# Measurement of Fish Morphological Features through Image Processing and Deep Learning Techniques
## 3 Experimental Results--Discussion  
The errors in the estimation of four parameters were examined first: length, height, area and area overlap. The error in the area was estimated from the comparison of the number of pixels enclosed by the fish contour, which was estimated by one of the methods presented in this paper, with the number of pixels enclosed by the actual fish contour. However, a different object may have been recognized as a fish with comparable dimensions. For this reason, area overlap error was also defined as the fraction of the actual fish pixels that did not overlap with the estimated fish area. The experimental results concerning these error parameters for the image processing techniques described in Figure 1 are presented in Table 1. The results listed in Table 1 concern either the Contour1 or Contour2 path, since the developed application could automatically select the appropriate results as described in Section 2.2. The estimated errors using the BMA and SCIA deep learning techniques described in Figure 11 are listed in Tables 2 and 3, respectively. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Species** & **Area Error** & **Length Error** & **Height Error** & **Overlap Error** \\ \hline _Dicentrarchus labrax_ & 27.8\% & 6.6\% & 9.1\% & 10.3\% \\ _Diplodus puttazzo_ & 20.5\% & 4.3\% & 12\% & 9.1\% \\ _Merlucius merlucius merlucius_ & 20\% & 6.9\% & 13\% & 14.2\% \\ _Sparus aurata_ & 25\% & 1.9\% & 22.4\% & 5.7\% \\ _Average_ & **23.33\%** & **4.93\%** & **14.13\%** & **9.83\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dimension errors for the image processing approach of Figure 1. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Species** & **Area Error** & **Length Error** & **Height Error** & **Overlap Error** \\ \hline _Dicentrarchus labrax_ & 18.6\% & 7.9\% & 18.4\% & 24.3\% \\ _Diplodus puttazzo_ & 26.4\% & 15.6\% & 15.5\% & 24.9\% \\ _Merlucius merlucius merlucius_ & 22\% & 12.2\% & 11.7\% & 25.8\% \\ _Sparus aurata_ & 24.4\% & 12\% & 15.5\% & 26.5\% \\ _Average_ & **22.85\%** & **11.93\%** & **15.28\%** & **25.38\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dimension errors for the deep learning BMA approach described in Figure 11.  
The developed methods attempted to localize the following fish parts: the mouth and the spiny, dorsal, pelvic, anal and caudal fins. The image regions recognized to correspond to these parts were colored appropriately. Ideally, the recognized regions should match the actual fish part, but in practice, three cases were met: (1) the recognized region pixels were a subset of the corresponding fish part (partial match), (2) the recognized region was overlapping with the fish part (overlapping match) and (3) the recognized region was outside the fish part (matching failure). The purpose of the fish part recognition was to extract additional features concerning this specific region (e.g., dimensions, color and texture). Therefore, a partial match (if a full match is not possible) was preferable to an overlapping match, since in the case of overlapping, additional processing may be required. For example, the area that did not belong to the fish part could be excluded based on texture or geometrical restrictions. Tables 4-6 list the experimental  
higher accuracy. The deep learning techniques, and especially the BMA trained method, could localize the spin, soft dorsal, pelvic and anal fins as well as the mouth position with higher accuracy. It is possible to exploit all these techniques in order to achieve the highest accuracy. For example, the same image can be used as input for the Contour1 path and the BMA deep learning inference concurrently. If the image background is simple enough, and thus the results of the Contour1 path are acceptable, the deep learning approach can be canceled. If the results of the Contour1 path are unacceptable, the image processing Contour2 path can be followed, and the deep learning approach can be allowed to finish. The results of the two methods can then be compared and combined.