MACHINE VISION TECHNOLOGY FOR FOOD QUALITY AND SAFETY INSPECTIONS

Simple item page
dc.contributor.advisorTao, Yangen_US
dc.contributor.authorJin, Fenghuaen_US
dc.contributor.departmentFischell Department of Bioengineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2009-01-24T06:45:08Z
dc.date.available2009-01-24T06:45:08Z
dc.date.issued2008-10-02en_US
dc.description.abstractWith increased expectations for food products of high quality and safety standards, the need for accurate, fast and objective determination of these characteristics in food products continues to grow. Machine vision as a non-destructive technology, provides an automated and economic way to accomplish these requirements. This research thus explored two applications of using machine vision techniques for food quality and safety inspections. The first application is using a combined X-ray and laser range imaging system to detect bone and other physical contaminants inside poultry meat. For this project, our research focuses on how to calibrate the imaging system. A unique three-step calibration method was developed and results showed that high accuracy has been achieved for the whole system calibration - a root mean square error of 0.20 mm, a standard deviation of 0.20 mm, and a maximum error of 0.48 mm. The second application is separating walnuts' shells and meat. A backlight imaging system was developed based on our finding that the backlit images of walnut shells and meat showed quite different texture patterns due to their different light transmittance properties. The texture patterns were characterized by several rotation invariant texture analysis methods. The uncorrelated and redundant features were further removed by a support vector machine (SVM) based recursive feature elimination method, with the SVM classifier trained concurrently for separations of walnuts' shells and meat. The experimental results showed that the proposed approach was very effective and could achieve an overall 99.2% separation accuracy. This high separation accuracy and low instrument cost make the proposed imaging system a great benefit to the walnut processing industry.en_US
dc.format.extent4598956 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/8772
dc.language.isoen_US
dc.subject.pqcontrolledEngineering, Generalen_US
dc.subject.pquncontrolledmachine visionen_US
dc.subject.pquncontrolledfood safety inspectionen_US
dc.subject.pquncontrolledcamera calibrationen_US
dc.subject.pquncontrolledbacklight imagingen_US
dc.subject.pquncontrolledtexture analysisen_US
dc.subject.pquncontrolledfood qualityen_US
dc.titleMACHINE VISION TECHNOLOGY FOR FOOD QUALITY AND SAFETY INSPECTIONSen_US
dc.typeDissertationen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
umi-umd-5791.pdf
Size:
4.39 MB
Format:
Adobe Portable Document Format
Download

Collections

UMD Theses and Dissertations
Fischell Department of Bioengineering Theses and Dissertations