Efficient Multi-structure Robust Fitting with Incremental Top-k Lists Comparison

  • Hoi Sim Wong
  • Tat-Jun Chin
  • Jin Yu
  • David Suter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6495)


Random hypothesis sampling lies at the core of many popular robust fitting techniques such as RANSAC. In this paper, we propose a novel hypothesis sampling scheme based on incremental computation of distances between partial rankings (top-k lists) derived from residual sorting information. Our method simultaneously (1) guides the sampling such that hypotheses corresponding to all true structures can be quickly retrieved and (2) filters the hypotheses such that only a small but very promising subset remain. This permits the usage of simple agglomerative clustering on the surviving hypotheses for accurate model selection. The outcome is a highly efficient multi-structure robust estimation technique. Experiments on synthetic and real data show the superior performance of our approach over previous methods.


Minimal Subset Partial Ranking Incremental Computation Randomize Hough Transform Planar Homographies 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hoi Sim Wong
    • 1
  • Tat-Jun Chin
    • 1
  • Jin Yu
    • 1
  • David Suter
    • 1
  1. 1.School of Computer ScienceThe University of AdelaideAustralia

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