@inproceedings{0eef43a2fc794679b212297a88e0215b,
title = "MCMC-based peak template matching for GCxGC",
abstract = "Comprehensive two-dimensional gas chromatography (GCxGC) is a new technology for chemical separation. Peak template matching is a technique for automatic chemical identification in GCxGC analysis. Peak template matching can be formulated as a largest common point set problem (LCP). Minimizing Hausdorff distances is one of the many techniques proposed for solving the LCP problem. This paper proposes two novel strategies to search the transformation space based on Markov chain Monte Carlo (MCMC) methods. Experiments on seven real data sets indicate that the transformations found by the new algorithms are effective and searching with two Markov chains is much faster than searching with one Markov chain.",
keywords = "Chemical analysis, Chemical engineering, Chemical technology, Computer science, Gas chromatography, Monte Carlo methods, Pixel, Shape, Space technology, Visualization",
author = "Mingtian Ni and Qingping Tao and Reichenbach, {S. E.}",
note = "Publisher Copyright: {\textcopyright} 2003 IEEE.; IEEE Workshop on Statistical Signal Processing, SSP 2003 ; Conference date: 28-09-2003 Through 01-10-2003",
year = "2003",
doi = "10.1109/SSP.2003.1289460",
language = "English (US)",
series = "IEEE Workshop on Statistical Signal Processing Proceedings",
publisher = "IEEE Computer Society",
pages = "514--517",
booktitle = "Proceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003",
}