A Probabilistic Approach to Pattern Matching in the Continuous Domain

Daniel Keren; Michael Werman; Joshua Feinberg

doi:10.1109/TPAMI.2011.284

Publication
2012
Issue No. 10 - Oct.
Abstract - A Probabilistic Approach to Pattern Matching in the Continuous Domain

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A Probabilistic Approach to Pattern Matching in the Continuous Domain

Oct. 2012 (vol. 34 no. 10)

pp. 1873-1885

	ASCII Text	x
	Daniel Keren, Michael Werman, Joshua Feinberg, "A Probabilistic Approach to Pattern Matching in the Continuous Domain," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 10, pp. 1873-1885, Oct., 2012.

	BibTex	x
	@article{ 10.1109/TPAMI.2011.284, author = {Daniel Keren and Michael Werman and Joshua Feinberg}, title = {A Probabilistic Approach to Pattern Matching in the Continuous Domain}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {34}, number = {10}, issn = {0162-8828}, year = {2012}, pages = {1873-1885}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.284}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - A Probabilistic Approach to Pattern Matching in the Continuous Domain IS - 10 SN - 0162-8828 SP1873 EP1885 EPD - 1873-1885 A1 - Daniel Keren, A1 - Michael Werman, A1 - Joshua Feinberg, PY - 2012 KW - Noise measurement KW - Probability distribution KW - Probabilistic logic KW - Physics KW - Uncertainty KW - Pattern matching KW - path integrals. KW - Pattern matching KW - distance between signals KW - sampling KW - energy of a signal KW - regularization KW - probability VL - 34 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER -

Daniel Keren, University of Haifa, Haifa

Michael Werman, Hebrew University of Jerusalem, Jerusalem

Joshua Feinberg, University of Haifa at Oranim, Tivon and Technion, Haifa/onm>

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.284

Web Extra: View Supplemental Material(PDF)

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.

Index Terms:

Noise measurement,Probability distribution,Probabilistic logic,Physics,Uncertainty,Pattern matching,path integrals.,Pattern matching,distance between signals,sampling,energy of a signal,regularization,probability

Citation:

Daniel Keren, Michael Werman, Joshua Feinberg, "A Probabilistic Approach to Pattern Matching in the Continuous Domain," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 10, pp. 1873-1885, Oct. 2012, doi:10.1109/TPAMI.2011.284

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