A Topological Graph-Based Representation for Denoising Low Quality Binary Images
A Topological Graph-Based Representation for Denoising Low Quality Binary Images
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DOI：
10.1109/iccvw.2019.00222
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Catherine Potts;Liping Yang
中科院分区：
文献类型：
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作者：
Catherine Potts;Liping Yang
专利或历史文献的扫描图像往往包含数字化过程中引入的局部之字形噪声；然而，从整体上看，全球结构对人类来说是显而易见的，但对机器来说却并非如此。现有的去噪方法对自然图像效果很好，但对二值图图像效果不佳，这使得计算机视觉和机器学习方法和算法难以提取特征。我们提出了一种基于拓扑图的表示来解决这个去噪问题。图形表示强调图表图像的形状和拓扑结构，使其非常适合用于机器学习应用，例如科学图表图像的分类和匹配。我们的方法和算法为计算机视觉提供了基本的结构，并为基于场景图的应用奠定了重要的基础，因为图像中对象之间的拓扑关系和空间排列被捕获并存储在我们的骨架图中。此外，尽管几乎所有基于像素的方法的参数都是不自适应的，但我们的方法具有鲁棒性，因为它只需要一个参数并且是自适应的。与现有方法的实验比较表明了该方法的有效性。
Scanned images of patent or historical documents often contain localized zigzag noise introduced by the digitizing process; yet when viewed as a whole image, global structures are apparent to humans, but not to machines. Existing denoising methods work well for natural images, but not for binary diagram images, which makes feature extraction difficult for computer vision and machine learning methods and algorithms. We propose a topological graph-based representation to tackle this denoising problem. The graph representation emphasizes the shapes and topology of diagram images, making it ideal for use in machine learning applications such as classification and matching of scientific diagram images. Our approach and algorithms provide essential structure and lay important foundation for computer vision such as scene graph-based applications, because topological relations and spatial arrangement among objects in images are captured and stored in our skeleton graph. In addition, while the parameters for almost all pixel-based methods are not adaptive, our method is robust in that it only requires one parameter and it is adaptive. Experimental comparisons with existing methods show the effectiveness of our approach.