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Global Contour and Region Based Shape Analysis and Similarity Measures

机译:基于全局轮廓和区域的形状分析和相似性度量

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摘要

More and more images have been generated in digital form around the world. There is a growing interest in finding images in large collections or from remote databases. In order to find an image, the image has to be described or represented by certain features. Shape is an important visual feature of an image. Searching for images using shape features has attracted much attention. There are many shape representation and description techniques in the literature. Object classification often operates by making decisions based on the values of several shape properties measured from an image of the object. Shape analysis is a useful tool for recognition of an object. This paper treats various aspects that are needed to solve shape matching problems: choosing the precise problem of global contour and region based shape analysis, selecting the properties of the similarity measure that are needed for the problem and choosing the specific similarity measure to compute the similarity.
机译:全球越来越多地以数字形式生成图像。在大型集合中或从远程数据库中查找图像的兴趣日益浓厚。为了找到图像,该图像必须由某些特征来描述或表示。形状是图像的重要视觉特征。使用形状特征搜索图像已引起广泛关注。文献中有许多形状表示和描述技术。对象分类通常通过根据从对象图像测量的几个形状属性的值来做出决策来进行操作。形状分析是识别对象的有用工具。本文讨论了解决形状匹配问题所需的各个方面:选择基于全局轮廓和区域的形状分析的精确问题,选择该问题所需的相似性度量的属性,以及选择特定的相似性度量以计算相似性。

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