An estimation-based approach for range image segmentation: on the reliability of primitive extraction

G.Y. Wang, Z. Houkes, Guangrong Ji, Bing Zheng, Xin Li

    Research output: Contribution to journalArticleAcademicpeer-review

    15 Citations (Scopus)

    Abstract

    This paper presents a new algorithm for estimation-based range image segmentation. Aiming at surface-primitive extraction from range data, we focus on the reliability of the primitive representation in the process of region estimation. We introduce an optimal description of surface primitives, by which the uncertainty of a region estimate is explicitly represented with a covariance matrix. Then the reliability of an estimate is interpreted in terms of “measure of uncertainty”. The segmentation approach follows the region-growing scheme, in which the regions are estimated in an iterative way. With the probabilistic model proposed in this paper, surface homogeneity is defined and tested by an optimal criterion. A notable feature of the algorithm is that the order of merging is organized to lead the growth towards the most reliable representation of the merged region. Concerned with man-made objects in the scene, we restrict the class of surface primitives to be quadric or planar. The proposed algorithm has been applied to real data and synthetic data and demonstrated with experimental results.
    Original languageUndefined
    Pages (from-to)157-169
    Number of pages13
    JournalPattern recognition
    Volume36
    Issue number1
    DOIs
    Publication statusPublished - 2002

    Keywords

    • Segmentation
    • Quadrics
    • Uncertainty
    • IR-74887
    • Primitive extraction
    • Optimal description
    • METIS-207016
    • Region growing

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