| In recent years, red tides with serious harm appear more and more frequently incoastal waters, which caused more attention by the governments, the public and scientists.The same phenomenon happens in China, the country arranged researches on formationmechanism, early warning, forecasting, preventing and controlling methods ofred tide on different levels such as the fundamental research and high-technology development,which aims to establish operational monitoring system of red tide. Therefore,identifying the dominant species of red tide rapidly and effectively plays an importantrole in automatic monitoring of red tide. According to the situation that Harmful AlgalBlooms (HAB) appear in China's coastal waters, this paper elaborates the work includingmicroscopic image acquisition of HAB on multi-viewpoints with different growingperiods and establishment of microscopic image database about HAB appearingfrequently in China's coastal waters. Based on the traditional biological morphologicaltaxonomy, this paper studies multi-viewpoints characteristics of HAB microscopicimages, establishes the microscopic image identification system of HAB, and buildsan image collecting and diagnosing system for HAB based on B/S (Browser/Server)architecture which can provide remote processing, analysing as well as feedback foruploaded microscopic images. The work mainly contains:1. Research on ecological characteristics and taxonomy of HAB appearing frequentlyin China's coastal waters. According to the information of red tide appearingin recent years, listing the species of red tide appearing frequently inChina's coastal waters including 40 species totally within 11 important species;studying the ecological characteristics of these 40 species by the observation aswell as photographs through microscopes; designing the taxonomic system forthese 40 species. These work lay the foundation for designing microscopic imagedatabase of HAB and microscopic image identification system.2. Design and implementation of HAB microscopic image database and diagnosis system. Based on the biological ecological taxonomic characteristics of the 40species and the application requirements of the microscopic image identificationsystem, designing and building the microscopic image database of HAB; designingand implementing the microscopic image diagnosis system for HAB basedon B/S architecture using the technology of JAVA (J2EE) and WEB database.Until now, this database collects 24 of 40 species within 10 of 11 importantspecies, including 15 species of Dinophyta, 4 species of Bacillariophyta,3 species of Dictyochophyceae, 1 species of Haptophyceae, and 1 speciesCyanophyta. Furthermore, the image database and diagnosis system have alreadyworked more than one and half a year respectively.3. Research on microsopic image identification. Based on the ecological taxonomiccharacteristics of the 40 species and the microscopic image diagnosis system forHAB, designing the technology roadmap of microscopic image identificationsystem. About this system, this paper mainly contains the work as follows:(a) proposing a method of image segmentation for cell image based on multipledirections projection and automatic thresholding. This method can separatethe object cell from the microcopic image which is useful for cell extractionof species without setae. Therefore, object cell information in microscopicimages can be extracted. Besides, the location of the cell in microscopicimages can be located accurately to remove noises around the cell becausethis method adopts 8 directions projection. This method lays the foundationfor further features extraction and identification.(b) analysing image decomposition model of TV-L~2 and TV-G as well astheir numerical solution based on variational partial differential equationstheoretically. The actual application of these two models in decomposingphytoplankon microscopic images illustrates that the texture of image isbetter described by G space in TV-G model than that by L~2 space in TV-L~2model. Therefore, TV-G model is used to decompose microscopic imagesinto cartoon and texture components. To identify the species, the cartoonimage expressing shape of the cell is described by Hu invariant moment which have the invariance of scale, translation and rotation while the texttareimage expressing valve stria is descibed by fractal dimension which havethe invariance of multi-scale and multi-resolution. And the experimentalresults prove feasibility of this method.(c) improving the image matching method based on fractal neighbor distance.The improved method can realise the object matching more exactly thantraditional method for phytoplankton images, which can improve the identificationrate dramatically. |
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