清华主页 EN
导航菜单

Image Analysis using Persistent Homology | BIMSA Topology Seminar

来源: 04-17

时间:2024-04-18 Thu 14:30-15:30

地点:A3-4-101 ZOOM: 928 682 9093 BIMSA

组织者:Matthew Burfitt, Jingyan Li, Jie Wu, Jiawei Zhou

主讲人:Shizuo Kaji Kyushu University

Abstract

Deep convolutional networks have proved to be extremely powerful in image analysis. However.they tend to be biased toward local features such as texture and often fail to capture the globalstructure of image and volume data. Persistent homology (PH), a tool from the emerging field oftopological data analysis, has been successfully used to detect global characteristics of data thatare overlooked by conventional methods. We present Cubical Ripser, an open-source softwaredesigned for high-efficiency computation of PH of cubical complexes. We will discuss how PH canbe integrated into a standard image analysis pipeline with some practical applications.


返回顶部
相关文章
  • Persistent homology filtering of signals over graphs | BIMSA Topology Seminar

    AbstractPersistent homology provides essential topological insights into datasets, representing eachtopological feature with an interval whose length, known as the feature's lifetime, represents itspersistence. Features with short lifetimes are typically regarded as noise, while those with longellifetimes are considered meaningful characteristics of the dataset. We introduce a novel filteringme...

  • Persistent homology and GLMY-homology

    PrerequisiteThe elementary theory about algebraic topologyAbstractIn applied mathematics, topological based data analysis (TDA) is an approach to the analysis of datasets using techniques from topology. In this course we will concentrate mainly on two such techniques: persistent homology and GLMY-homology. Persistent homology is an algebraic tool for measuring topological features of shapes and...