Heat Propagation Contours for 3D Non-rigid Shape Analysis

    Research output: Chapter in Book/Conference paperConference paper

    4 Citations (Scopus)


    We present a novel local shape descriptor by means of General Adaptive Neighborhoods (GANs) based on the properties of the heat diffusion process on a Riemannian manifold. The GAN is a spatial region, surrounding the feature point and fitting its local shape structure, which is isometric. Our signature, called the Heat Propagation Contours (HPCs), is obtained by analysing the well-known heat kernel and extracting contours automatically within the GAN as heat dissipates from the feature point onto the rest of the shape. HPCs capture geometric information around the feature point by investigating the heat propagation process both in the temporal and spatial domain. HPCs share many useful characteristics with the heat based methods. Particularly, it captures the intrinsic geometry of a shape and is suitable for non-rigid shape analysis. In addition, our signature provides an elegant and efficient way to describe the neighborhood of the feature point in a multi-scale approach. The proposed descriptor is evaluated on several datasets to demonstrate its effectiveness. © 2016 IEEE.
    Original languageEnglish
    Title of host publication2016 IEEE Winter Conference on Applications of Computer Vision (WACV)
    EditorsA Hoogs, L Davis
    Place of PublicationUnited States
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Number of pages7
    ISBN (Print)9781509006410
    Publication statusPublished - 2016
    Event2016 IEEE Winter Conference on Applications of Computer Vision - Lake Placid, United States
    Duration: 7 Mar 201610 Mar 2016


    Conference2016 IEEE Winter Conference on Applications of Computer Vision
    Abbreviated titleWACV 2016
    CountryUnited States
    CityLake Placid

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