Image Processing Reference
In-Depth Information
CHAPTER 3
Measuring rainbow trout by
using simple statistics
Marcelo Romero; José Manuel Miranda; Hector A. Montes-Venegas
Facultad de Ingeniería, Universidad Autónoma del Estado
de México, Toluca, Estado de México, Mexico
Abstract
Traditionally, a manual method is used to classify the rainbow trout in small farms, which generally
cause stress and physical damage to the fish. Additionally, this manual classification is not always accur-
ate, as farmers only visually check whether the trout is fry, fingerling, or table-fish size. In this article, we
robustly evaluate our simple statistical system to measure rainbow trout in farms. For this research, we
have designed and implemented a novel prototype that includes canalization, illumination, and vision
components to take a 2D downward-view image of the trout. After that, this image is processed to get the
trout's contour, which is used to estimate the fish's length by adjusting the best regression curve to this
contour. Finally, the trout's size is defined by the minimum Mahalanobis distance to training data. We
have evaluated our experimental results as a binary classification problem and the best precision scores
are 95.93%, 93.21%, and 96.25% when classifying fry, fingerling, and table-fish trout, respectively. These
experimental results are computed by using our state-of-the-art database of 1800 rainbow trout images,
which was collected with our physical prototype for this publication.
Keywords
Rainbow trout measuring
Statistical measuring
Fish classification
Acknowledgments
Authors thank to the Research Department of the Autonomous University of the State of Mexico
(UAEMex) for its financial support through the research project SIyEA/32742012 M. Their grat-
itude also goes to the Mexican Council for Science and Technology (CONACyT) for the schol-
arship granted to Jose Manuel Miranda (634478).
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