An iris segmentation using harmony search algorithm and fast circle fitting with blob detection

Pupil and iris segmentation based on ellipsis or circle recognition are sensitive to light reflections and reflected images. The method presented here is independent of size and shape and at the same time insensitive to light reflections and reflected mirror images. The pupil detected using the algo...

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Autores:: Malinowski, Kamil
Saeed, Khalid

Tipo de recurso:: Article of journal

Fecha de publicación:: 2022

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: eng

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dc.title.eng.fl_str_mv	An iris segmentation using harmony search algorithm and fast circle fitting with blob detection
title	An iris segmentation using harmony search algorithm and fast circle fitting with blob detection
spellingShingle	An iris segmentation using harmony search algorithm and fast circle fitting with blob detection Blob detection Eye noise Eye noise Eye pupil Imperfection Iris segmentation
title_short	An iris segmentation using harmony search algorithm and fast circle fitting with blob detection
title_full	An iris segmentation using harmony search algorithm and fast circle fitting with blob detection
title_fullStr	An iris segmentation using harmony search algorithm and fast circle fitting with blob detection
title_full_unstemmed	An iris segmentation using harmony search algorithm and fast circle fitting with blob detection
title_sort	An iris segmentation using harmony search algorithm and fast circle fitting with blob detection
dc.creator.fl_str_mv	Malinowski, Kamil Saeed, Khalid
dc.contributor.author.spa.fl_str_mv	Malinowski, Kamil Saeed, Khalid
dc.subject.proposal.eng.fl_str_mv	Blob detection Eye noise Eye noise Eye pupil Imperfection Iris segmentation
topic	Blob detection Eye noise Eye noise Eye pupil Imperfection Iris segmentation
description	Pupil and iris segmentation based on ellipsis or circle recognition are sensitive to light reflections and reflected images. The method presented here is independent of size and shape and at the same time insensitive to light reflections and reflected mirror images. The pupil detected using the algorithm can be a reference point to further segmentation of the sclera of the eye as well as of the iris. The method is also effective when the pupil and iris are not positioned perpendicularly to the camera eye. The algorithm’s average segmentation accuracy for all tested databases was 96% when considering only noisy and distorted images whilst a result of 100% was achieved with unblurred and clear images. The proposed method can be quickly and simply reproduced with a combination of known image processing methods. The developed algorithm for detecting the eyelid boundaries is effective with noisy and poor quality images due to the use of edge approximation using the Harmony Search Algorithm. An optimized shape detection method was used to detect the pupil and its edges. A method based on the variation and the average was used to eliminate shadows and eyelashes. The proposed scheme was tested on the UBIRIS.v1 database, MMU.v1 database and MILES databases, providing high results and short segmentation time. Segmentation accuracy for UBIRIS.v1 was 98.14%, for MMU.v1 – 90% and for MILES – 99.8%.
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-07-07T13:29:46Z
dc.date.available.none.fl_str_mv	2022-07-07T13:29:46Z 2024-03-04
dc.date.issued.none.fl_str_mv	2022-03-04
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.citation.spa.fl_str_mv	Kamil Malinowski, Khalid Saeed, An iris segmentation using harmony search algorithm and fast circle fitting with blob detection, Biocybernetics and Biomedical Engineering, Volume 42, Issue 1, 2022, Pages 391-403, ISSN 0208-5216, https://doi.org/10.1016/j.bbe.2022.02.010.
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dc.identifier.doi.spa.fl_str_mv	10.1016/j.bbe.2022.02.010
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identifier_str_mv	Kamil Malinowski, Khalid Saeed, An iris segmentation using harmony search algorithm and fast circle fitting with blob detection, Biocybernetics and Biomedical Engineering, Volume 42, Issue 1, 2022, Pages 391-403, ISSN 0208-5216, https://doi.org/10.1016/j.bbe.2022.02.010. 10.1016/j.bbe.2022.02.010 0208-5216 Corporación Universidad de la Costa REDICUC - Repositorio CUC
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dc.language.iso.none.fl_str_mv	eng
language	eng
dc.relation.ispartofjournal.spa.fl_str_mv	Biocybernetics and Biomedical Engineering
dc.relation.references.spa.fl_str_mv	Raina, U.K., Gupta, S.K., Gupta, A., Goray, A., Saini, V. Effect of cycloplegia on optical biometry in pediatric eyes (2018) Journal of Pediatric Ophthalmology and Strabismus, 55 (4), pp. 260-265. Cited 5 times. https://www.healio.com/ophthalmology/journals/jpos/2018-7-55-4/%7B16e9d54c-61bf-4051-9e93-e000c5ea05d7%7D/effect-of-cycloplegia-on-optical-biometry-in-pediatric-eyes.pdf doi: 10.3928/01913913-20180327-05 Alizadeh, Y., Akbari, M., Moghadam, R.S., Medghalchi, A., Dourandeesh, M., Bromandpoor, F. Macular optical coherence tomography before cataract surgery (Open Access) (2021) Journal of Current Ophthalmology, 33 (3), pp. 317-322. http://www.jcurrophthalmol.org/ doi: 10.4103/joco.joco_240_20 Proença, H., Alexandre, L.A. UBIRIS: A noisy iris image database (Open Access) (2005) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3617 LNCS, pp. 970-977. Cited 367 times. https://www.springer.com/series/558 ISBN: 3540288694; 978-354028869-5 doi: 10.1007/11553595_119 (2013) MMU. MMU Iris Database. MILES Iris Dataset, Accessed: 2021-06-01. https://drive.google.com/drive/folders/0B5OBp4zckpLnU3YxMnozSGhGelE Sundaram, R., Ravichandran, K.S., Jayaraman, P., Venkatraman, B. Extraction of blood vessels in fundus images of retina through hybrid segmentation approach (Open Access) (2019) Mathematics, 7 (2), art. no. 169. Cited 18 times. https://www.mdpi.com/2227-7390/7/2/169/pdf doi: 10.3390/math7020169 Sadikoglu, F., Uzelaltinbulat, S. Biometric Retina Identification Based on Neural Network (Open Access) (2016) Procedia Computer Science, 102, pp. 26-33. Cited 31 times. http://www.sciencedirect.com/science/journal/18770509 doi: 10.1016/j.procs.2016.09.365 Meng, X.J., Yin, Y.L., Yang, G.P., Xi, X.M. Retinal identification based on an improved circular gabor filter and scale invariant feature transform (Open Access) (2013) Sensors (Switzerland), 13 (7), pp. 9248-9266. Cited 35 times. http://www.mdpi.com/1424-8220/13/7/9248/pdf doi: 10.3390/s130709248 Borah, T.R., Sarma, K.K., Talukdar, P. Retina and fingerprint based biometric identification system (2013) Int J Comput Appl (IJCA), 74. Cited 8 times. Ortega, M., Penedo, M.G., Rouco, J., Barreira, N., Carreira, M.J. Retinal verification using a feature points-based biometric pattern (Open Access) (2009) Eurasip Journal on Advances in Signal Processing, 2009, art. no. 235746. Cited 61 times. doi: 10.1155/2009/235746 Abdelwahed, H., Hashim, A., Hasan, A. Segmentation approach for a noisy iris images based on hybrid techniques (2020) Eng Technol J, 38 (11), pp. 1684-1691. Cited 4 times. Rapaka, S., Rajesh Kumar, P., Katta, M., Lakshminarayana, K., Bhupesh Kumar, N. A new segmentation method for non-ideal iris images using morphological reconstruction FCM based on improved DSA (Open Access) (2021) SN Applied Sciences, 3 (1), art. no. 53. Cited 5 times. springer.com/snas doi: 10.1007/s42452-020-04110-1 Varkarakis, V., Bazrafkan, S., Corcoran, P. Deep neural network and data augmentation methodology for off-axis iris segmentation in wearable headsets (Open Access) (2020) Neural Networks, 121, pp. 101-121. Cited 22 times. www.elsevier.com/locate/neunet doi: 10.1016/j.neunet.2019.07.020 Jan, F., Min-Allah, N. An effective iris segmentation scheme for noisy images (2020) Biocybernetics and Biomedical Engineering, 40 (3), pp. 1064-1080. Cited 6 times. http://www.sciencedirect.com/science/journal/02085216 doi: 10.1016/j.bbe.2020.06.002 Hao, K., Feng, G., Ren, Y., Zhang, X. Iris Segmentation Using Feature Channel Optimization for Noisy Environments (2020) Cognitive Computation, 12 (6), pp. 1205-1216. Cited 3 times. http://www.springer.com/biomed/neuroscience/journal/12559 doi: 10.1007/s12559-020-09759-9 Sahmoud, S., Fathee, H.N. Fast Iris Segmentation Algorithm for Visible Wavelength Images Based on Multi-color Space (2020) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12002 LNCS, pp. 239-250. Cited 2 times. https://www.springer.com/series/558 ISBN: 978-303040604-2 doi: 10.1007/978-3-030-40605-9_21 Kheirolahy, R., Ebrahimnezhad, H., Sedaaghi, M. Robust pupil boundary detection by optimized color mapping for iris recognition (2009) 2009 14th International CSI Computer Conference, CSICC 2009, art. no. 5349260, pp. 170-175. Cited 4 times. ISBN: 978-142444262-1 doi: 10.1109/CSICC.2009.5349260 Lee, S., Lee, D., Park, Y. Pupil segmentation using orientation fields, radial non-maximal suppression and elliptic approximation (Open Access) (2019) Advances in Electrical and Computer Engineering, 19 (2), pp. 69-74. Cited 3 times. http://www.aece.ro/displaypdf.php?year=2019&number=2&article=9 doi: 10.4316/aece.2019.02009 Omran, M., Alshemmary, E.N. An Iris Recognition System Using Deep convolutional Neural Network (Open Access) (2020) Journal of Physics: Conference Series, 1530 (1), art. no. 012159. Cited 9 times. http://iopscience.iop.org/journal/1742-6596 doi: 10.1088/1742-6596/1530/1/012159 Şimşek, İ.B., Şirolu, C. Analysis of surgical outcome after upper eyelid surgery by computer vision algorithm using face and facial landmark detection (2021) Graefe's Arch Clin Exp Ophthalmol, pp. 1-7. Cited 2 times. Jalilian, E., Karakaya, M., Uhl, A. CNN-based off-angle iris segmentation and recognition (2021) IET Biom, 10 (5), pp. 518-535. Cited 2 times. Jan, F., Min-Allah, N., Agha, S., Usman, I., Khan, I. A robust iris localization scheme for the iris recognition (2021) Multimedia Tools Appl, 80 (3), pp. 4579-4605. Cited 4 times. Sardar, M., Banerjee, S., Mitra, S. Iris Segmentation Using Interactive Deep Learning (Open Access) (2020) IEEE Access, 8, art. no. 9274419, pp. 219322-219330. Cited 3 times. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 doi: 10.1109/ACCESS.2020.3041519 Jan, F., Alrashed, S., Min-Allah, N. Iris segmentation for non-ideal Iris biometric systems (2021) Multimedia Tools and Applications. Cited 5 times. https://link.springer.com/journal/11042 doi: 10.1007/s11042-021-11075-9 Li, Y.-H., Huang, P.-J., Juan, Y. An Efficient and Robust Iris Segmentation Algorithm Using Deep Learning (Open Access) (2019) Mobile Information Systems, 2019, art. no. 4568929. Cited 22 times. http://www.hindawi.com/journals/misy/contents/ doi: 10.1155/2019/4568929 Horn, B., Klaus, B., Horn, P. Robot vision (1986) . Cited 2995 times. MIT press Faugeras, O., Faugeras, O.A. Three-dimensional computer vision: a geometric viewpoint (1993) . Cited 3218 times. MIT press (1950) Mathematische Zeitschrift, 53 (3), pp. 210-218. Cited 57 times. doi: 10.1007/BF01175656 Suzuki, S., be, K. Topological structural analysis of digitized binary images by border following (1985) Computer Vision, Graphics and Image Processing, 30 (1), pp. 32-46. Cited 1593 times. doi: 10.1016/0734-189X(85)90016-7 Sklansky, J. Finding the convex hull of a simple polygon (1982) Pattern Recognition Letters, 1 (2), pp. 79-83. Cited 158 times. doi: 10.1016/0167-8655(82)90016-2 Otsu, Nobuyuki THRESHOLD SELECTION METHOD FROM GRAY-LEVEL HISTOGRAMS. (1979) IEEE Trans Syst Man Cybern, SMC-9 (1), pp. 62-66. Cited 27222 times. doi: 10.1109/tsmc.1979.4310076 Davies, E.R. Computer and machine vision: theory, algorithms, practicalities (2012) . Cited 800 times. Academic Press Atherton, T.J., Kerbyson, D.J. Size invariant circle detection (1999) Image and Vision Computing, 17 (11), pp. 795-803. Cited 388 times. https://www.journals.elsevier.com/image-and-vision-computing doi: 10.1016/s0262-8856(98)00160-7 Zhang, C., Huber, F., Knop, M., Hamprecht, F.A. Yeast cell detection and segmentation in bright field microscopy (2014) 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, art. no. 6868107, pp. 1267-1270. Cited 10 times. ISBN: 978-146731959-1 doi: 10.1109/isbi.2014.6868107 Al-Sharadqah, A., Chernov, N. Error analysis for circle fitting algorithms (Open Access) (2009) Electronic Journal of Statistics, 3, pp. 886-911. Cited 141 times. doi: 10.1214/09-EJS419 Tahir, A.A.K., Anghelus, S. An accurate and fast method for eyelid detection (2020) International Journal of Biometrics, 12 (2), pp. 163-178. http://www.inderscience.com/ijbm doi: 10.1504/IJBM.2020.107715 Ak, T.A., Steluta, A. An iris recognition system using a new method of iris localization (2021) Int J Open Inf Technol, 9 (7), pp. 67-76. Geem, Z.W., Kim, J.H., Loganathan, G.V. A New Heuristic Optimization Algorithm: Harmony Search (2001) Simulation, 76 (2), pp. 60-68. Cited 4470 times. doi: 10.1177/003754970107600201 Nadia, B., Abdessalam, B., Mohamed, B. Eyelids, eyelashes detection algorithm and houghtransform method for noise removal in iris recognition (Open Access) (2020) Indonesian Journal of Electrical Engineering and Computer Science, 18 (2), pp. 731-735. Cited 2 times. http://ijeecs.iaescore.com/index.php/IJEECS/article/view/20954/13720 doi: 10.11591/ijeecs.v18.i2.pp731-735 Liu, C.-C., Chung, P.-C., Lyu, C.-M., Liu, J., Yu, S.-S. A novel iris segmentation scheme (Open Access) (2014) Mathematical Problems in Engineering, 2014, art. no. 684212. Cited 6 times. http://www.hindawi.com/journals/mpe/contents.html doi: 10.1155/2014/684212 Horng, M.-H. Performance evaluation of multiple classification of the ultrasonic supraspinatus images by using ML, RBFNN and SVM classifiers (2010) Expert Systems with Applications, 37 (6), pp. 4146-4155. Cited 27 times. doi: 10.1016/j.eswa.2009.11.008
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spelling	Malinowski, KamilSaeed, Khalid2022-07-07T13:29:46Z2024-03-042022-07-07T13:29:46Z2022-03-04Kamil Malinowski, Khalid Saeed, An iris segmentation using harmony search algorithm and fast circle fitting with blob detection, Biocybernetics and Biomedical Engineering, Volume 42, Issue 1, 2022, Pages 391-403, ISSN 0208-5216, https://doi.org/10.1016/j.bbe.2022.02.010.https://hdl.handle.net/11323/934110.1016/j.bbe.2022.02.0100208-5216Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Pupil and iris segmentation based on ellipsis or circle recognition are sensitive to light reflections and reflected images. The method presented here is independent of size and shape and at the same time insensitive to light reflections and reflected mirror images. The pupil detected using the algorithm can be a reference point to further segmentation of the sclera of the eye as well as of the iris. The method is also effective when the pupil and iris are not positioned perpendicularly to the camera eye. The algorithm’s average segmentation accuracy for all tested databases was 96% when considering only noisy and distorted images whilst a result of 100% was achieved with unblurred and clear images. The proposed method can be quickly and simply reproduced with a combination of known image processing methods. The developed algorithm for detecting the eyelid boundaries is effective with noisy and poor quality images due to the use of edge approximation using the Harmony Search Algorithm. An optimized shape detection method was used to detect the pupil and its edges. A method based on the variation and the average was used to eliminate shadows and eyelashes. The proposed scheme was tested on the UBIRIS.v1 database, MMU.v1 database and MILES databases, providing high results and short segmentation time. Segmentation accuracy for UBIRIS.v1 was 98.14%, for MMU.v1 – 90% and for MILES – 99.8%.13 páginasapplication/pdfengElsevier Sp. z o.o.Poland© Copyright 2022 Elsevier B.V., All rights reserved.Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfAn iris segmentation using harmony search algorithm and fast circle fitting with blob detectionArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85https://www.sciencedirect.com/science/article/pii/S0208521622000134#!Biocybernetics and Biomedical EngineeringRaina, U.K., Gupta, S.K., Gupta, A., Goray, A., Saini, V. Effect of cycloplegia on optical biometry in pediatric eyes (2018) Journal of Pediatric Ophthalmology and Strabismus, 55 (4), pp. 260-265. Cited 5 times. https://www.healio.com/ophthalmology/journals/jpos/2018-7-55-4/%7B16e9d54c-61bf-4051-9e93-e000c5ea05d7%7D/effect-of-cycloplegia-on-optical-biometry-in-pediatric-eyes.pdf doi: 10.3928/01913913-20180327-05Alizadeh, Y., Akbari, M., Moghadam, R.S., Medghalchi, A., Dourandeesh, M., Bromandpoor, F. Macular optical coherence tomography before cataract surgery (Open Access) (2021) Journal of Current Ophthalmology, 33 (3), pp. 317-322. http://www.jcurrophthalmol.org/ doi: 10.4103/joco.joco_240_20Proença, H., Alexandre, L.A. UBIRIS: A noisy iris image database (Open Access) (2005) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3617 LNCS, pp. 970-977. Cited 367 times. https://www.springer.com/series/558 ISBN: 3540288694; 978-354028869-5 doi: 10.1007/11553595_119(2013) MMU. MMU Iris Database.MILES Iris Dataset, Accessed: 2021-06-01. https://drive.google.com/drive/folders/0B5OBp4zckpLnU3YxMnozSGhGelESundaram, R., Ravichandran, K.S., Jayaraman, P., Venkatraman, B. Extraction of blood vessels in fundus images of retina through hybrid segmentation approach (Open Access) (2019) Mathematics, 7 (2), art. no. 169. Cited 18 times. https://www.mdpi.com/2227-7390/7/2/169/pdf doi: 10.3390/math7020169Sadikoglu, F., Uzelaltinbulat, S. Biometric Retina Identification Based on Neural Network (Open Access) (2016) Procedia Computer Science, 102, pp. 26-33. Cited 31 times. http://www.sciencedirect.com/science/journal/18770509 doi: 10.1016/j.procs.2016.09.365Meng, X.J., Yin, Y.L., Yang, G.P., Xi, X.M. Retinal identification based on an improved circular gabor filter and scale invariant feature transform (Open Access) (2013) Sensors (Switzerland), 13 (7), pp. 9248-9266. Cited 35 times. http://www.mdpi.com/1424-8220/13/7/9248/pdf doi: 10.3390/s130709248Borah, T.R., Sarma, K.K., Talukdar, P. Retina and fingerprint based biometric identification system (2013) Int J Comput Appl (IJCA), 74. Cited 8 times.Ortega, M., Penedo, M.G., Rouco, J., Barreira, N., Carreira, M.J. Retinal verification using a feature points-based biometric pattern (Open Access) (2009) Eurasip Journal on Advances in Signal Processing, 2009, art. no. 235746. Cited 61 times. doi: 10.1155/2009/235746Abdelwahed, H., Hashim, A., Hasan, A. Segmentation approach for a noisy iris images based on hybrid techniques (2020) Eng Technol J, 38 (11), pp. 1684-1691. Cited 4 times.Rapaka, S., Rajesh Kumar, P., Katta, M., Lakshminarayana, K., Bhupesh Kumar, N. A new segmentation method for non-ideal iris images using morphological reconstruction FCM based on improved DSA (Open Access) (2021) SN Applied Sciences, 3 (1), art. no. 53. Cited 5 times. springer.com/snas doi: 10.1007/s42452-020-04110-1Varkarakis, V., Bazrafkan, S., Corcoran, P. Deep neural network and data augmentation methodology for off-axis iris segmentation in wearable headsets (Open Access) (2020) Neural Networks, 121, pp. 101-121. Cited 22 times. www.elsevier.com/locate/neunet doi: 10.1016/j.neunet.2019.07.020Jan, F., Min-Allah, N. An effective iris segmentation scheme for noisy images (2020) Biocybernetics and Biomedical Engineering, 40 (3), pp. 1064-1080. Cited 6 times. http://www.sciencedirect.com/science/journal/02085216 doi: 10.1016/j.bbe.2020.06.002Hao, K., Feng, G., Ren, Y., Zhang, X. Iris Segmentation Using Feature Channel Optimization for Noisy Environments (2020) Cognitive Computation, 12 (6), pp. 1205-1216. Cited 3 times. http://www.springer.com/biomed/neuroscience/journal/12559 doi: 10.1007/s12559-020-09759-9Sahmoud, S., Fathee, H.N. Fast Iris Segmentation Algorithm for Visible Wavelength Images Based on Multi-color Space (2020) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12002 LNCS, pp. 239-250. Cited 2 times. https://www.springer.com/series/558 ISBN: 978-303040604-2 doi: 10.1007/978-3-030-40605-9_21Kheirolahy, R., Ebrahimnezhad, H., Sedaaghi, M. Robust pupil boundary detection by optimized color mapping for iris recognition (2009) 2009 14th International CSI Computer Conference, CSICC 2009, art. no. 5349260, pp. 170-175. Cited 4 times. ISBN: 978-142444262-1 doi: 10.1109/CSICC.2009.5349260Lee, S., Lee, D., Park, Y. 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Cited 27 times. doi: 10.1016/j.eswa.2009.11.008403391142Blob detectionEye noiseEye noiseEye pupilImperfectionIris segmentationPublicationORIGINALAn iris segmentation using harmony search algorithm and fast circle fitting with blob detection.pdfAn iris segmentation using harmony search algorithm and fast circle fitting with blob detection.pdfapplication/pdf3148258https://repositorio.cuc.edu.co/bitstreams/f08b2eab-3103-430c-b0a7-fd4c7cb5abb3/download13ef9880344b3afc75ccd6749cf5e3e5MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/d8907dfd-18fd-4021-bc31-dfb7a3420e64/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTAn iris segmentation using harmony search algorithm and fast circle fitting with blob detection.pdf.txtAn iris segmentation using harmony search algorithm and fast circle fitting with blob 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An iris segmentation using harmony search algorithm and fast circle fitting with blob detection

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