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dc.contributor.authorKing, William E.en_US
dc.date.accessioned2014-03-14T20:33:57Z
dc.date.available2014-03-14T20:33:57Z
dc.date.issued1998-02-20en_US
dc.identifier.otheretd-042099-141554en_US
dc.identifier.urihttp://hdl.handle.net/10919/31799
dc.description.abstractThis thesis describes the development of a commercial machine vision system as a case study for utilizing the Modular Reprogrammable Real-time Processing Hardware (MORRPH) board. The commercial system described in this thesis is based on a prototype system that was developed as a test-bed for developing the necessary concepts and algorithms. The prototype system utilized color linescan cameras, custom framegrabbers, and standard PCs to color-sort red oak parts (staves). When a furniture manufacturer is building a panel, very often they come from edge-glued paneled parts. These are panels formed by gluing several smaller staves together along their edges to form a larger panel. The value of the panel is very much dependent upon the â matchâ of the individual stavesâ i.e. how well they create the illusion that the panel came from a single board as opposed to several staves. The prototype system was able to accurately classify staves based on color into classes defined through a training process. Based on Trichromatic Color Theory, the system developed a probability density function in 3-D color space for each class based on the parts assigned to that class during training. While sorting, the probability density function was generated for each scanned piece, and compared with each of the class probability density functions. The piece was labeled the name of the class whose probability density function it most closely matched. A â best-faceâ algorithm was also developed to arbitrate between pieces whose top and bottom faces did not fall into the same classes. [1] describes the prototype system in much greater detail. In developing a commercial-quality machine vision system based on the prototype, the primary goal was to improve throughput. A Field Programmable Gate Array (FPGA)-based Custom Computing Machine (FCCM) called the MORRPH was selected to assume most of the computational burden, and increase throughput in the commercial system. The MORRPH was implemented as an ISA-bus interface card, with a 3 x 2 array of Processing Elements (PE). Each PE consists of an open socket which can be populated with a Xilinx 4000 series FPGA, and an open support socket which can be populated with support chips such as external RAM, math processors, etc. In implementing the prototype algorithms for the commercial system, a partition was created between those algorithms that would be implemented on the MORRPH board, and those that would be left as implemented on the host PC. It was decided to implement such algorithms as Field-Of-View operators, Shade Correction, Background Extraction, Gray-Scale Channel Generation, and Histogram Generation on the MORRPH board, and to leave the remainder of the classification algorithms on the host. By utilizing the MORRPH board, an industrial machine vision system was developed that has exceeded customer expectations for both accuracy and throughput. Additionally, the color-sorter received the International Woodworking Fairâ s Challengers Award for outstanding innovation.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartetd2.pdfen_US
dc.rightsI hereby grant to Virginia Tech or its agents the right to archive and to make available my thesis or dissertation in whole or in part in the University Libraries in all forms of media, now or hereafter known. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation.en_US
dc.subjectcolor sortingen_US
dc.subjecthardwooden_US
dc.subjectimage processingen_US
dc.subjectmachine visionen_US
dc.subjectFPGAen_US
dc.subjectcolor matchingen_US
dc.subjectreconfigurable computingen_US
dc.titleUsing an FPGA-Based Processing Platform in an Industrial Machine Vision Systemen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
dc.contributor.committeechairConners, Richard W.en_US
dc.contributor.committeememberKline, D. Earlen_US
dc.contributor.committeememberAbbott, A. Lynnen_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-042099-141554/en_US
dc.date.sdate1999-04-20en_US
dc.date.rdate2000-04-28
dc.date.adate1999-04-28en_US


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