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http://dx.doi.org/10.9708/jksci/2012.17.10.061

An Improved Asterias Amurensis Recognition Method Based on Morphological Characteristics Analysis Techniques  

Shin, Hyun-Deok (Dept. of Computer Science and Engineering, Seoul Women's University)
Jeon, Young-Cheol (Dept. of Computer Science, Kwandong University)
Publication Information
Journal of the Korea Society of Computer and Information / v.17, no.10, 2012 , pp. 61-69 More about this Journal
Abstract
The population of highly prolific, predatory Asterias amurensis is growing sharply from year to year along the coastline of Korea, a nation surrounded by water on three sides. To make matters worse, the fact that Asterias amurensis devours living fish and shellfish has caused a heavy loss for fishermen involved in the aquaculture industry. What it all boils down to is the significance of technologies allowing one to recognize Asterias amurensis individuals using underwater images for the purpose of exterminating Asterias amurensis or identifying a change in the population of Asterias amurensis or the migration route of Asterias amurensis. An improved Asterias amurensis recognition method based on the morphological characteristics of Asterias amurensis was proposed in this paper. The proposed recognition method aimed at cases marked by the lack of extraction information on concaveness and convexity, which are the morphological characteristics of Asterias amurensis. Extracting all the characteristics of Asterias amurensis from images taken underwater is very difficult. In this respect, the proposed recognition is effective in terms of recognizing individuals in a diversity of Asterias amurensis images. As a result of the experiment, Our proposed method has achieved superior performance with 92.5% than other method.
Keywords
Asterias amurensis; Recognition; Morphological characteristics; Feature extraction; Feature Analysis;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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