• Login
    View Item 
    •   Eurographics DL Home
    • Computer Graphics Forum
    • Volume 33 (2014)
    • 33-Issue 1
    • View Item
    •   Eurographics DL Home
    • Computer Graphics Forum
    • Volume 33 (2014)
    • 33-Issue 1
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Multi‐Scale Kernels Using Random Walks

    Thumbnail
    View/Open
    v33i1pp164-177.pdf (4.559Mb)
    Date
    2014
    Author
    Sinha, A.
    Ramani, K.
    Pay-Per-View via TIB Hannover:

    Try if this item/paper is available.

    Metadata
    Show full item record
    Abstract
    We introduce novel multi‐scale kernels using the random walk framework and derive corresponding embeddings and pairwise distances. The fractional moments of the rate of continuous time random walk (equivalently diffusion rate) are used to discover higher order kernels (or similarities) between pair of points. The formulated kernels are isometry, scale and tessellation invariant, can be made globally or locally shape aware and are insensitive to partial objects and noise based on the moment and influence parameters. In addition, the corresponding kernel distances and embeddings are convergent and efficiently computable. We introduce dual Green's mean signatures based on the kernels and discuss the applicability of the multi‐scale distance and embedding. Collectively, we present a unified view of popular embeddings and distance metrics while recovering intuitive probabilistic interpretations on discrete surface meshes.We introduce novel multi‐scale kernels using the random walk framework and derive corresponding embeddings and pairwise distances. The fractional moments of the rate of continuous time random walk (equivalently diffusion rate) are used to discover higher order kernels (or similarities) between pair of points. The formulated kernels are isometry, scale and tessellation invariant, can be made globally or locally shape aware and are insensitive to partial objects and noise based on the moment and influence parameters. In addition, the corresponding kernel distances and embeddings are convergent and efficiently computable.
    BibTeX
    @article {10.1111:cgf.12264,
    journal = {Computer Graphics Forum},
    title = {{Multi‐Scale Kernels Using Random Walks}},
    author = {Sinha, A. and Ramani, K.},
    year = {2014},
    publisher = {The Eurographics Association and John Wiley and Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.12264}
    }
    URI
    http://dx.doi.org/10.1111/cgf.12264
    Collections
    • 33-Issue 1

    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA
     

     

    Browse

    All of Eurographics DLCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    BibTeX | TOC

    Create BibTeX Create Table of Contents

    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA