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Evaluation of color-based machine vision for lumber processing in furniture rough mills

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dc.contributor.authorWidoyoko, Agusen
dc.contributor.committeechairKline, D. Earlen
dc.contributor.committeememberLamb, Fred M.en
dc.contributor.committeememberWeidenbeck, J.K.en
dc.contributor.departmentWood Science and Forest Productsen
dc.date.accessioned2014-03-14T21:43:13Zen
dc.date.adate2008-08-22en
dc.date.available2014-03-14T21:43:13Zen
dc.date.issued1996en
dc.date.rdate2008-08-22en
dc.date.sdate2008-08-22en
dc.description.abstractThis research study examined the potential application of a color-based machine vision system under development at Virginia Tech for lumber processing in the furniture rough mill. The evaluation was done by conducting a yield study using 134 red oak boards. ROMI-RIP, a rip-first simulation program by Thomas (1995), was used to simulate yields for both the manually digitized lumber data and the machine vision scanned lumber data. The color-based machine vision system was evaluated by comparing the optimum yield obtainable when using lumber data derived from the automatic scanning system to: (1) observed yield from an existing state-of-the-art rip-first rough mill and (2) the optimum yield from manually digitized lumber data. Overall, the color-based machine vision system resulted in about 17 percent lower yield than was measured in the rough mill and 20 percent lower than the optimum, based on manually digitized lumber data. An analysis of the yield percentage point difference between the machine vision-based yields and optimal yields indicates: (1) approximately 11.5 yield points were lost due to errors in defect detection accuracy, (2) 7.3 yield points were lost due to errors in the machine vision material handling system, and (3) 1.3 yield points were lost due to data digitization and truncation errors. Since material handling, data digitization, and truncation problems are solvable with current technologies, future research should focus on developing systems that can improve the accuracy of feature recognition in lumber.en
dc.description.degreeMaster of Scienceen
dc.format.extentix, 117 leavesen
dc.format.mediumBTDen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-08222008-063556en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-08222008-063556/en
dc.identifier.urihttp://hdl.handle.net/10919/44347en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.haspartLD5655.V855_1996.W536.pdfen
dc.relation.isformatofOCLC# 35920225en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectlumberen
dc.subjectyielden
dc.subjectdefect detectionen
dc.subjectmachine visionen
dc.subject.lccLD5655.V855 1996.W536en
dc.titleEvaluation of color-based machine vision for lumber processing in furniture rough millsen
dc.typeThesisen
dc.type.dcmitypeTexten
thesis.degree.disciplineWood Science and Forest Productsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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