Abstract
In order to promote the efficiency and accuracy of image registration, this paper proposes an improved registration algorithm for fruit tree images acquired by a dual-sensor vision system. In the algorithm, feature points are extracted by FAST detectors from Gaussian scale space, and main orientation is determined and SURF descriptor is created with statistical method of neighbor intensity distribution, matching pairs are determined with a relative ratio method and a iterative purifying method in succession. Experimental results show that the proposed algorithm outperforms previous algorithms comprehensively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mai, C., Zheng, L., Li, M.: Rapid 3D reconstruction of fruit tree based on point cloud registration. Trans. Chin. Soc. Agric. Eng. 31(supp. 2), 137–144 (2015)
Fan, Y., Huang, X.: Research progress in reconstruction of three-dimensional structure of trees. Forestry Mach. Woodworking Equipment 41(2), 26–28 (2013)
Liu, G., Si, Y., Feng, J.: 3D reconstruction of agriculture and forestry crops. Trans. Chin. Soc. Agric. Mach. 45(6), 38–46 (2014)
Schulze, M.: An approach for the calibration of a combined RGB-sensor and 3D-camera device. In: Paper presented at the Proceedings of SPIE 8085, Videometricsm, Range Imaging and Applications, vol. XI (2011)
Ma, X., Gang, L., Feng, J., Wei, Z.: Multi-source image registration for Canopy Organ of apple trees in mature period. Trans. Chin. Soc. Agric. Mach. 45(4), 82–88 (2014)
Rosten, L., Drummond, T.: A machine learning approach to corner detection. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 105–119 (2010)
Wang, M., Dai, Y.P.: Local robust feature based on FAST corner detection. Beijing Ligong Daxue Xuebao/Trans. Beijing Inst. Technol. 33(10), 1045–1050 (2013)
Guo, L., Li, J., Zhu, Y.: Fast image matching algorithm based on multi-scale FAST-9. Comput. Eng. 38(12), 208–210 (2012)
Viswanathan, D.: Features from Accelerated Segment Test (2016). https://pdfs.semanticscholar.org/cd26/7a4b04d835dbecf01d47fc69ed3a38c23055.pdf
Bay, H., et al.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)
Nie, T., Hao, X., Fu, T., Zhao, W.: Electronic image stabilization based on improved fast feature matching. Electron. Measur. Technol. 38(11), 42–45 (2015)
Feng, J., Liu, G., Wang, S., Ma, X., Zhou, W.: Multi-source Images Registration for harvesting robot to recognize fruits. Trans. Chin. Soc. Agric. Mach. 44(3), 197–203 (2013)
Chen, J., Han, X.: Image matching algorithm combining FAST-SURF and improved k-d tree nearest neighbor search. J. Xian Univ. Technol. 32(2), 213–217, 252 (2016)
Li, H., Wang, K., Liu, S.: Registration method between infrared and visible images of electrical equipment based on gray-scale redundancy and SURF. Power Syst. Prot. Control. 39(11), 111–115, 123 (2011)
Acknowledgements
The authors thank the financial support from the Natural Science Foundation of Hebei Province (No. C2015204043) and the Science and Engineering Foundation of Agricultural University of Hebei Province (No. LG20140602).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 IFIP International Federation for Information Processing
About this paper
Cite this paper
Feng, J., Zeng, L., Li, J. (2019). Fruit Tree Image Registration Based on Improved FAST Algorithm. In: Li, D. (eds) Computer and Computing Technologies in Agriculture X. CCTA 2016. IFIP Advances in Information and Communication Technology, vol 509. Springer, Cham. https://doi.org/10.1007/978-3-030-06155-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-06155-5_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-06154-8
Online ISBN: 978-3-030-06155-5
eBook Packages: Computer ScienceComputer Science (R0)
