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Trawling Pattern Analysis with Neural Classifier

  • Ying Tang
  • Xinsheng Yu
  • Ni Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)

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.

Keywords

Line Segment Bottom Trawling Neural Network Classifier Stationary Wavelet Stationary Wavelet Transform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ying Tang
    • 1
  • Xinsheng Yu
    • 2
  • Ni Wang
    • 1
  1. 1.College of Information Science and Technology 
  2. 2.College of Marine Geo-ScienceOcean University of ChinaQingdaoP.R. China

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