The development of on-line algal concentrating device and phytoplankton counter
Date Issued
2011
Date
2011
Author(s)
Kuo, Ting-Wei
Abstract
The aim of this research is to develop an on-line algal cell concentrator, and to capture algal image with automatic image extraction device. Finally these images will be counted and recognized by image recognition and numerating program.
The first part of the research works was designing and manufacturing an automatic algal cell concentrating device, which was composed of a plankton net, a timer, a water pump and electronic valves. Secondly, phytoplanktons taken from the ecological pond at Graduate Institute of Environmental Engineering of National Taiwan University were concentrated by the on-line device. Then, the algal images were acquired by a CCD microscope.
Finally, the images were processed with a phytoplankton recognition system. In order to build the database of features of algal species, including Chlamydomonas, Microcystis and Staurastrum, the algal images were processed with an image processing program, which includes the steps of image segmentation and features extraction. Then those unknown pictures were classified by Baye’s classifier and minimum distance method.
From the results of the feature database establishment and parameter optimization with learning sample sets, the recognition system could better identify Chlamydomonas and Staurastrum with the correct-recognition ratios of 95.6% and 93.3%, respectively. The system showed lower correctness for Microcystis, 46%. By deleting some inefficient features based on the results of discrimination analysis, the correctness ratios for Chlamydomonas, Microcystis and Staurastrum were improved to 96.4%, 73.7% and 97.3%, respectively. The discrimination analysis is able to enhance the performance of the recognition system.
Although the correctness of recognition for Microcystis could be raised to 73.7%, the size of the training database is not big enough to successfully recognize all types of Microcystis due to that the appearance of Microcystis is irregular and complicate. So the size of the training database should be enlarged to acquire more special cases of Microcystis.
Subjects
Microcystis
image recognition
algal cell concentrating
feature discrimination analysis
Type
thesis