Line drawing interpretation using belief propagation

Yansheng Ming*, Hongdong Li, Jun Sun

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    The interpretation of line drawings of trihedral planer objects is a classic problem. In this paper, it is formulated as a Bayesian inference problem. Given a line drawing image, a Markov random field can be built whose nodes represent the labels of edges. Its clique potential functions are designed to encode the valid junctions in the Huffman-Clowes catalogue. The belief propagation algorithm is used to find the most probable labeling of the edges. We find this algorithm closely related to the arc consistency methods. However our probabilistic formulation can accommodate uncertainty in junction detection and make use of various image cues. These advantages are demonstrated in the experiments.

    Original languageEnglish
    Title of host publicationProceedings - 2011 International Conference on Digital Image Computing
    Subtitle of host publicationTechniques and Applications, DICTA 2011
    Pages113-118
    Number of pages6
    DOIs
    Publication statusPublished - 2011
    Event2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011 - Noosa, QLD, Australia
    Duration: 6 Dec 20118 Dec 2011

    Publication series

    NameProceedings - 2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011

    Conference

    Conference2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011
    Country/TerritoryAustralia
    CityNoosa, QLD
    Period6/12/118/12/11

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