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Robust adaptive-scale parametric model estimation for computer vision.

Hanzi Wang1, David Suter

  • 1Department of Electrical and Computer Systems Engineering, Monash University, Clayton Vic. 3800, Australia. hanzi.wang@eng.monash.edu.au

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 4, 2004
PubMed
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We introduce two robust estimators, the Two-Step Scale estimator (TSSE) and the Adaptive Scale Sample Consensus (ASSC) estimator, for model fitting. ASSC significantly improves outlier rejection, handling over 80% outliers without prior scale knowledge.

Area of Science:

  • Computer Vision
  • Statistical Modeling
  • Machine Learning

Background:

  • Robust model fitting requires accurate estimation of both model parameters and inlier data scale.
  • Existing methods like RANSAC often need prior knowledge of the inlier scale, limiting their applicability.
  • Handling data with high outlier percentages and multiple structures remains a challenge.

Purpose of the Study:

  • To propose novel robust estimation techniques for model fitting.
  • To develop an Adaptive Scale Sample Consensus (ASSC) estimator that integrates scale estimation directly into the consensus process.
  • To evaluate the performance of ASSC against existing robust methods, particularly in challenging scenarios.

Main Methods:

  • The Two-Step Scale estimator (TSSE) utilizes nonparametric density and density gradient estimation for robust scale estimation.

Related Experiment Videos

  • The Adaptive Scale Sample Consensus (ASSC) estimator combines RANSAC with TSSE, employing a modified objective function considering inlier count and scale.
  • ASSC is designed to be robust to discontinuous signals and data with multiple structures, tolerating over 80% outliers.

    • Main Results:

    • ASSC demonstrates superior robustness to heavily corrupted data compared to LMedS, RESC, and ALKS on synthetic datasets.
    • ASSC eliminates the need for prior knowledge about the inlier scale, a key advantage over traditional RANSAC.
    • Experiments in computer vision tasks, including range image segmentation and robust fundamental matrix estimation, yielded promising results.

    Conclusions:

    • The proposed ASSC estimator offers a highly robust and versatile approach to model fitting in the presence of significant outliers.
    • ASSC effectively estimates both model parameters and inlier scale simultaneously, simplifying the robust estimation process.
    • ASSC shows significant potential for real-world applications in computer vision and other fields requiring robust data analysis.