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A Machine Vision System Using Circular Autoregressive Models for Rapid Recognition of Salmonella typhimurium


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Tipo de publicación

Científica

Tipología

Investigación y estudios

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Impreso: Revista de divulgación científica

Resumen

Abstract. 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.

Autores

O. Trujillo, C. L. Griffis, Y. Li., and M. F. Slavik

Registro ISSN

2145-0935

SNIES Área

Engineering

SNIES Categoría

Bioengineering

Fecha de publicación 01 de junio de 2012
Fecha de aceptación 01 de mayo de 2012
Medio indexado (nombre)

Revista de Ingenierías de Universidad Antonio Nariño: IngeUAN

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Enlaces http://csifesvr.uan.edu.co:81/index.php/ingeuan/index

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