Identification of an Efficient FilteringSegmentation Technique for Automated Counting of Fish Fingerlings

The counting of fish fingerlings is an important process in determining the accurate consumption of feeds for a certain density of fingerlings in a pond. Image processing is a modern approach to automate the counting process. It involves six basic steps, namely, image acquisition, cropping, scaling, filtering, segmentation, and measurement and analysis. In this study, two (2) filtering and two (2) segmentation algorithms are identified based on the following observations: the nonuniform brightness and contrast of the image; random noise brought about by feeds, waste, and spots in the container; and the likelihood of the image samples or application used by the different authors of the smoothing and clustering algorithms in their respective experiments. Four (4) combinations of filtering-segmentation algorithms are implemented and tested. Results show that combination of local normalization filter and iterative selection threshold yield a very high counting accuracy using the measurement function such as Precision, Recall, and F-measure. A Graphical User Interface (GUI) is also presented to visualize the image processing steps and its counting results.