A bayesian approach to textile defect detection problem and a comparative analysis

dc.contributor	Graduate Program in Systems and Control Engineering.
dc.contributor.advisor	Ertüzün, Ayşın.
dc.contributor.author	Yakın, Abdullah.
dc.date.accessioned	2023-03-16T11:34:49Z
dc.date.available	2023-03-16T11:34:49Z
dc.date.issued	2010.
dc.description.abstract	The purpose of this thesis was to detect the defects on textile fabric images using the particle filters. We approached the problem from a Bayesian perspective and represented the model in a state space formulation. To describe the state space formulation; the texture models such as linear, 2-D linear and Markov Random Field models and the noise types like Gaussian, mixture of Gaussian and alpha-stable noise are investigated to find the best representation that is appropriate for our textile images. The implementation results are compared with Kalman, Extended Kalman and Unscented Kalman filters. Finally time and performance analysis of the filters is given.
dc.format.extent	30cm.
dc.format.pages	xiv, 61 leaves;
dc.identifier.other	SCO 2010 Y35
dc.identifier.uri	https://digitalarchive.library.bogazici.edu.tr/handle/123456789/15660
dc.publisher	Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2010.
dc.relation	Includes appendices.
dc.relation	Includes appendices.
dc.subject.lcsh	Bayesian statistical decision theory.
dc.subject.lcsh	Kalman filtering.
dc.title	A bayesian approach to textile defect detection problem and a comparative analysis

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