Abstract
The fundamental feature of Computer vision involves consolidating image processing, pattern recognition and classification procedures. Extricating data from a digital picture relies on upon first distinguishing essential objects or dividing the picture into homogenous sectors or objects termed as segmentation and afterward allotting out these sectors or objects to specific classes termed as classification procedure. The term homogeneous may allude to the shade of the region or an object, however it additionally may utilize different characteristics, for example, composition and shape. This study concentrates on implementing image segmentation and classification on six different fish species using the watershed and the nearest neighbor classifier (kNN) algorithm.
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Bhatia, M., Pandey, M., Kumar, N., Hooda, M., Akriti (2017). Enacting Segmentation Algorithms for Classifying Fish Species. In: Saini, H., Sayal, R., Rawat, S. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 8. Springer, Singapore. https://doi.org/10.1007/978-981-10-3818-1_5
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DOI: https://doi.org/10.1007/978-981-10-3818-1_5
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