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Application of learning algorithms for colour recognition on underwater images
Author(s) -
Hoth Julian,
Kowalczyk Wojciech
Publication year - 2015
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.201510319
Subject(s) - underwater , artificial intelligence , computer science , focus (optics) , task (project management) , computer vision , image (mathematics) , support vector machine , quality (philosophy) , algorithm , pattern recognition (psychology) , optics , engineering , geology , physics , systems engineering , philosophy , oceanography , epistemology
Abstract Objects look very different in the underwater environment compared to their appearance in sunlight. High quality images with correct colouring simplify the detection of underwater objects. Hence, image processing is required to obtain images of high quality and correct colouring. Current algorithms focus on the colour reconstruction of scenery at diving depth where a significant part of sunlight is still present and different colours can still be distinguished. At greater depth the filtering is much stronger such that this is not possible. In this study it is investigated whether machine learning can be used to transform image data obtained in a controlled laboratory setup. The images are fed through learning machines with or without pre‐filters. It is shown that k ‐nearest neighbour and support vector machines are most suitable for the given task and yield excellent results. (© 2015 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)