Efficient kernel density estimation using the fast Gauss transform with applications to color modeling and tracking

Ahmed Elgammal, Ramani Duraiswami, Larry S. Davis

Research output: Contribution to journalArticlepeer-review

169 Scopus citations

Abstract

Many vision algorithms depend on the estimation of a probability density function from observations. Kernel density estimation techniques are quite general and powerful methods for this problem, but have a significant disadvantage in that they are computationally intensive. In this paper, we explore the use of kernel density estimation with the fast Gauss transform (FGT) for problems in vision. The FGT allows the summation of a mixture of M Gaussians at N evaluation points in O(M + N) time, as opposed to O(MN) time for a naive evaluation and can be used to considerably speed up kernel density estimation. We present applications of the technique to problems from image segmentation and tracking and show that the algorithm allows application of advanced statistical techniques to solve practical vision problems in real-time with today's computers.

Original languageEnglish (US)
Pages (from-to)1499-1504
Number of pages6
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume25
Issue number11
DOIs
StatePublished - Nov 2003

All Science Journal Classification (ASJC) codes

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

Keywords

  • Color modeling
  • Fast Gauss transform
  • Kernel density estimation
  • Statistical methods
  • Tracking

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