Robust adaptive segmentation of range images

Kil Moo Lee, Peter Meer, Rae Hong Park

Research output: Contribution to journalArticlepeer-review

112 Scopus citations

Abstract

We propose a novel image segmentation technique using the robust adaptive least kth order squares (ALKS) estimator which minimizes the kth order statistics of the squared of residuals. The optimal value of k is determined from the data and the procedure detects the homogeneous surface patch representing the relative majority of the pixels. The ALKS shows a better tolerance to structured outliers than other recently proposed similar techniques: Minimize the Probability of Randomness (MINPRAN) and Residual Consensus (RESC). The performance of the new fully autonomous range image segmentation algorithm is compared to several other methods.

Original languageEnglish (US)
Pages (from-to)200-205
Number of pages6
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume20
Issue number2
DOIs
StatePublished - 1998

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

Keywords

  • Autonomous image analysis
  • Least wh order squares
  • Range image segmentation
  • Robust methods
  • Surface fitting

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