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Multi-Bernoulli sample consensus for simultaneous robust fitting of multiple structures in machine vision

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Abstract

In many image processing applications, such as parametric range and motion segmentation, multiple instances of a model are fitted to data points. The most common robust fitting method, RANSAC, and its extensions are normally devised to segment the structures sequentially, treating the points belonging to other structures as outliers. Thus, the ratio of inliers is small and successful fitting requires a very large number of random samples, incurring cumbrous computation. This paper presents a new method to simultaneously fit multiple structures to data points in a single run. We model the parameters of multiple structures as a random finite set with multi-Bernoulli distribution. Simultaneous search for all structure parameters is performed by Bayesian update of the multi-Bernoulli parameters. Experiments involving segmentation of numerous structures show that our method outperforms well-known methods in terms of estimation error and computational cost. The fast convergence and high accuracy of our method make it an excellent choice for real-time estimation and segmentation of multiple structures in image processing applications.

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Notes

  1. In the related literature, the term “elemental subsets” has been also used for \(p\)-tuples.

  2. In some cases, the independence assumption is not perfectly correct. For instance, in case of estimating and segmenting multiple motions, the motions can be influencing each other due to the objects interacting with each other.

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Authors and Affiliations

  1. RMIT University, Victoria, Australia

    Reza Hoseinnezhad & Alireza Bab-Hadiashar

Authors
  1. Reza Hoseinnezhad
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  2. Alireza Bab-Hadiashar
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Correspondence to Reza Hoseinnezhad.

Additional information

This work was supported by ARC Discovery Projects Grants DP130104404 and DP130102524.

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Hoseinnezhad, R., Bab-Hadiashar, A. Multi-Bernoulli sample consensus for simultaneous robust fitting of multiple structures in machine vision. SIViP 9, 1727–1736 (2015). https://doi.org/10.1007/s11760-014-0632-9

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  • DOI: https://doi.org/10.1007/s11760-014-0632-9

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