Automatic recognition and numerating of phytoplankton with microscopic imaging
Date Issued
2008
Date
2008
Author(s)
Cheng, Tsung-chin
Abstract
The aim of this research is to develop a system for automatic monitoring phytoplankton in real water bodies. The system includes sample injector, digital image capture system , digital image process, and pattern recognition. The first part of the system is the sample injector including the flowing cell the pump pumping sample to the focal point of the microscope intermittently. The second part of the system is image capture system in which CCD camera captures images and the acquired images were processed for pattern recognition based on the sample from Zui-Yue Lake in National Taiwan University. The third part of the system is computerized pattern. The Bayes''classifier processes were used for the pattern recognition of the algae features. The system is able to estimate the numbers of phytoplankton by total water volume and the numbers of phytoplankton in the plenty of captured images. The recognition accuracy of the algae specimen achieves 63.9%; 61.6% for Chroomonas; 61.1% for Ankistrodesmus; 51.6% for Scenedesmus. In intermittent flowing and capturing images, The recognition accuracy is down to 14.3% for Pediastum because Pediastum are tend to mix with other objects and rotating quite often; the recognition accuracy achieves 56.3% for Scenedesmus; 76.2% for Chroomonas. Ankistrodesmus were not observed due to the season. Recognition accuracy of the recognition system is not good enough. With further improvement the system has the potential to be a powerful automatic monitoring device or phytoplankton in the future.
Subjects
phytoplankton
pattern recognition
digital image process
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-97-R95541123-1.pdf
Size
23.53 KB
Format
Adobe PDF
Checksum
(MD5):c2f85454141cbdb4b561bef98a291647