A note on conic fitting by the gradient weighted least-squares estimation: Refined eigenvector solution

G.Y. Wang, Z. Houkes, B. Zheng, X. Li

    Research output: Contribution to journalArticleAcademicpeer-review

    5 Citations (Scopus)

    Abstract

    The gradient weighted least-squares criterion is a popular criterion for conic fitting. When the non-linear minimisation problem is solved using the eigenvector method, the minimum is not reached and the resulting solution is an approximation. In this paper, we refine the existing eigenvector method so that the minimisation of the non-linear problem becomes exactly. Consequently we apply the refined algorithm to the re-normalisation approach, by which the new iterative scheme yields to bias-corrected solution but based on the exact minimiser of the cost function. Experimental results show the improved performance of the proposed algorithm.
    Original languageEnglish
    Pages (from-to)1695-1703
    Number of pages9
    JournalPattern recognition letters
    Volume23
    Issue number14
    DOIs
    Publication statusPublished - 2002

    Keywords

    • METIS-206871
    • Eigenvector method
    • Gradient weighted least-squares
    • Bias-corrected
    • IR-74689
    • Conic fitting

    Fingerprint Dive into the research topics of 'A note on conic fitting by the gradient weighted least-squares estimation: Refined eigenvector solution'. Together they form a unique fingerprint.

    Cite this