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.creatorNombo, Josiah
dc.creatorMwambela, Alfred
dc.creatorKisangiri, Michael
dc.date2016-05-19T13:07:34Z
dc.date2016-05-19T13:07:34Z
dc.date2015
dc.date.accessioned2018-03-27T08:52:55Z
dc.date.available2018-03-27T08:52:55Z
dc.descriptionThis 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.
dc.identifierNombo, 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).
dc.identifierhttp://hdl.handle.net/20.500.11810/2154
dc.identifier10.5120/19263-0960
dc.identifier.urihttp://hdl.handle.net/20.500.11810/2154
dc.languageen
dc.subjectElectrical capacitance tomography
dc.subjectImage reconstruction algorithms
dc.subjectData fitting
dc.subjectGompertz function
dc.titlePerformance Analysis of Grey Level Fitting Mechanism based Gompertz Function for Image Reconstruction Algorithms in Electrical Capacitance Tomography Measurement System
dc.typeJournal Article, Peer Reviewed

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