Development of laser speckle metrology and its identification techniques

Ruey Nan Yeh; Po Yi Sung; Chia Hung Yeh; Wen Yu Tseng; Jin Wei Yeh; Yen Hao Chang

doi:10.1109/ICMLC.2011.6016876

Development of laser speckle metrology and its identification techniques

Ruey Nan Yeh^*, Po Yi Sung, Chia Hung Yeh, Wen Yu Tseng, Jin Wei Yeh, Yen Hao Chang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

The main goal of this paper is to identify last speckle images rapidly. Digital image processing techniques are employed to analyze the characteristics of laser speckle images and match them up to achieve laser speckle image identification. Besides the database is built to accelerate the identification process and further enhance its practicability. In terms of building the database, Gabor filter is utilized to enhance the extracted characteristics as well as to generate the feature vectors. The final step is adopting K-means clustering to build the classification model of feature vectors. The process of identifying laser speckle images is described as follows. Through experiments we observed that scale invariant feature transform (SIFT) can extract features of laser speckle images very well. However the drawback is that it took too much time to compute and match up those features, which is not suitable for fast laser speckle identification. Therefore the proposed method took enhance SIFT as backbone. Experimental results demonstrate that the retrieval performance of the proposed method is accurate when the database size contains 516 images.

Original language	English
Title of host publication	Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Pages	1381-1385
Number of pages	5
DOIs	https://doi.org/10.1109/ICMLC.2011.6016876
Publication status	Published - 2011
Externally published	Yes
Event	2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 - Guilin, Guangxi, China Duration: 2011 Jul 10 → 2011 Jul 13

Publication series

Name	Proceedings - International Conference on Machine Learning and Cybernetics
Volume	3
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Conference

Conference	2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Country/Territory	China
City	Guilin, Guangxi
Period	2011/07/10 → 2011/07/13

Keywords

Gabor feature
K-means
Laser speckle image
SIFT

ASJC Scopus subject areas

Artificial Intelligence
Computational Theory and Mathematics
Computer Networks and Communications
Human-Computer Interaction

Access to Document

10.1109/ICMLC.2011.6016876

Cite this

Yeh, R. N., Sung, P. Y., Yeh, C. H., Tseng, W. Y., Yeh, J. W., & Chang, Y. H. (2011). Development of laser speckle metrology and its identification techniques. In Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 (pp. 1381-1385). Article 6016876 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 3). https://doi.org/10.1109/ICMLC.2011.6016876

Development of laser speckle metrology and its identification techniques. / Yeh, Ruey Nan; Sung, Po Yi; Yeh, Chia Hung et al.
Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011. 2011. p. 1381-1385 6016876 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yeh, RN, Sung, PY, Yeh, CH, Tseng, WY, Yeh, JW & Chang, YH 2011, Development of laser speckle metrology and its identification techniques. in Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011., 6016876, Proceedings - International Conference on Machine Learning and Cybernetics, vol. 3, pp. 1381-1385, 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011, Guilin, Guangxi, China, 2011/07/10. https://doi.org/10.1109/ICMLC.2011.6016876

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abstract = "The main goal of this paper is to identify last speckle images rapidly. Digital image processing techniques are employed to analyze the characteristics of laser speckle images and match them up to achieve laser speckle image identification. Besides the database is built to accelerate the identification process and further enhance its practicability. In terms of building the database, Gabor filter is utilized to enhance the extracted characteristics as well as to generate the feature vectors. The final step is adopting K-means clustering to build the classification model of feature vectors. The process of identifying laser speckle images is described as follows. Through experiments we observed that scale invariant feature transform (SIFT) can extract features of laser speckle images very well. However the drawback is that it took too much time to compute and match up those features, which is not suitable for fast laser speckle identification. Therefore the proposed method took enhance SIFT as backbone. Experimental results demonstrate that the retrieval performance of the proposed method is accurate when the database size contains 516 images.",

keywords = "Gabor feature, K-means, Laser speckle image, SIFT",

author = "Yeh, {Ruey Nan} and Sung, {Po Yi} and Yeh, {Chia Hung} and Tseng, {Wen Yu} and Yeh, {Jin Wei} and Chang, {Yen Hao}",

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AU - Chang, Yen Hao

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AB - The main goal of this paper is to identify last speckle images rapidly. Digital image processing techniques are employed to analyze the characteristics of laser speckle images and match them up to achieve laser speckle image identification. Besides the database is built to accelerate the identification process and further enhance its practicability. In terms of building the database, Gabor filter is utilized to enhance the extracted characteristics as well as to generate the feature vectors. The final step is adopting K-means clustering to build the classification model of feature vectors. The process of identifying laser speckle images is described as follows. Through experiments we observed that scale invariant feature transform (SIFT) can extract features of laser speckle images very well. However the drawback is that it took too much time to compute and match up those features, which is not suitable for fast laser speckle identification. Therefore the proposed method took enhance SIFT as backbone. Experimental results demonstrate that the retrieval performance of the proposed method is accurate when the database size contains 516 images.

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Development of laser speckle metrology and its identification techniques

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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