The development of numerating and recognition system for phytoplankton
Date Issued
2007
Date
2007
Author(s)
Tsai, Yu-chia
DOI
zh-TW
Abstract
The aim of this research is to develop a system for monitoring the
types and abundance of phytoplanktons in water. Flow cytometry, digital
image capturing, digital image processing, and pattern recognition(PR) were integrated in the system. Four major sets of researches were performed in this study. The first set of the research workes were the design and fabrication of the flowing cell. This was followed by image capturing by digital camera through microscope. The third set of research was image pre-processing, then the images obtained from the secong step were processed with developed PR technique in the fourth step.
By pumping the algal suspension sampled from the garden pond in my institute into the handcrafted flowing cell with a programmed metering pump, focusing the microscope to the cell chamber and taking picture with a digital camera, images of phytoplankton with satisfactory resolution for the following pattern recognition could be captured.
The developed image pre-processing procedure is able to select the images of algae from laboratory cultures and to save them for the later pattern recognition procedure. The images from either the algal specimen made from field sample or laboratory cultured algae flowing through the flow cell were used for the pattern-recognition tests. The results show that the Bayes’ classifier based on the single Gaussian probability density function is able to classify images of four types of algae from specimen with 88% to 100% successful recognition, except for the algae with specific orientational characteristics, for example, Staurastrum sp. Due to less difference among the laboratory cultured species, the successful recognition of them ranges from 64% to 100%. The preliminary study indicates that with further improvement the integrated system has the potential to be a powerful automatic monitoring device for phytoplankton in the future.
Subjects
藻類
樣式辨認
數位影像處理
algae
pattern recognition
digital image processing
Type
thesis
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