分形与多尺度分维分析：Bouligand-Minkowski方法的比较研究
Fractal and Multi-Scale Fractal Dimension analysis: a comparative study of Bouligand-Minkowski method
摘要：
形状是表征物体最重要的视觉属性之一，在模式识别中起着重要的作用。有各种方法来提取形状的相关信息。复杂度是形状分析中广泛使用的一种方法，分形维数和多尺度分形维数都是常用的复杂度估计方法，本文将分形维数和多尺度分形维数在形状分析中进行了比较研究。通过实验比较使用的形状数据库预先分类，这两种方法进行了比较。考虑了每种方法的不同参数配置，并对每种方法的结果进行了讨论。
英文摘要：
Shape is one of the most important visual attributes to characterize objects, playing a important role in pattern recognition. There are various approaches to extract relevant information of a shape. An approach widely used in shape analysis is the complexity, and Fractal Dimension and Multi-Scale Fractal Dimension are both well-known methodologies to estimate it. This papers presents a comparative study between Fractal Dimension and Multi-Scale Fractal Dimension in a shape analysis context. Through experimental comparison using a shape database previously classified, both methods are compared. Different parameters configuration of each method are considered and a discussion about the results of each method is also presented.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
