@inproceedings{b853f8094f63433bb9b2afabd98af1db,
title = "Rotation-Invariant Fast Template Matching Based on Sequential Monte Carlo",
abstract = "Template matching is widely applied in Computer Vision. In the case of a template rotation application, it is still nontrivial to find a template matching method with satisfactory matching accuracy and computational complexity. In this work, we propose a fast template matching method based on Sequential Monte Carlo. The method treats the matching process via a Hidden Markov Model(HMM) which establishes a Bayesian framework providing an approximated solution by an importance sampling approach. This solution is utilized to match the template and estimate the position of target template in a background image. Experimental results show a promising template matching improvement in both matching accuracy and matching time.",
keywords = "Bayessian model, HMM, fast template matching, rotation-invariant",
author = "Cuifang Xie and Min Guo and Hongfei Feng and Chen Wong and Lei Sun",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019 ; Conference date: 11-12-2019 Through 13-12-2019",
year = "2019",
month = dec,
doi = "10.1109/ICSIDP47821.2019.9173101",
language = "English",
series = "ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019",
address = "United States",
}