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dc.contributor.committeeChairSari-Sarraf, Hamed
dc.contributor.committeeChairHequet, Eric F.
dc.contributor.committeeMemberSaed, Mohammed and Computer Engineering
dc.creatorShahriar, Muneem
dc.description.abstractCotton 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.subjectFiber maturity
dc.subjectFiber length
dc.subjectCotton fiber
dc.titleMachine vision system for quantification of cotton fiber length and maturity

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