A generalized kernel consensus-based robust estimator

Hanzi Wang, Daniel Mirota, Gregory Hager

Research output: Contribution to journalArticle

Abstract

In this paper, we present a new Adaptive-Scale Kernel Consensus (ASKC) robust estimator as a generalization of the popular and state-of-the-art robust estimators such as RANdom SAmple Consensus (RANSAC), Adaptive Scale Sample Consensus (ASSC), and Maximum Kernel Density Estimator (MKDE). The ASKC framework is grounded on and unifies these robust estimators using nonparametric kernel density estimation theory. In particular, we show that each of these methods is a special case of ASKC using a specific kernel. Like these methods, ASKC can tolerate more than 50 percent outliers, but it can also automatically estimate the scale of inliers. We apply ASKC to two important areas in computer vision, robust motion estimation and pose estimation, and show comparative results on both synthetic and real data.

Original languageEnglish (US)
Article number5184846
Pages (from-to)178-184
Number of pages7
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume32
Issue number1
DOIs
StatePublished - 2010

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Keywords

  • Kernel density estimation
  • Model fitting
  • Motion estimation
  • Pose estimation
  • Robust statistics

ASJC Scopus subject areas

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

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