Workshop proceeding - final.pdf - Faculty of Information and ...

ase on SIFT are also unsuitable to our application because common key points can hardly be found.
The important reason is that the key points are wholly dependent on the image intensities.
With regard to the running speed, though the images are only zoomed out at 2 times, a higher
running efficiency is obtained. The changeable resolution algorithm only takes less than one fortieth
running time of the standard pixel by pixel cross correlation method, but they have same registration
results, so the changeable resolution algorithm is a fast and effective realization of the NCC method.
6. Conclusion
The paper presented a fast realization algorithm of automatic optical and infrared image registration.
It reduces the computation considerably by utilizing changeable resolution algorithm and has obtained
inspiring results at a fast running speed. It can also be used in other applications. As we mentioned
above, the image registration is only the first work to do, the aim is to estimate the canopy temperature.
But delightful thing is that it is our ongoing research, and some great progresses have been made.
Moreover, how to get more accurate registration and how to make algorithm suit to more variational
background are also our ongoing research.
7. Acknowledgement
We would like to thank Mr Ashley Wheaton and Dr Nicola Cooley for their providing images and
some useful discussions.
References:
[1] H.G. Jones. Use of thermography for quantitative studies of spatial and temporal variation of
stomatal conductance over leaf surfaces. Journal of Plant, Cell and Environment (1999) 22, 1043–
1055.
[2] H.G. Jones. Use of infrared thermometry for estimation of stomatal conductance as a possible aid
to irrigation scheduling. Journal of Agricultural and Forest Meteorology 95 (1999) 139-149.
[3] L. Guilioni , H.G. Jones, I. Leinonen, J.P. Lhomme. On the relationships between stomatal
resistance and leaf temperatures in thermography. Journal of Agricultural and Forest Meteorology
148 (2008): 1908 – 1912.
[4] O. Grant, L. Tronina1, H. G. Jones and M. M. Chaves (2007) Exploring thermal imaging variables
for the detection of stress responses in grapevine under different irrigation regimes. Journal of
Experimental Botany, Vol. 58, No. 4, pp. 815–825, 2007.
[5] Wheaton, A.D., Cooley, N., Dunn, G., Goodwin, I., Needs, S., (2007) Evaluation of infrared
thermography to determine the crop water status of Cabernet Sauvignon grapevines. Poster paper.
13th Australian Wine Industry Technical Conference, Adelaide, 28 July – 2 August.
[6] Du-Ming Tsai, Chien-Ta Lin. Fast normalized cross correlation for defect detection. Journal of
Pattern Recognition Letters 24 (2003) 2625–2631.
[7] A. Roche, G. Malandain, etc. The correlation ratio as a new similarity measure for multimodal
image registration. Medical Image Computing and Computer Assisted Intervention - MICCAI'98,
1496: 1115-1124,1998.
[8] S. Klein. Evaluation of Optimization Methods for Nonrigid Medical Image Registration Using
Mutual Information and B-Splines. IEEE Transactions on Image Processing, 16(12): 2879-2890,
2007.
[9] Jian Chen, Jie Tian. Real-time multi-modal rigid registration based on a novel symmetric-SIFT
descriptor. Progress in Natural Science 19 (2009): 643–651.
[10] Yang Weiping, Wang Xuezhi, etc, Automatic optical and IR image fusion for plant water stress
analysis, Proc of 12 th International Conference on Information Fusion, 1053-1059, 2009.
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