Consideration is given to the application of Markov random field (MRF) models to the problem of edge labeling in range images. The authors propose a segmentation algorithm which handles both jump and crease edges. The jump and crease edge likelihoods at each edge site are computed using special local operators. These likelihoods are then combined in a Bayesian framework with a MRF prior distribution on the edge labels to derive the a posterior distribution of labels. An approximation to the maximum a posteriori estimate is used to obtain the edge labelings. The edge-based segmentation has been integrated with a region-based segmentation scheme resulting in a robust surface segmentation method.<>


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    MRF model-based segmentation of range images


    Contributors:
    Jain, A.K. (author) / Nadabar, S.G. (author)


    Publication date :

    1990-01-01


    Size :

    419687 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    SEGMENTATION OF LIDAR RANGE IMAGES

    SMOLYANSKIY NIKOLAI / OLDJA RYAN / CHEN KE et al. | European Patent Office | 2021

    Free access

    Segmentation of lidar range images

    SMOLYANSKIY NIKOLAI / OLDJA RYAN / CHEN KE et al. | European Patent Office | 2024

    Free access

    Segmentation of range images into planar regions

    Checchin, P. / Trassoudaine, L. / Alizon, J. | IEEE | 1997


    Segmentation of Range Images into Planar Regions

    Checchin, P. / Trassoudaine, L. / Alizon, J. et al. | British Library Conference Proceedings | 1997


    3-D Range Images Segmentation Based on Deriche's Optimum Filters

    Djebali, M. / Melkemi, M. / Vandorpe, D. et al. | British Library Conference Proceedings | 1994