An image processing pipeline to segment iris for unconstrained cow identification system

References

[1] Bridle J., Automatic dairy cow identification, Journal of Agricultural Engineering Research, 21(1), 1976, 41–4810.1016/0021-8634(76)90097-4Search in Google Scholar

[2] Cambier J.L., System and method for animal identification using IRIS images, 2012, uS Patent 8,189,879Search in Google Scholar

[3] Shadduck J., Golden B., Retinal imaging in secure identification and source verification of livestock, Proceedings ID/INFO Expo, 2002Search in Google Scholar

[4] Kumar S., Tiwari S., Singh S.K., Face recognition for cattle, in 2015 Third International Conference on Image Information Processing (ICIIP), IEEE, 2015, 65–7210.1109/ICIIP.2015.7414742Search in Google Scholar

[5] Evans J., Van Eenennaam A., Livestock identification, Emerging management systems in animal identification. Fact Sheet, 5, 2005Search in Google Scholar

[6] Bai H., Zhou G., Hu Y., Sun A., Xu X., Liu X., Lu C., Traceability technologies for farm animals and their products in China, Food Control, 79, 2017, 35–4310.1016/j.foodcont.2017.02.040Search in Google Scholar

[7] Hammer N., Pfeifer M., Staiger M., Adrion F., Gallmann E., Jung-bluth T., Cost-benefit analysis of an UHF-RFID system for animal identification, simul taneous detection and hotspot monitoring of fattening pigs and dairy cows, LANDTECHNIK, 72(3), 2017, 130–155Search in Google Scholar

[8] Marchant J., Secure animal identification and source verification, JM Communications, UK. Copyright Optibrand Ltd., LLC, 2002Search in Google Scholar

[9] Deiner K., Bik H.M., Mächler E., Seymour M., Lacoursière-Roussel A., Altermatt F., Creer S., Bista I., Lodge D.M., de Vere N., et al., Environmental DNA metabarcoding: transforming how we survey animal and plant communities, Molecular ecology, 26(21), 2017, 5872–589510.1111/mec.14350Search in Google Scholar PubMed

[10] Huang J., Wang Y., Tan T., Cui J., A new iris segmentation method for recognition, In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., volume 3, IEEE, 2004, 554–557Search in Google Scholar

[11] Malisiewicz T., Efros A.A., Improving Spatial Support for Objects via Multiple Segmentations., In N.M. Rajpoot, A.H. Bhalerao, eds., BMVC, British Machine Vision Association, 2007, 1–1010.5244/C.21.55Search in Google Scholar

[12] Rabinovich A., Vedaldi A., Belongie S., Does image segmentation improve object categorization, Technical report, 2007Search in Google Scholar

[13] Witten I.H., Frank E., Hall M.A., Pal C.J., Data Mining: Practical machine learning tools and techniques, Morgan Kaufmann, 2016Search in Google Scholar

[14] Daugman J., How iris recognition works, IEEE Transactions on circuits and systems for video technology, 14(1), 2004, 21–3010.1109/TCSVT.2003.818350Search in Google Scholar

[15] Lu Ming, Image segmentation algorithm research and improvement, In 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE), volume 5, 2010, V5–211–V5–214, 10.1109/ICACTE.2010.557911410.1109/ICACTE.2010.5579114Search in Google Scholar

[16] Kumar S., Singh S.K., Visual animal biometrics: survey, IET Bio-metrics, 6(3), 2017, 139–156, 10.1049/iet-bmt.2016.001710.1049/iet-bmt.2016.0017Search in Google Scholar

[17] Duyck J., Finn C., Hutcheon A., Vera P., Salas J., Ravela S., Sloop: A pattern retrieval engine for individual animal identification, Pattern Recognition, 48(4), 2015, 1059 – 1073, https://doi.org/10.1016/j.patcog.2014.07.01710.1016/j.patcog.2014.07.017Search in Google Scholar

[18] Varior R.R., Haloi M., Wang G., Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification, CoRR, abs/1607.08378, 201610.1007/978-3-319-46484-8_48Search in Google Scholar

[19] Raghavendra C., Kumaravel A., Sivasubramanian S., Iris technology: A review on iris based biometric systems for unique human identification, In Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET), 2017 International Conference on, IEEE, 2017, 1–610.1109/ICAMMAET.2017.8186679Search in Google Scholar

[20] He Z., Tan T., Sun Z., Qiu X., Toward accurate and fast iris segmentation for iris biometrics, IEEE transactions on pattern analysis and machine intelligence, 31(9), 2009, 1670–168410.1109/TPAMI.2008.183Search in Google Scholar PubMed

[21] Mei J., Si Y., Gao H., Novel approaches to improve robustness accuracy and rapidity of iris recognition systems, IEEE transactions on industrial informatics, 8(1), 2011, 110–11710.1109/TII.2011.2166791Search in Google Scholar

[22] Hoffiauer H., Alonso-Fernandez F., Bigun J., Uhl A., Experimental analysis regarding the influence of iris segmentation on the recognition rate, IET Biometrics, 5(3), 2016, 200–21110.1049/iet-bmt.2015.0069Search in Google Scholar

[23] Hajari K., Bhoyar K., A review of issues and challenges in designing iris recognition systems for noisy imaging environment, In Pervasive Computing (ICPC), 2015 International Conference on, IEEE, 2015, 1–610.1109/PERVASIVE.2015.7087003Search in Google Scholar

[24] Nigam I., Vatsa M., Singh R., Ocular biometrics: A survey of modalities and fusion approaches, Information Fusion, 26, 2015, 1 – 35, https://doi.org/10.1016/j.inffus.2015.03.00510.1016/j.inffus.2015.03.005Search in Google Scholar

[25] Ahmadi N., Akbarizadeh G., Hybrid robust iris recognition approach using iris image pre-processing, two-dimensional gabor features and multi-layer perceptron neural network/PSO, IET Biometrics, 7(2), 2017, 153–16210.1049/iet-bmt.2017.0041Search in Google Scholar

[26] Arslan A., Şen B., Çelebi F.V., Uysal B.S., Automatic segmentation of region of interest for dry eye disease diagnosis system, in Signal Processing and Communication Application Conference (SIU), 2016 24th, IEEE, 2016, 1817–182010.1109/SIU.2016.7496115Search in Google Scholar

[27] De Marsico M., Petrosino A., Ricciardi S., Iris recognition through machine learning techniques: A survey, Pattern Recognition Letters, 82, 2016, 106–11510.1016/j.patrec.2016.02.001Search in Google Scholar

[28] Ramlee R., Ramli A., Noh Z., Pupil Segmentation of Abnormal Eye using Image Enhancement in Spatial Domain, In Materials Science and Engineering Conference Series, volume 210, 2017, 01203110.1088/1757-899X/210/1/012031Search in Google Scholar

[29] Telgad R., Siddiqui A., Deshmukh P., Survey: Iris feature extraction techniques for persons recognition system, Advances in Computational Research, 7(1), 2015, 176Search in Google Scholar

[30] Sankowski W., Grabowski K., Napieralska M., Zubert M., Napieralski A., Reliable algorithm for iris segmentation in eye image, Image and vision computing, 28(2), 2010, 231–23710.1016/j.imavis.2009.05.014Search in Google Scholar

[31] Jeong D.S., Hwang J.W., Kang B.J., Park K.R., Won C.S., Park D.K., Kim J., A new iris segmentation method for non-ideal iris images, Image and vision computing, 28(2), 2010, 254–26010.1016/j.imavis.2009.04.001Search in Google Scholar

[32] Gou C., Wu Y., Wang K., Wang K., Wang F.Y., Ji Q., A joint cascaded framework for simultaneous eye detection and eye state estimation, Pattern Recognition, 67, 2017, 23–3110.1016/j.patcog.2017.01.023Search in Google Scholar

[33] Hansen D.W., Ji Q., In the eye of the beholder: A survey of models for eyes and gaze, IEEE transactions on pattern analysis and machine intelligence, 32(3), 2010, 478–50010.1109/TPAMI.2009.30Search in Google Scholar PubMed

[34] Zhang X., Sugano Y., Fritz M., Bulling A., MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 1–1, 10.1109/TPAMI.2017.277810310.1109/TPAMI.2017.2778103Search in Google Scholar PubMed

[35] Wang P., Green M.B., Ji Q., Wayman J., Automatic eye detection and its validation, In Computer Vision and Pattern Recognition-Workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on, IEEE, 2005, 164–164Search in Google Scholar

[36] Valenti R., Gevers T., Accurate eye center location and tracking using isophote curvature, In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, IEEE, 2008, 1–810.1109/CVPR.2008.4587529Search in Google Scholar

[37] Leo M., Cazzato D., De Marco T., Distante C., Unsupervised approach for the accurate localization of the pupils in near-frontal facial images, Journal of Electronic Imaging, 22(3), 2013, 033033–03303310.1117/1.JEI.22.3.033033Search in Google Scholar

[38] Leo M., Cazzato D., De Marco T., Distante C., Unsupervised eye pupil localization through differential geometry and local self-similarity matching, PloS one, 9(8), 2014, e10282910.1371/journal.pone.0102829Search in Google Scholar PubMed PubMed Central

[39] Valenti R., Gevers T., Accurate eye center location through invariant isocentric patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(9), 2012, 1785–179810.1109/TPAMI.2011.251Search in Google Scholar PubMed

[40] Fuhl W., Kübler T., Sippel K., Rosenstiel W., Kasneci E., Excuse: Robust pupil detection in real-world scenarios, In International Conference on Computer Analysis of Images and Patterns, Springer, 2015, 39–5110.1007/978-3-319-23192-1_4Search in Google Scholar

[41] Borza D., Darabant A.S., Danescu R., Real-Time Detection and Measurement of Eye Features from Color Images, Sensors, 16(7), 2016, 110510.3390/s16071105Search in Google Scholar PubMed PubMed Central

[42] Fuhl W., Santini T., Kasneci G., Kasneci E., PupilNet: convolutional neural networks for robust pupil detection, arXiv preprint arXiv:1601.04902, 2016Search in Google Scholar

[43] Allen A., Golden B., Taylor M., Patterson D., Henriksen D., Skuce R., Evaluation of retinal imaging technology for the biometric identification of bovine animals in Northern Ireland, Livestock Science, 116(1), 2008, 42–5210.1016/j.livsci.2007.08.018Search in Google Scholar

[44] Kumar S., Singh S.K., Singh R., Singh A.K., Animal Biometrics: Techniques and Applications, Springer, 201810.1007/978-981-10-7956-6Search in Google Scholar

[45] Li W., Ji Z., Wang L., Sun C., Yang X., Automatic individual identification of Holstein dairy cows using tailhead images, Computers and Electronics in Agriculture, 142, 2017, 622–63110.1016/j.compag.2017.10.029Search in Google Scholar

[46] Musgrave C., Cambier J.L., System and method of animal identification and animal transaction authorization using iris patterns, 2002, uS Patent 6,424,727Search in Google Scholar

[47] Noviyanto A., Arymurthy A.M., Automatic cattle identification based on muzzle photo using speed-up robust features approach, In Proceedings of the 3rd European conference of computer science, ECCS, volume 110, 2012, 114Search in Google Scholar

[48] Lowe D.G., Object recognition from local scale-invariant features, in Computer vision, 1999. The proceedings of the seventh IEEE international conference on, volume 2, Ieee, 1999, 1150–115710.1109/ICCV.1999.790410Search in Google Scholar

[49] Chen J., Shan S., He C., Zhao G., Pietikainen M., Chen X., Gao W., WLD: A robust local image descriptor, IEEE transactions on pattern analysis and machine intelligence, 32(9), 2010, 1705–172010.1109/TPAMI.2009.155Search in Google Scholar PubMed

[50] He D.C., Wang L., Texture unit, texture spectrum, and texture analysis, IEEE transactions on Geoscience and Remote Sensing, 28(4), 1990, 509–51210.1109/TGRS.1990.572934Search in Google Scholar

[51] Gaber T., Tharwat A., Hassanien A.E., Snasel V., Biometric cattle identification approach based on weber’s local descriptor and adaboost classifier, Computers and Electronics in Agriculture, 122, 2016, 55–6610.1016/j.compag.2015.12.022Search in Google Scholar

[52] Zhang M., Zhao L., An iris localization algorithm based on geometrical features of cow eyes, in MIPPR 2009: Automatic Target Recognition and Image Analysis, volume 7495, International Society for Optics and Photonics, 2009, 74951710.1117/12.832494Search in Google Scholar

[53] Sun S., Zhao L., Bovine iris segmentation using region-based active contour model, International Journal of Innovative Computing, Information and Control, 8(9), 2012, 6461–6471Search in Google Scholar

[54] Zhao L., Shengnan S., Wang X., Tracking and traceability system using livestock Iris code in meat supply chain, International Journal of Innovative Computing, Information and Control, 7(5), 2011, 2201–2212Search in Google Scholar

[55] Lu Y., He X., Wen Y., Wang P.S., A new cow identification system based on iris analysis and recognition, International journal of biometrics, 6(1), 2014, 18–3210.1504/IJBM.2014.059639Search in Google Scholar

[56] He X., Yan J., Chen G., Shi P., Contactless autofeedback iris capture design, IEEE Transactions on Instrumentation and Measurement, 57(7), 2008, 1369–137510.1109/TIM.2007.915437Search in Google Scholar

[57] Larregui J.I., Espinosa J., Ganuza M.L., Castro S.M., Biometric Iris Identification in Bovines, in Computer Science & Technology Series, XX Argentine Congress of Computer Science selected papers, 2015, 111–121Search in Google Scholar

[58] Bora D.J., Gupta A.K., Khan F.A., Comparing the performance of L* A* B* and HSV color spaces with respect to color image segmentation, arXiv preprint arXiv:1506.01472, 2015Search in Google Scholar

[59] Sural S., Qian G., Pramanik S., Segmentation and histogram generation using the HSV color space for image retrieval, In Image Processing. 2002. Proceedings. 2002 International Conference on, volume 2, IEEE, 2002, II–IISearch in Google Scholar

[60] Soille P., Advances in the analysis of topographic features on discrete images, In Discrete Geometry for Computer Imagery, Springer, 2002, 271–29610.1007/3-540-45986-3_16Search in Google Scholar

[61] Vincent L., Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms, IEEE transactions on image processing, 2(2), 1993, 176–20110.1109/83.217222Search in Google Scholar

[62] Meyer F., Topographic distance and watershed lines, Signal processing, 38(1), 1994, 113–12510.1016/0165-1684(94)90060-4Search in Google Scholar

[63] El Kaddouhi S., Saaidi A., Abarkan M., Eye detection based on the Viola-Jones method and corners points, Multimedia Tools and Applications, 76(21), 2017, 23077–2309710.1007/s11042-017-4415-5Search in Google Scholar

[64] Rosin P.L., Unimodal thresholding, Pattern recognition, 34(11), 2001, 2083–209610.1016/S0031-3203(00)00136-9Search in Google Scholar

[65] Angus es la raza predominante de la ganaderia argentina, http://www.infocampo.com.ar/angus-es-la-razapredominante-de-la-ganaderia-argentina/, [Online; Accessed: 2018-09-16]Search in Google Scholar

[66] FAQs - American Angus Association, http://www.angus.org/Pub/FAQs.aspx, [Online; Accessed: 2019-03-03]Search in Google Scholar

[67] Kilkenny C., Browne W., Cuthill I.C., Emerson M., Altman D.G., Animal research: reporting in vivo experiments: the ARRIVE guidelines, British journal of pharmacology, 160(7), 2010, 1577–157910.1111/j.1476-5381.2010.00872.xSearch in Google Scholar PubMed PubMed Central

[68] Directive E., DIRECTIVE 2010/63, EU of the European Parliament and of the Council of, 22, 2010Search in Google Scholar

[69] Taha A.A., Hanbury A., Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool, BMC medical imaging, 15(1), 2015, 2910.1186/s12880-015-0068-xSearch in Google Scholar PubMed PubMed Central

[70] Daugman J.G., High confidence visual recognition of persons by a test of statistical independence, IEEE transactions on pattern analysis and machine intelligence, 15(11), 1993, 1148–116110.1109/34.244676Search in Google Scholar

[71] Leo M., De Marco T., Distante C., Highly usable and accurate iris segmentation, in 2014 22nd International Conference on Pattern Recognition, IEEE, 2014, 2489–249410.1109/ICPR.2014.430Search in Google Scholar

[72] De Marco T., Cazzato D., Leo M., Distante C., Randomized circle detection with isophotes curvature analysis, Pattern Recognition, 48(2), 2015, 411–42110.1016/j.patcog.2014.08.007Search in Google Scholar