Machine vision system for quantification of cotton fiber length and maturity

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dc.contributor.committeeChair Sari -Sarraf , Dr . Hamed
dc.contributor.committeeChair Hequet , Dr . Eric
dc.contributor.committeeMember Saed , Dr . Mohammad
dc.degree.department Electrical and Computer Engineering
dc.degree.discipline Electrical and Computer Engineering
dc.degree.grantor Texas Tech University
dc.degree.level Masters
dc.degree.name Master of Science
dc.rights.availability Unrestricted .
dc.creator Shahriar , Muneem
dc.date.accessioned 2014 -02 -19T18 :44 :23Z
dc.date.available 2012 -06 -01T14 :53 :11Z
dc.date.available 2014 -02 -19T18 :44 :23Z
dc.date.issued 2008 -08
dc.identifier.uri http : / /hdl .handle .net /2346 /20816
dc.description.abstract Cotton is an important cash crop in the United States (third producer , first exporter ) . There is a constant demand for high quality cotton fibers in the export market , especially for fiber length and maturity . This requires an ever moving research into methodologies that can measure these qualities from cotton fibers accurately and quickly . In a previous study , a fiber length algorithm was developed to measure the length of cotton fibers with good accuracy (+ / - 1 % of true length ) and was validated on 20 cotton samples totaling 10 ,000 fibers . The objective of this thesis is to develop a machine vision system for the quantification of both fiber length and maturity . To achieve this , an improved image acquisition system is proposed which acquires high -resolution (25 ,400 dpi ) longitudinal scans of complete cotton fibers without breaking the fibers into individual segments or applying any physical stress to straighten them . Software algorithms are implemented on these scans to extract features related to fiber length and maturity . For length measurement , Wang ~{! / ~}s length algorithm is employed because it is invariant to fiber shapes , intra -fiber crimps and inter -fiber intersections . However , modifications have been made primarily to enhance the computational speed of the algorithm so that length measurements are close to real -time . The modified algorithm has also been validated on the original 20 cotton samples . An indirect method of estimating fiber maturity based on the evaluation of cotton fiber characteristics is also proposed . The maturity algorithm measures changes in fiber width , fiber convolutions , and fiber translucency along the length of a fiber and creates features that are pertinent to study these characteristics in more detail . The proposed algorithm has been applied to a sample of 50 mature and 50 immature cotton fibers . The results indicate that all three fiber haracteristics show statistically significant differences between the two samples . Further analysis has also shown that some features such as thin places per -unit -length and intensity differences are excellent predictors of maturity . The least deterministic characteristic found is changes in fiber width . To conclude , the findings imply that the system is fully capable of measuring fiber length , and capable of quantifying maturity differences between cotton samples .
dc.format.mimetype application /pdf
dc.language.iso eng
dc.subject Fiber Maturity
dc.subject Fiber Length
dc.subject Cotton Fiber
dc.title Machine vision system for quantification of cotton fiber length and maturity
dc.type Thesis

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Machine vision system for quantification of cotton fiber length and maturity. Master's thesis, Texas Tech University. Available electronically from http : / /hdl .handle .net /2346 /20816 .

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