A probabilistic approach to pattern matching in the continuous domain

Daniel Keren, Michael Werman, Joshua Feinberg

Research output: Contribution to journalArticlepeer-review

Abstract

The goal of this paper is to solve the following basic problem: Given discrete noisy samples from a continuous signal, compute the probability distribution of its distance from a fixed template. As opposed to the typical restoration problem, which considers a single optimal signal, the computation of the entire probability distribution necessitates integrating over the entire signal space. To achieve this, we apply path integration techniques. The problem is studied in one and two dimensions, and an accurate solution as well as an efficient approximation scheme are provided.

Original languageEnglish
Article number6122033
Pages (from-to)1873-1885
Number of pages13
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume34
Issue number10
DOIs
StatePublished - 2012

Keywords

  • Pattern matching
  • distance between signals
  • energy of a signal
  • path integrals
  • probability
  • regularization
  • sampling

ASJC Scopus subject areas

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

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