A machine vision system using circular autoregressive models for rapid recognition of salmonella typhimurium
The 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 fluores...
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UNIVERSIDAD ANTONIO NARIÑO
2013
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| Online Access: | https://revistas.uan.edu.co/index.php/ingeuan/article/view/333 https://repositorio.uan.edu.co/handle/123456789/10418 |
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| author | Trujillo, O. Griffis, C. L. Li, Y. Slavik, M. F. |
| author_facet | Trujillo, O. Griffis, C. L. Li, Y. Slavik, M. F. |
| author_sort | Trujillo, O. |
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| description | The 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. |
| format | info:eu-repo/semantics/article |
| id | repositorio.uan.edu.co-123456789-10418 |
| institution | Repositorio Digital UAN |
| language | spa |
| publishDate | 2013 |
| publisher | UNIVERSIDAD ANTONIO NARIÑO |
| record_format | dspace |
| spelling | repositorio.uan.edu.co-123456789-104182024-10-14T03:49:56Z A machine vision system using circular autoregressive models for rapid recognition of salmonella typhimurium A machine vision system using circular autoregressive models for rapid recognition of salmonella typhimurium Trujillo, O. Griffis, C. L. Li, Y. Slavik, M. F. Bacteria detection fluorescence microscopy machine vision image analysis pattern recognition The 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. The 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. 2013-05-14 2024-10-10T02:24:44Z 2024-10-10T02:24:44Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/version/c_970fb48d4fbd8a85 https://revistas.uan.edu.co/index.php/ingeuan/article/view/333 https://repositorio.uan.edu.co/handle/123456789/10418 spa https://revistas.uan.edu.co/index.php/ingeuan/article/view/333/279 https://creativecommons.org/licenses/by-nc-sa/4.0 http://purl.org/coar/access_right/c_abf2 application/pdf UNIVERSIDAD ANTONIO NARIÑO INGE@UAN - TENDENCIAS EN LA INGENIERÍA; Vol. 2 Núm. 4 (2012) 2346-1446 2145-0935 |
| spellingShingle | Bacteria detection fluorescence microscopy machine vision image analysis pattern recognition Trujillo, O. Griffis, C. L. Li, Y. Slavik, M. F. A machine vision system using circular autoregressive models for rapid recognition of salmonella typhimurium |
| title | A machine vision system using circular autoregressive models for rapid recognition of salmonella typhimurium |
| title_full | A machine vision system using circular autoregressive models for rapid recognition of salmonella typhimurium |
| title_fullStr | A machine vision system using circular autoregressive models for rapid recognition of salmonella typhimurium |
| title_full_unstemmed | A machine vision system using circular autoregressive models for rapid recognition of salmonella typhimurium |
| title_short | A machine vision system using circular autoregressive models for rapid recognition of salmonella typhimurium |
| title_sort | machine vision system using circular autoregressive models for rapid recognition of salmonella typhimurium |
| topic | Bacteria detection fluorescence microscopy machine vision image analysis pattern recognition |
| url | https://revistas.uan.edu.co/index.php/ingeuan/article/view/333 https://repositorio.uan.edu.co/handle/123456789/10418 |
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