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A Probabilistic Approach to Pattern Matching in the Continuous Domain
Oct. 2012 (vol. 34 no. 10)
pp. 1873-1885
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>
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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