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Research of Image Edge Detection Based on Wavelet Transform and EMD

Author: WeiLiHua
Tutor: MaSheXiang
School: Tianjin University of Technology
Course: Signal and Information Processing
Keywords: Edge Detection Wavelet Transform Multi-scale analysis Empirical Mode Decomposition Interpolation fitting Limited neighborhood
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 96
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Abstract


The image edge detection is one of the basis of the content of the field of image processing. Edge detection is often used as the first step of image understanding and analysis, image recognition and segmentation process for detecting image features important attributes. Wavelet by virtue of a good time-frequency and multi-scale analysis techniques can detect the image edge noise suppression and provides an effective way for image edge detection. Wavelet basis function with self-adaptive, that is a big difference in terms of the non-stationary signals, wavelet local analysis capability is weak, therefore, how better access to high-quality image edge will become the image processing An important task. This article focused on the wavelet transform and empirical mode decomposition algorithm research and analysis in the application of image edge detection, studied Comparative Results of analysis: the use of the traditional edge detection algorithms and edge detection using wavelet transform; based on wavelet transform and two dimensional empirical mode decomposition method of combining image edge detection; bidimensional empirical mode decomposition based on limited neighborhood image edge detection. Proposed for non-stationary signals with larger differences, wavelet local analysis capability is weak, the wavelet transform and two-dimensional empirical mode decomposition method of combining image edge detection. Empirical mode decomposition algorithm is a powerful tool to deal with non-linear - non-stationary signals, the decomposition process is entirely driven by the data, and can be easily extended to the two-dimensional space, we use symmetric boundary leak issues using the gray scale of the image data mean calculation Screener termination conditions, improved two-dimensional empirical mode decomposition algorithm, and combined with wavelet edge detection, simulation results show that the algorithm is superior to using wavelet transform edge extraction, be able to get a better edge extraction effect. Need to consume a lot of time for the two-dimensional empirical mode decomposition algorithm bidimensional empirical mode decomposition based on limited neighborhood the algorithm discarded the longer consumption interpolation fitting surface, the process did not terminate the conditions imposed, reducing the algorithm running time. In this paper, the algorithm of image enhancement and noise image denoising do after edge extraction Finally, the simulation results verify the effectiveness of the algorithm.

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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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