"形状特征描述与聚类算法研究：计算机视觉中的关键问题"

该研究论文主要讨论了形状特征描述及聚类算法在人工智能、计算机视觉和模式识别领域的关键性用途。在形状聚类中，如何在没有任何给定信息的情况下选择特征是一个常见的难题。近年来，动态规划、谱图论、中轴转换和数据降维等方法受到了广泛关注。该研究旨在通过对动态规划、谱图论、中轴转换和数据降维等方法的综述，探讨在形状特征描述和聚类算法中的应用和改进。通过论文的分析和总结，可以发现这些方法在解决形状聚类问题中发挥着重要作用，并为进一步研究提供了有益的参考和启示。Shape recognition and clustering are key issues in artificial intelligence, computer vision, and pattern recognition. One of the most common problems in shape clustering is how to select features without any given information. In recent years, dynamic programming, spectral graph theory, medial axis transformation, and data dimensionality reduction have received a lot of attention. This dissertation mainly discusses the application and improvement of dynamic programming, spectral graph theory, medial axis transformation, and data dimensionality reduction methods in shape feature description and clustering algorithms. Through the review of these methods, it is found that they play an important role in solving shape clustering problems and provide useful references and inspirations for further research.