@inproceedings{36b91afc35cc4c96ae56117f0a646430,
title = "Gaussian muti-scale fast registration algorithm and its application in machine vision",
abstract = "To meet the real-time requirement of image processing in machine vision applications, a novel Gaussian Muti-scale Fast Registration (GMFR) algorithm is proposed. GMFR avoids the problem of losing details of images in sub-sampling process by means of Gaussian filtering based on muti-scale images. The advantage of image Cross-Correlation (CC) and Cross-Power Spectrum (CPS) are integrated by the presented method to achieve both accuracy and efficiency of image registration. The performance of GMFR is analyzed quantificationally based on the pre-defined performance function, which shows that the advantage of GMFR algorithm increases exponentially as the image gets larger. A machine vision environment is built for surface defects detection of lead frames and verifies the accuracy, robustness and high computing efficiency of GMFR in noisy image registration.",
author = "Cao, \{X. H.\} and Yang, \{J. H.\} and Y. Dai",
note = "Publisher Copyright: {\textcopyright} 2015 Taylor \& Francis Group, London.; International Conference on Testing and Measurement: Techniques and Applications, TMTA 2015 ; Conference date: 16-01-2015 Through 17-01-2015",
year = "2015",
language = "英语",
isbn = "9781138028128",
series = "Testing and Measurement: Techniques and Applications - Proceedings of the 2015 International Conference on Testing and Measurement: Techniques and Applications, TMTA 2015",
publisher = "CRC Press/Balkema",
pages = "325--328",
editor = "Kennis Chan",
booktitle = "Testing and Measurement",
}