling Beverage Cans using the Eigenface Algorithm", in Y. Tsompanakis, (Editor), "Proceedings of the Third International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 21, 2013. doi:10.4203/ccp.103.21

Keywords: automatic sorting, eigenface, detection, beverage cans, pattern recognition.

Summary

This paper describes the prototype implementation of a real-time automatic identification and sorting system for recyclable beverage cans using an intelligent computer vision technique. The image recognition system was developed based on the eigenface algorithm and achieved its ability to identify and sort by means of an automatic learning process. Three experiments have been conducted based on position and types of beverage cans moving on a conveyor belt. The results show that the identification and sorting of beverage cans achieved with an accuracy of up to 95%. It is concluded that the performance of the proposed system is robust enough for commercial applications.

purchase the full-text of this paper (price £20)

go to the previous paper
go to the next paper
return to the table of contents
return to the book description
purchase this book (price £42 +P&P)

	Computational & Technology Resources an online resource for computational, engineering & technology publications
	not logged in - login
Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 103 PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNOLOGY IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING Edited by: Y. Tsompanakis Paper 21 An Automatic Sorting System for Recycling Beverage Cans using the Eigenface Algorithm I. Yani¹, E. Scavino², M.A. Hannan², D.A. Wahab³ and H. Basri¹ ¹Department of Civil and Structural Engineering ²Department of Electrical, Electronic and System Engineering ³Department of Mechanical and Materials Engineering Faculty of Engineering and Built Environment, National University of Malaysia, Bangi, Malaysia doi:10.4203/ccp.103.21 purchase the full-text of this paper Full Bibliographic Reference for this paper I. Yani, E. Scavino, M.A. Hannan, D.A. Wahab, H. Basri, "An Automatic Sorting System for Recycling Beverage Cans using the Eigenface Algorithm", in Y. Tsompanakis, (Editor), "Proceedings of the Third International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 21, 2013. doi:10.4203/ccp.103.21 Keywords: automatic sorting, eigenface, detection, beverage cans, pattern recognition. Summary This paper describes the prototype implementation of a real-time automatic identification and sorting system for recyclable beverage cans using an intelligent computer vision technique. The image recognition system was developed based on the eigenface algorithm and achieved its ability to identify and sort by means of an automatic learning process. Three experiments have been conducted based on position and types of beverage cans moving on a conveyor belt. The results show that the identification and sorting of beverage cans achieved with an accuracy of up to 95%. It is concluded that the performance of the proposed system is robust enough for commercial applications. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £42 +P&P)
Back to top	©Civil-Comp Limited 2023 - terms & conditions