A probabilistic approach to pattern matching in the continuous domain

Daniel Keren; Michael Werman; Joshua Feinberg

doi:10.1109/TPAMI.2011.284

A probabilistic approach to pattern matching in the continuous domain

Daniel Keren, Michael Werman, Joshua Feinberg

Research output: Contribution to journal › Article › peer-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 language	English
Article number	6122033
Pages (from-to)	1873-1885
Number of pages	13
Journal	IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume	34
Issue number	10
DOIs	https://doi.org/10.1109/TPAMI.2011.284
State	Published - 2012

Keywords

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

ASJC Scopus subject areas

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

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10.1109/TPAMI.2011.284

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KW - distance between signals

KW - energy of a signal

KW - path integrals

KW - probability

KW - regularization

KW - sampling

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A probabilistic approach to pattern matching in the continuous domain

Abstract

Keywords

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