Abstract
It has been noticed that bottom trawling not only caused the decline of major fish stocks, but also damaged the biomass of non-target species and habitats as well. This paper proposes a method for identification of trawling marks from video images. The proposed method adopts a pattern recognition approach based on the extraction and the analysis of pattern shape of seabed images. At first, an approach of stationary wavelet transform based edge detection and line segment trace algorithm is developed for line detection. Second, based on the extracted line segments, shape features are computed and classified with a neural network classifier. Experiments on a variety of real seabed images are presented.
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Tang, Y., Yu, X., Wang, N. (2006). Trawling Pattern Analysis with Neural Classifier. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_81
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DOI: https://doi.org/10.1007/11881070_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
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