Image Feature Extraction based on Pulse coupled neural Networks for Seafloor sediment classification

DOI

10.2991/iske.2007.125How to use a DOI?

Keywords

Pulse coupled neural networks (PCNN), Feature extraction, Seafloor sediment classification, Side-scan sonar images

Abstract

For the reason of different images with different space distribution of gray levels, we proposed a texture representation based on simplified pulse coupled neural networks (PCNN) model which output a series of binary images corresponding to different gray levels. Then we transformed the images into 1D temporal sequence by calculating their variances to form feature vectors. Experiments show that the texture representation was rotation invariant which provided high classification rate for natural texture images. When used to classify side-scan sonar seafloor images of 12 types of sediment, accurate recognition rate of 100% was obtained. With the inherent parallel capability of PCNN, the method is more suited for real-time processing of sonar systems

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chenchen Liu
AU  - Zhimeng Zhang
PY  - 2007/10
DA  - 2007/10
TI  - Image Feature Extraction based on Pulse coupled neural Networks for Seafloor sediment classification
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 730
EP  - 733
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.125
DO  - 10.2991/iske.2007.125
ID  - Liu2007/10
ER  -