Performance Analysis of Grey Level Fitting Mechanism based Gompertz Function for Image Reconstruction Algorithms in Electrical Capacitance Tomography Measurement System

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dc.contributor.authorNombo, Josiah
dc.contributor.authorMwambela, Alfred
dc.contributor.authorKisangiri, Michael
dc.date.accessioned2016-05-19T13:07:34Z
dc.date.available2016-05-19T13:07:34Z
dc.date.issued2015
dc.description.abstractThis paper analyses the performance of grey level fitting mechanism based on Gompertz function used in Electrical Capacitance Tomography measurement system. In order to evaluate its performance, the data fitting mechanism has been applied to common image reconstruction algorithms which include; Linear Back Projection, Singular Value Decomposition, Tikhonov Regularization, Iterative Tikhonov Regularization, Landweber iteration and Projected Landweber iteration. Images were reconstructed using measured capacitance data for annular and stratified flows, and qualitative and quantitative evaluation were done on the reconstructed images in comparison with respective reference images. Results show that this grey level fitting mechanism is better in terms of improving image spatial resolution, minimizing relative image error and distribution error and maximizing correlation coefficient.en_US
dc.identifier.citationNombo, J., Mwambela, A. and Kisangiri, M., 2015. Performance Analysis of Grey Level Fitting Mechanism based Gompertz Function for Image Reconstruction Algorithms in Electrical Capacitance Tomography Measurement System. International Journal of Computer Applications, 109(15).en_US
dc.identifier.doi10.5120/19263-0960
dc.identifier.urihttp://hdl.handle.net/20.500.11810/2154
dc.language.isoenen_US
dc.subjectElectrical capacitance tomographyen_US
dc.subjectImage reconstruction algorithmsen_US
dc.subjectData fittingen_US
dc.subjectGompertz functionen_US
dc.titlePerformance Analysis of Grey Level Fitting Mechanism based Gompertz Function for Image Reconstruction Algorithms in Electrical Capacitance Tomography Measurement Systemen_US
dc.typeJournal Article, Peer Revieweden_US
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