Image Processing Reference
In-Depth Information
FIGURE 8 Processing a table-fish trout image for classification using our statistical measur-
ing system.
For this research, we have designed and implemented a functional prototype to collect RGB
trout images. This prototype includes canalization, illumination, and vision components that
have been meticulously assembled. Also, we believe that this prototype could be easily integ-
rated into a mechanical system to interconnect lined earth tanks in farms.
Our experimental results encourage our research as they have shown that our classification
system is effective, where 95.93%, 93.21%, and 96.25% precisions are observed when classify-
ing fry, fingerling, and table-fish trout, respectively.
It is important to observe that, although our statistical approach has been inspired to meas-
ure rainbow trout, this approach can be applied to other fishes grown in farms.
As part of our future work, we are integrating water flow and stereovision into our proto-
type to investigate two main issues: the light reflection into the water and the presence of tur-
bulence. Our final aim is to implement an economical classification system for small farms.
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visión literaria y análisis [BSc. Final dissertation]. Mexico: Engineering Department,
Autonomous University of the State of Mexico; 2013.
[2] Miranda J. Prototipo de un sistema clasificador de la trucha arcoíris utilizando un
modelo estadístico de su longitud obtenido de imágenes 2D [BSc thesis]. Mexico:
Engineering Department, Autonomous University of the State of Mexico; 2014.
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ishes using computer vision. Research project SIyEA 32742012 M. Mexico: Autonom-
ous University of the State of Mexico; 2012.
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[7] Gallego I, Carrillo R, Garcı´a D, Sasso I, Guerrero J, Burrola C, et al. Programa maestro,
sistema producto trucha del Estado de Me´xico. Government Plan for Rainbow Trout Produc-
tion. Mexico: Autonomous University of the State of Mexico; 2007.
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