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双分布匹配的图像分割
Image Segmentation using Dual Distribution Matching |
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| 课程网址: |
http://videolectures.net/bmvc2012_taniai_distribution_matching/
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| 主讲教师: |
Tatsunori Taniai
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| 开课单位: |
东京大学
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| 开课时间: |
2012-10-09
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| 课程语种: |
英语
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| 中文简介: |
提出了一种图像分割方法，在给出图像前景区域和背景区域的近似颜色分布时，将图像分割为前景区域和背景区域。我们的方法受到了全局一致性度量的启发，这种度量直接评估给定分布和结果分割分布之间的相似性，最近提出这种度量是为了克服传统像素(局部)一致性度量的局限性。我们的方案的主要特点是使用了两个输入分布(前台和后台)，与之前的研究相比，增强了鲁棒性。为此，我们建立了一个新的数学模型来描述两个输入分布和分割之间的一致性，其中两个分布匹配项的权重参数被设置为与前景和背景区域的大小近似成比例。我们称之为双重分布匹配(DDM)。我们还导出了一种使用图割的优化方法。实验结果表明了该方法的有效性，并与局部一致性测度和全局一致性测度进行了比较。
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| 课程简介: |
We propose an image segmentation method that divides an image into foreground and background regions when the approximate color distributions for these regions are given. Our approach was inspired by global consistency measures that directly evaluate the similarity between a given distribution and the distribution of the resulting segmentation, which were recently proposed in order to overcome the limitations of traditional pixelwise (local) consistency measures. The main feature of our proposal is that it uses two (foreground and background) input distributions, which increases the robustness compared to previous studies. To achieve this, we formulated a new mathematical model that describes the consistencies between the two input distributions and the segmentation, in which weighting parameters for the two distribution matching terms are set to be approximately proportional to the size of the foreground and background areas. We call this dual distribution matching (DDM). We also derived an optimization method that uses graph cuts. Experimental results that show the effectiveness of our method and comparisons between local and global consistency measures are presented.
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| 关 键 词: |
图像分割方法; 传统像素; 双重分布匹配
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| 课程来源: |
视频讲座网
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| 最后编审: |
2021-01-31：nkq
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| 阅读次数: |
46
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