Full Text:
<3235>
CLC number: TP391.4
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 0
Clicked: 7205
Correlation analysis-based image segmentation approach for automatic agriculture vehicle
| |||||||||||||
ZHANG Fang-ming, YING Yi-bin, JIANG Huan-yu, SHIN Beom-soo. Correlation analysis-based image segmentation approach for automatic agriculture vehicle[J]. Journal of Zhejiang University Science A, 2005, 6(10): 1158-1162.
@article{title="Correlation analysis-based image segmentation approach for automatic agriculture vehicle",
author="ZHANG Fang-ming, YING Yi-bin, JIANG Huan-yu, SHIN Beom-soo",
journal="Journal of Zhejiang University Science A",
volume="6",
number="10",
pages="1158-1162",
year="2005",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2005.A1158"
}
%0 Journal Article
%T Correlation analysis-based image segmentation approach for automatic agriculture vehicle
%A ZHANG Fang-ming
%A YING Yi-bin
%A JIANG Huan-yu
%A SHIN Beom-soo
%J Journal of Zhejiang University SCIENCE A
%V 6
%N 10
%P 1158-1162
%@ 1673-565X
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A1158
TY - JOUR
T1 - Correlation analysis-based image segmentation approach for automatic agriculture vehicle
A1 - ZHANG Fang-ming
A1 - YING Yi-bin
A1 - JIANG Huan-yu
A1 - SHIN Beom-soo
J0 - Journal of Zhejiang University Science A
VL - 6
IS - 10
SP - 1158
EP - 1162
%@ 1673-565X
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.A1158
Abstract: It is important to segment image correctly to extract guidance information for automatic agriculture vehicle. If we can make the computer know where the crops are, we can extract the guidance line easily. Images were divided into some rectangle small windows, then a pair of 1-D arrays was constructed in each small windows. The correlation coefficients of every small window constructed the features to segment images. The results showed that correlation analysis is a potential approach for processing complex farmland for guidance system, and more correlation analysis methods must be researched.
[1] Benson, E.R., 2001. Vision Based Guidance of an Agricultural Combine. Ph.D Thesis, UIUC, p.116-123.
[2] Debain, C., Chateau, T., Berducat, M., Martinet, P., Bonton, P., 2000. A guidance-assistance system for agricultural vehicles. Computers and Electronics in Agriculture, 25:29-51.
[3] Francisco, R.M., Zhang, Q., Reid, J.F., 2003. Automatic Navigation with Stereo Disparity. ASAE Annual International Meeting. Las Vegas, Nevada, USA.
[4] Kise, M., Zhang, Q., Francisco, R.M., 2004. Stereovision-based 3D Field Recognition for Agricultural Vehicle. Proceedings of the CIGR International Conference, Beijing, Session V.
[5] Marchant, J.A., 1996. Tracking of row structure in three crops using image analysis. Computers and Electronics in Agriculture, 15:161-179.
[6] Marchant, J.A., Brivot, A.R., 1995. Real-time tracking of plant rows using a Hough transform. Real-Time Imaging, 1:363-371.
[7] Ollis, M., Stentz, A., 1997. First Results in Vision-based Crop Line Tracking. Proceedings of the IEEE Robotics and Automation Conference, Minneapolis, Minnesota, USA, 1:951-956.
[8] O’Sulliran, J.A., Montagnino, L.J., 2004. Azimuth Correlation Models for Radar Imaging. Proceedings of SPIE—Algorithms for Synthetic Aperture Radar Imagery, 5095:45-53.
[9] Searcy, S.W., Reid, J.F., 1987. Vision-based guidance of an agricultural tractor. IEEE Control Systems, 12:39-43.
[10] Shen, M.X., Li, X.Z., Ji, C.Y., 2003. Detection of areas of cropland scenery using morphology. Transactions of the Chinese Society for Agricultural Machinery, 34(1):92-94.
[11] Torii, T., Kanuma, T., Okamoto, T., 1996. Image Analysis of Crop Row for Agricultural Mobile Robot. Proceedings of AGENG96, Madrid, Spain, p.1045-1046.
Open peer comments: Debate/Discuss/Question/Opinion
<1>