A machine vision system using circular autoregressive models for rapid recognition of salmonella typhimurium

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dc.creatorTrujillo, O.
dc.creatorGriffis, C. L.
dc.creatorLi, Y.
dc.creatorSlavik, M. F.
dc.date2013-05-14
dc.date.accessioned2024-10-10T02:24:44Z
dc.date.available2024-10-10T02:24:44Z
dc.descriptionThe objective of this research was to develop a machine vision system using image processing and statistical modeling techniques to identify and enumerate bacteria on slides containing Salmonella typhimurium. Pictures of bacterial cells were acquired with a CCD camera attached to a motorized fluorescence microscope. A shape boundary modeling technique, based on the use of circular autoregressive model parameters, was used. A feature weighting classifier was trained with ten images belonging to each shape class (rod shape and circle shape). In order to enhance the discrimination of circular shapes, a size range was added to the recognition algorithm. Experimental results showed that the model parameters could be used as descriptors of shape boundaries detected in digitized binary images of bacterial cells. The introduction of the rotated coordinate method and the circular size restriction, reduced the differences between automated and manual recognition/enumeration from 7% to less than 1%. The computer analyzed each image in approximately 5 s (a total of 2 h including sample preparation), while the bacteriologist spent an average of 1 min for each image.en-US
dc.descriptionThe objective of this research was to develop a machine vision system using image processing and statistical modeling techniques to identify and enumerate bacteria on slides containing Salmonella typhimurium. Pictures of bacterial cells were acquired with a CCD camera attached to a motorized fluorescence microscope. A shape boundary modeling technique, based on the use of circular autoregressive model parameters, was used. A feature weighting classifier was trained with ten images belonging to each shape class (rod shape and circle shape). In order to enhance the discrimination of circular shapes, a size range was added to the recognition algorithm. Experimental results showed that the model parameters could be used as descriptors of shape boundaries detected in digitized binary images of bacterial cells. The introduction of the rotated coordinate method and the circular size restriction, reduced the differences between automated and manual recognition/enumeration from 7% to less than 1%. The computer analyzed each image in approximately 5 s (a total of 2 h including sample preparation), while the bacteriologist spent an average of 1 min for each image.es-ES
dc.formatapplication/pdf
dc.identifierhttps://revistas.uan.edu.co/index.php/ingeuan/article/view/333
dc.identifier.urihttps://repositorio.uan.edu.co/handle/123456789/10418
dc.languagespa
dc.publisherUNIVERSIDAD ANTONIO NARIÑOes-ES
dc.relationhttps://revistas.uan.edu.co/index.php/ingeuan/article/view/333/279
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0es-ES
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa
dc.sourceINGE@UAN - TENDENCIAS EN LA INGENIERÍA; Vol. 2 Núm. 4 (2012)es-ES
dc.source2346-1446
dc.source2145-0935
dc.subjectBacteria detectionen-US
dc.subjectfluorescence microscopyen-US
dc.subjectmachine visionen-US
dc.subjectimage analysisen-US
dc.subjectpattern recognitionen-US
dc.titleA machine vision system using circular autoregressive models for rapid recognition of salmonella typhimuriumen-US
dc.titleA machine vision system using circular autoregressive models for rapid recognition of salmonella typhimuriumes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
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