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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.author Nombo, Josiah
dc.contributor.author Mwambela, Alfred
dc.contributor.author Kisangiri, Michael
dc.date.accessioned 2019-08-29T05:27:34Z
dc.date.available 2019-08-29T05:27:34Z
dc.date.issued 2015-01
dc.identifier.uri http://dspace.nm-aist.ac.tz/handle/123456789/439
dc.description Research Article published by International Journal of Computer Applications Volume 109 – No. 15, January 2015 en_US
dc.description.abstract This 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.language.iso en en_US
dc.publisher International Journal of Computer Applications en_US
dc.subject Electrical Capacitance Tomography en_US
dc.subject Image Reconstruction Algorithms en_US
dc.subject Data Fitting en_US
dc.subject Gompertz function en_US
dc.title Performance Analysis of Grey Level Fitting Mechanism based Gompertz Function for Image Reconstruction Algorithms in Electrical Capacitance Tomography Measurement System en_US
dc.type Article en_US


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