Proceedings of 2013 International Conference on Information Science and Computer Applications

Image Mosaic Algorithm and Its Application to the Microimage of Grass Seeds

Authors
Lina Ning, Xin Pan, Lin Zhai, Fan Han
Corresponding Author
Lina Ning
Available Online October 2013.
DOI
https://doi.org/10.2991/isca-13.2013.31How to use a DOI?
Keywords
image mosaic, SIFT, RANSAC, adaptive Gamma correction
Abstract
This paper presents an image mosaic algorithm and its application to the microimage of grass seeds. The following main steps are involved, firstly in the registration stage, the scale invariant feature transform (SIFT) is employed to obtain initial matches. Then the Random Sample Consensus (RANSAC) is used to remove incorrect matches effectively. Finally in the fusion stage, fade-in and fade-out method is applied to smooth seams which exist in the stitched image. Since the effect is not obvious, the adaptive Gamma correction method is used to weaken illumination effect on image quality to obtain a stitched image, which contains more information than each of the original images. Experimental results demonstrate that the proposed approach achieves good performance for grass seeds microimage mosaic in both subjective and objective evaluations.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2013 International Conference on Information Science and Computer Applications (ISCA 2013)
Part of series
Advances in Intelligent Systems Research
Publication Date
October 2013
ISBN
978-90786-77-85-7
ISSN
1951-6851
DOI
https://doi.org/10.2991/isca-13.2013.31How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Lina Ning
AU  - Xin Pan
AU  - Lin Zhai
AU  - Fan Han
PY  - 2013/10
DA  - 2013/10
TI  - Image Mosaic Algorithm and Its Application to the Microimage of Grass Seeds
BT  - 2013 International Conference on Information Science and Computer Applications (ISCA 2013)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/isca-13.2013.31
DO  - https://doi.org/10.2991/isca-13.2013.31
ID  - Ning2013/10
ER  -