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<title>基于多分辨率分析及QFCM算法的图像分割方法研究</title>
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<div class=Section1>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<h4 align=center style='text-align:center'><span lang=EN-US>2000年 8月 〖 Vol 37,
No 8, pp. 948 - 953 〗</span></h4>
<div class=MsoNormal align=center style='text-align:center'><span lang=EN-US>
<hr size=1 width="100%" align=center>
</span></div>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<div align=center>
<table class=MsoNormalTable border=0 cellspacing=3 cellpadding=0 width="97%"
style='width:97.0%;mso-cellspacing:2.2pt;mso-padding-alt:1.5pt 1.5pt 1.5pt 1.5pt'>
<tr style='mso-yfti-irow:0'>
<td width="9%" rowspan=2 style='width:9.0%;background:#CCCCFF;padding:1.5pt 1.5pt 1.5pt 1.5pt'>
<p class=MsoNormal align=right style='text-align:right'><b>题目<span
lang=EN-US><o:p></o:p></span></b></p>
</td>
<td width="91%" style='width:91.0%;background:#DCEAFC;padding:1.5pt 1.5pt 1.5pt 1.5pt'>
<p class=MsoNormal>基于多分辨率分析及<span lang=EN-US>QFCM算法的图像分割方法研究</span></p>
</td>
</tr>
<tr style='mso-yfti-irow:1'>
<td width="91%" style='width:91.0%;background:ivory;padding:1.5pt 1.5pt 1.5pt 1.5pt'>
<p class=MsoNormal><span lang=EN-US>RESEARCH ON IMAGE SEGMENTATION APPROACH
BASED ON MULTIRESOLUTION ANALYSIS AND QFCM ALGORITHM</span></p>
</td>
</tr>
<tr style='mso-yfti-irow:2'>
<td width="9%" rowspan=2 style='width:9.0%;background:#CCCCFF;padding:1.5pt 1.5pt 1.5pt 1.5pt'>
<p class=MsoNormal align=right style='text-align:right'><b>作者<span
lang=EN-US><o:p></o:p></span></b></p>
</td>
<td width="91%" style='width:91.0%;background:#DCEAFC;padding:1.5pt 1.5pt 1.5pt 1.5pt'>
<p class=MsoNormal>耿伯英 杨静宇</p>
</td>
</tr>
<tr style='mso-yfti-irow:3'>
<td width="91%" style='width:91.0%;background:ivory;padding:1.5pt 1.5pt 1.5pt 1.5pt'>
<p class=MsoNormal><span lang=EN-US>GENG BO-YING AND YANG JING-YU</span></p>
</td>
</tr>
<tr style='mso-yfti-irow:4'>
<td width="9%" rowspan=2 style='width:9.0%;background:#CCCCFF;padding:1.5pt 1.5pt 1.5pt 1.5pt'>
<p class=MsoNormal align=right style='text-align:right'><b>单位<span
lang=EN-US><o:p></o:p></span></b></p>
</td>
<td width="91%" style='width:91.0%;background:#DCEAFC;padding:1.5pt 1.5pt 1.5pt 1.5pt'>
<p class=MsoNormal><span lang=EN-US>(南京理工大学计算机科学与工程系 南京 210094)</span></p>
</td>
</tr>
<tr style='mso-yfti-irow:5'>
<td width="91%" style='width:91.0%;background:ivory;padding:1.5pt 1.5pt 1.5pt 1.5pt'>
<p class=MsoNormal><span lang=EN-US>(Department of Computer Science and
Engineering, Nanjing University of Science and Technology, Nanjing 210094)</span></p>
</td>
</tr>
<tr style='mso-yfti-irow:6'>
<td width="9%" rowspan=2 style='width:9.0%;background:#CCCCFF;padding:1.5pt 1.5pt 1.5pt 1.5pt'>
<p class=MsoNormal align=right style='text-align:right'><b>关键词<span
lang=EN-US><o:p></o:p></span></b></p>
</td>
<td width="91%" style='width:91.0%;background:#DCEAFC;padding:1.5pt 1.5pt 1.5pt 1.5pt'>
<p class=MsoNormal>图像分割，多分辨率小波分析，快速模糊<span lang=EN-US>C-均值聚类方法，特征集</span></p>
</td>
</tr>
<tr style='mso-yfti-irow:7'>
<td width="91%" style='width:91.0%;background:ivory;padding:1.5pt 1.5pt 1.5pt 1.5pt'>
<p class=MsoNormal><span lang=EN-US>image segmentation, multiresolution
wavelet analysis, qfcm algorithm, feature set</span></p>
</td>
</tr>
<tr style='mso-yfti-irow:8'>
<td width="9%" rowspan=2 style='width:9.0%;background:#CCCCFF;padding:1.5pt 1.5pt 1.5pt 1.5pt'>
<p class=MsoNormal align=right style='text-align:right'><b>摘要<span
lang=EN-US><o:p></o:p></span></b></p>
</td>
<td width="91%" style='width:91.0%;background:#DCEAFC;padding:1.5pt 1.5pt 1.5pt 1.5pt'>
<p class=MsoNormal>在图像的多分辨率小波分析的基础上，采用高斯<span lang=EN-US>-马尔可夫随机场模型来描述图像的局部特征.利用LMS算法(the
least-mean-square algorithm)求得模型的参数估计，构造出图像的特征集.再利用快速模糊C-均值聚类方法（QFCM）对该特征集进行模糊划分，从而完成图像的分割.实验证明，这种方法具有较强的适应性，尤其对于景物-背景对比度差以及信噪比较低的一类图像，具有良好的分割效果.</span></p>
</td>
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<tr style='mso-yfti-irow:9;mso-yfti-lastrow:yes'>
<td width="91%" style='width:91.0%;background:ivory;padding:1.5pt 1.5pt 1.5pt 1.5pt'>
<p class=MsoNormal><span lang=EN-US>The hierarchical multiresolution wavelet
analysis in conjunction with the contextual information of the image
extracted from GMRF results in local features of the image. The model
parameters are estimated by the LMS algorithm, and these parameters
constitute a feature set of the image. The segmentation is then implemented
via fuzzy partitioning of the feature set with the QFCM algorithm. The
adaptiveness of the approach has been tested by simulations on a wide range
of images. The experiment shows that this technique is especially effective
for those images with low object-surrounding contrast or images with low SNR.</span></p>
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<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
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