Publication: Vision-based egg grade classifier
dc.citedby | 5 | |
dc.contributor.author | Zalhan M.Z. | en_US |
dc.contributor.author | Sera Syarmila S. | en_US |
dc.contributor.author | Mohd Nazri I. | en_US |
dc.contributor.author | Mohd Taha I. | en_US |
dc.contributor.authorid | 36730188700 | en_US |
dc.contributor.authorid | 36683226000 | en_US |
dc.contributor.authorid | 57217075866 | en_US |
dc.contributor.authorid | 57194007864 | en_US |
dc.date.accessioned | 2023-05-29T06:39:04Z | |
dc.date.available | 2023-05-29T06:39:04Z | |
dc.date.issued | 2017 | |
dc.description | Coordinate measuring machines; Human computer interaction; Image classification; Image enhancement; Intelligent systems; Medical imaging; Military photography; Classification algorithm; Digital image processing technique; Human computer interfaces; Image filtering; Image processing technique; Industrial inspections; Object classification; Vision systems; Image processing | en_US |
dc.description.abstract | Digital image processing techniques (DIP) have been widely used in various types of application recently. A variety of these techniques are now being used in many types of application area such as object classification, intelligent system, robotics, biometrics system, medical visualization, military, law enforcement, image enhancement and restoration, industrial inspection, artistic effect and human computer interfaces. This paper proposes the implementation of digital image processing techniques to classify three different categories of commercial eggs. The proposed system consists of the study on different types and sizes of commercial eggs, real size measurement of these eggs using Coordinate Measure Machine (CMM) and camera, classification algorithm and the development of vision based egg classification system. Image processing techniques such as image filtering and image enhancements have been applied in the system. Results have shown that the proposed system has been able to successfully classify three categories of commercial eggs with accuracy of more than 96%. � 2016 IEEE. | en_US |
dc.description.nature | Final | en_US |
dc.identifier.ArtNo | 7890772 | |
dc.identifier.doi | 10.1109/ICICTM.2016.7890772 | |
dc.identifier.epage | 35 | |
dc.identifier.scopus | 2-s2.0-85018365049 | |
dc.identifier.spage | 31 | |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018365049&doi=10.1109%2fICICTM.2016.7890772&partnerID=40&md5=df019cd791ff51aa68ee5a15330adc17 | |
dc.identifier.uri | https://irepository.uniten.edu.my/handle/123456789/23281 | |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.source | Scopus | |
dc.sourcetitle | ICICTM 2016 - Proceedings of the 1st International Conference on Information and Communication Technology | |
dc.title | Vision-based egg grade classifier | en_US |
dc.type | Conference Paper | en_US |
dspace.entity.type | Publication |