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Image Processing Research of Phytoplankton Based on Multi-wavelet Theory

Author: SongLiNa
Tutor: JiGuangRong
School: Ocean University of China
Course: Signal and Information Processing
Keywords: Multi - wavelet transform Image Noise Reduction Translation invariant Feature Extraction Phytoplankton
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 51
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Abstract


Phytoplankton as the most important primary producers in the marine ecosystem , a tremendous impact on the marine environment and marine biological resources . How to classify identification of phytoplankton cells is the focus of our research . The multiwavelet theory based study , phytoplankton cell image noise reduction and feature extraction . The main work includes: 1. Multiwavelet study as a starting point in the history of the development of the basic theory of single wavelet to introduce the multiwavelet development and multi - wavelet analysis theory of knowledge , and at the same time , introduced multiwavelet four characteristics , and multiwavelet characteristics , requirements, and details the method of orthogonal symmetric compactly supported multiwavelets . To improve image quality and to facilitate the recognition process , the image noise reduction is essential . Traditional Donoho threshold algorithm based on a global threshold method instead of using local threshold , and improvement on the contractile function . In order to overcome the Pseudo-Gibbs phenomenon , using the method of translation invariant image processing . The experiments show that the translation invariant multiwavelet noise reduction effect in terms of the visual effects , or in the signal-to-noise ratio gain and minimum mean square sense are better than traditional hard threshold and soft threshold to overcome the noise reduction effect of the hard threshold method poor and soft threshold method over smooth signal distortion shortcomings . For image classification and feature extraction is good or bad is to determine the key factors of the classification performance . According to the texture characteristics of multi - resolution analysis of wavelet transform and a round screen algal cells , we proposed a multi- wavelet transform and PCA combined method of image feature extraction , identification , and third-order close neighbors . The experiments show that the multi- wavelet transform and PCA - based image feature extraction method can not only improve the recognition rate , and can greatly reduce the computation time and improve the operation rate . The above image processing research on phytoplankton , we have improved the the phytoplankton cells image quality , effective analysis and extraction of the characteristics of phytoplankton cells to reduce the computation time and improve the recognition rate , lay the foundation for the classification and identification of phytoplankton cells .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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