An Improved Bovine Iris Segmentation Method

Zhongliang Luo; Jingguo Dai; Yingbiao Jia; Jiazhong He

doi:10.1051/matecconf/201926703002

All issues

Volume 267 (2019)

MATEC Web Conf., 267 (2019) 03002

References

Open Access

Issue		MATEC Web Conf. Volume 267, 2019 2018 2^nd AASRI International Conference on Intelligent Systems and Control (ISC 2018)


Article Number		03002
Number of page(s)		5
Section		Biological and Chemical Engineering
DOI		https://doi.org/10.1051/matecconf/201926703002
Published online		11 February 2019

L. D. Zhao, S.N. Sun, Q. Kong, Food traceability system based on iris recognition middleware. Icic Express Letters, 2009, 3(4):1251-1256. [Google Scholar]
J. Velez, A. Sanchez, J. Sanchez, Beef identification in industrial Slaughter houses using machine vision techniques. Spanish Journal of Agricultural Research, 2013, 11(4): 945-957 [CrossRef] [Google Scholar]
S. Ahmed, T. Gaber, A. Tharwat, Muzzle-Based Cattle Identification Using Speed up Robust Feature Approach[C]// Intelligent Networking and Collaborative Systems, 2015 International Conference on. IEEE, 2015. 99-104 [Google Scholar]
S. Ammendrup, Fussel A. E. Legislative requirements for the identification and traceability of farm animals within the European union[J]. Scientific and Technical Review, 2001, 20(2): 437-443 [Google Scholar]
Z.L. Luo, Y.B. Jia, The bovine iris location method based on dynamic contour tracking and least square principle. International Journal of Signal Processing, Image Processing and Pattern Recognition, 2016, 9(7): 275-284 [CrossRef] [Google Scholar]
U. Gonzales Barren, G. Corkery, B. Barry. Assessment of retinal recognition technology as a biometric method for sheep identification [J]. Computer and Electronics in Agriculture, 2008, 60(2): 156-166 [CrossRef] [Google Scholar]
J.S. Zhang, H. Zhou, Comparisons on animal ID technology [J]. Chinese Journal of Animal Science, 2008, 44(11):55-58. [Google Scholar]
S. Masahiko, Y. Osamu, A horse identification system using biometrics[J]. Systems and Computers in Japan, 2001, 32(14): 12-23 [Google Scholar]
A. Allen, B. Golden, M. Taylor. Evaluation of retinal imaging technology for the biometric identification of bovine animals in Northern Ireland [J]. Livestock Science, 2008, 116(3):42-52 [CrossRef] [Google Scholar]
D.W. Sheng, X.F. He, Y. Lu. A cattle iris segmentation method aased on keast square principle[J]. Journal of Image and Graphics, 2009, 14(10): 2122-2126 [Google Scholar]
Li Chao Zhao Lindu. Research on Cattle Iris Localization Algorithm and Its Application in Meat Food Tracking and Traceability System[J]. China Safety Science Journal, 2011, 21(3):124-129 [Google Scholar]
R.P. Wildes, Iris recognition: An emerging biometric technology [C] // Proceeding of the IEEE, 1997, 85(9): 1348-1363 [CrossRef] [Google Scholar]
J. Daugman, New methods in iris recognition.[J]. IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society, 2007, 37(5):1167-75. [CrossRef] [Google Scholar]
M. Nirali Bhagwagar, A. Yagnik Rathod. A Survey on iris recognition for authentication [J]. International Journal of Technical Research and Applications, 2015, 3(2):148-151. [Google Scholar]
Kass M., Witkin A., Terzopoulos D. Snakes: Active contour models [J]. International Journal of Computer Vision, 1988, 1(4):32l-331. [Google Scholar]
Caselles V., Kimmel R., Sapiro G.. Geodesic active contours [J]. International Journal of Computer Vision, 1997, 22(1):61-79. [Google Scholar]
C.M. Li, C.Y. Xu, C.F. Gui, Level set evolution without reinitialization: A new variational formulation[C]//. Proceeding of the IEEE Computer Society Conference 0n Computer Vision and Pattern Recognition, 2005, 430-436 [Google Scholar]
B. Yan, B. Wang, Y. Li, Optimal ellipse fitting method based on least-square principle [J]. Journal of Beijing University of Aeronautics & Astronautics, 2008, 34(3): 295-298. [Google Scholar]
S. Gayathri, N. Mohanapriya, Survey on contrast enhancement techniques, International Journal of Advanced Research in Computer and Communication Engineering, 2013, 2(11) :4176–4180. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.

[1] L. D. Zhao, S.N. Sun, Q. Kong, Food traceability system based on iris recognition middleware. Icic Express Letters, 2009, 3(4):1251-1256. [Google Scholar]

[2] J. Velez, A. Sanchez, J. Sanchez, Beef identification in industrial Slaughter houses using machine vision techniques. Spanish Journal of Agricultural Research, 2013, 11(4): 945-957 [CrossRef] [Google Scholar]

[3] S. Ahmed, T. Gaber, A. Tharwat, Muzzle-Based Cattle Identification Using Speed up Robust Feature Approach[C]// Intelligent Networking and Collaborative Systems, 2015 International Conference on. IEEE, 2015. 99-104 [Google Scholar]

[4] S. Ammendrup, Fussel A. E. Legislative requirements for the identification and traceability of farm animals within the European union[J]. Scientific and Technical Review, 2001, 20(2): 437-443 [Google Scholar]

[5] Z.L. Luo, Y.B. Jia, The bovine iris location method based on dynamic contour tracking and least square principle. International Journal of Signal Processing, Image Processing and Pattern Recognition, 2016, 9(7): 275-284 [CrossRef] [Google Scholar]

[6] U. Gonzales Barren, G. Corkery, B. Barry. Assessment of retinal recognition technology as a biometric method for sheep identification [J]. Computer and Electronics in Agriculture, 2008, 60(2): 156-166 [CrossRef] [Google Scholar]

[7] J.S. Zhang, H. Zhou, Comparisons on animal ID technology [J]. Chinese Journal of Animal Science, 2008, 44(11):55-58. [Google Scholar]

[8] S. Masahiko, Y. Osamu, A horse identification system using biometrics[J]. Systems and Computers in Japan, 2001, 32(14): 12-23 [Google Scholar]

[9] A. Allen, B. Golden, M. Taylor. Evaluation of retinal imaging technology for the biometric identification of bovine animals in Northern Ireland [J]. Livestock Science, 2008, 116(3):42-52 [CrossRef] [Google Scholar]

[10] D.W. Sheng, X.F. He, Y. Lu. A cattle iris segmentation method aased on keast square principle[J]. Journal of Image and Graphics, 2009, 14(10): 2122-2126 [Google Scholar]

[11] Li Chao Zhao Lindu. Research on Cattle Iris Localization Algorithm and Its Application in Meat Food Tracking and Traceability System[J]. China Safety Science Journal, 2011, 21(3):124-129 [Google Scholar]

[12] R.P. Wildes, Iris recognition: An emerging biometric technology [C] // Proceeding of the IEEE, 1997, 85(9): 1348-1363 [CrossRef] [Google Scholar]

[13] J. Daugman, New methods in iris recognition.[J]. IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society, 2007, 37(5):1167-75. [CrossRef] [Google Scholar]

[14] M. Nirali Bhagwagar, A. Yagnik Rathod. A Survey on iris recognition for authentication [J]. International Journal of Technical Research and Applications, 2015, 3(2):148-151. [Google Scholar]

[15] Kass M., Witkin A., Terzopoulos D. Snakes: Active contour models [J]. International Journal of Computer Vision, 1988, 1(4):32l-331. [Google Scholar]

[16] Caselles V., Kimmel R., Sapiro G.. Geodesic active contours [J]. International Journal of Computer Vision, 1997, 22(1):61-79. [Google Scholar]

[17] C.M. Li, C.Y. Xu, C.F. Gui, Level set evolution without reinitialization: A new variational formulation[C]//. Proceeding of the IEEE Computer Society Conference 0n Computer Vision and Pattern Recognition, 2005, 430-436 [Google Scholar]

[18] B. Yan, B. Wang, Y. Li, Optimal ellipse fitting method based on least-square principle [J]. Journal of Beijing University of Aeronautics & Astronautics, 2008, 34(3): 295-298. [Google Scholar]

[19] S. Gayathri, N. Mohanapriya, Survey on contrast enhancement techniques, International Journal of Advanced Research in Computer and Communication Engineering, 2013, 2(11) :4176–4180. [Google Scholar]