[1]温金玉,宣士斌,肖石林.基于小波变换的自适应水岭边缘检测技术[J].计算机技术与发展,2018,28(06):77-79.[doi:10.3969/ j. issn.1673-629X.2018.06.017]
　WEN Jin-yu,XUAN Shi-bin,XIAO Shi-lin.An Adaptive Watershed Edge Detection Technology Based on Wavelet Transform[J].,2018,28(06):77-79.[doi:10.3969/ j. issn.1673-629X.2018.06.017]
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基于小波变换的自适应水岭边缘检测技术()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年06期
页码:
77-79
栏目:
智能、算法、系统工程
出版日期:
2018-06-10

文章信息/Info

Title:
An Adaptive Watershed Edge Detection Technology Based on Wavelet Transform
文章编号:
1673-629X(2018)06-0077-03
作者:
温金玉; 宣士斌; 肖石林
广西民族大学 信息科学与工程学院,广西 南宁 530006
Author(s):
WEN Jin-yu; XUAN Shi-bin; XIAO Shi-lin
School of Information Science and Engineering,Guangxi University for Nationlities,Nanning 530006,China
关键词:
小波变换; 图像分割; 自适应阈值; 边缘检测; 形态学梯度
Keywords:
wavelet transform; image segmentation; adaptive threshold; edge detection; morphological gradient
分类号:
TP301
DOI:
10.3969/ j. issn.1673-629X.2018.06.017
文献标志码:
A
摘要:
提出一种基于小波变换的自适应分水岭边缘检测方法。 该方法首先使用数学形态学中的加掩模梯度法的多次分水岭多领域运算对灰度图像进行梯度标记,接着选择名为 Coif 的小波基函数对上一步处理后的图像进行小波多尺度分解变换,即对梯度图像进行多层分解,得到低频系数和高频系数。 提取低频系数和高频系数的水平、垂直及对角分量对各层小波分解系数进行处理。 利用自适应阈值法对小波分解系数进行两次阈值法处理后重构。 最后应用 Canny 微分算子进行图像分割。 实验结果表明,与已有分割结果对比,该方法具有更好的效果。
Abstract:
We propose an adaptive watershed edge detection algorithm based on wavelet transform. Firstly,the repeated watershed with mask gradient and multi domain method in mathematical morphology is used for label gradient on segmented gray image. Next,a wavelet basis function named Coif is adopted to do multi-scale decomposing on the image processed,which means the gradient image is decomposed into multi layers. After that,we acquire low and high frequency coefficients. The horizontal,vertical and diagonal components of low and high frequency coefficients are extracted in order to deal with each layer of coefficients of the wavelet decomposition. Additionally,the wavelet coefficients are processed twice by the adaptive threshold method before the image is reconstructed. Eventually,the Canny operator is used to segment image. Experiment demonstrates that the proposed method has better effect compared to the existing segmentation results.

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更新日期/Last Update: 2018-08-16