Image Processing Reference
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Then, we observe that this regression curve is related to the trout's length, which could be
estimated by computing the Euclidean distance among the points within the regression curve.
Finally, given a probe-length ( l i ) a classification can be done by comparing against training
lengths. For this research, such comparison is performed by computing the Mahalanobis dis-
tance [ 15 ] from training fry, fingerling, and table-trout lengths:
Hence, a probe-trout t i is classified through its estimated length l i by comparing its Mahalan-
obis distance d i against a predefined threshold, which in fact is the number of standard devi-
ations that is expected to be to the training mean length ( ).
4 Experimental framework
This section presents the experimental framework to illustrate how rainbow trout is measured
using our statistical approach.
As show in Figure 3 , after an RGB image is taken by our prototype, we are following a ive-
stage image processing to get the trout's contour. As explained in Section 3 , we are measuring
the trout's length using this contour.
FIGURE 3 Processing an incoming RGB image to estimate the trout's length using our stat-
istical approach.
To classify the trout's image within an image, we are performing four main steps. Firstly,
an RGB image of the trout is taken using our prototype ( Section 2 ) . Secondly, this RGB image
is processed to obtain the trout's contour. Thirdly, the trout's length is estimated by applying
our statistical method ( Section 3 ) to the trout's contour. Finally, using that estimated length,
the trout is classified using a binary classification approach.
In Section 4.1 , we provide more detail about our image processing step.
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