{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T11:42:52Z","timestamp":1770032572899,"version":"3.49.0"},"reference-count":24,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T00:00:00Z","timestamp":1616630400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2021,11,17]]},"abstract":"<jats:p>We present an automated approach to segment key structures of the eye, viz., the iris, pupil and sclera in images obtained using an Augmented Reality (AR)\/ Virtual Reality (VR) application. This is done using a two-step classifier: In the first step, we use an auto encoder-decoder network to obtain a pixel-wise classification of regions that comprise the iris, sclera and the background (image pixels that are outside the region of the eye). In the second step, we perform a pixel-wise classification of the iris region to delineate the pupil. The images in the study are from the OpenEDS challenge and were used to evaluate both the accuracy and computational cost of the proposed segmentation method. Our approach achieved a score of 0.93 on the leaderboard, outperforming the baseline model by achieving a higher accuracy and using a smaller number of parameters. These results demonstrate the great promise pipelined models hold along with the benefit of using domain-specific processing and feature engineering in conjunction with deep-learning based approaches for segmentation tasks.<\/jats:p>","DOI":"10.3233\/jifs-189858","type":"journal-article","created":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T14:36:57Z","timestamp":1617115017000},"page":"5359-5365","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Automated segmentation of key structures of the eye using a light-weight two-step classifier"],"prefix":"10.1177","volume":"41","author":[{"given":"Adish","family":"Rao","sequence":"first","affiliation":[{"name":"PES Center for Pattern Recognition, PES University Electronic City Campus, Bengaluru, India"}]},{"given":"Aniruddha","family":"Mysore","sequence":"additional","affiliation":[{"name":"PES Center for Pattern Recognition, PES University Electronic City Campus, Bengaluru, India"}]},{"given":"Siddhanth","family":"Ajri","sequence":"additional","affiliation":[{"name":"PES Center for Pattern Recognition, PES University Electronic City Campus, Bengaluru, India"}]},{"given":"Abhishek","family":"Guragol","sequence":"additional","affiliation":[{"name":"PES Center for Pattern Recognition, PES University Electronic City Campus, Bengaluru, India"}]},{"given":"Poulami","family":"Sarkar","sequence":"additional","affiliation":[{"name":"PES Center for Pattern Recognition, PES University Electronic City Campus, Bengaluru, India"}]},{"given":"Gowri","family":"Srinivasa","sequence":"additional","affiliation":[{"name":"PES Center for Pattern Recognition and the Department of Computer Science and Engineering, PES University, Bengaluru, India"}]}],"member":"179","published-online":{"date-parts":[[2021,3,25]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2017.04.006"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1080\/17470218.2015.1098709"},{"key":"e_1_3_1_4_2","unstructured":"MoraK.A.F. MonayF. and OdobezJ.-M. EYEDIAP in Proceedings of the Symposium on Eye Tracking Research and Applications \u2013 ETRA \u201914. ACM Press 2014."},{"key":"e_1_3_1_5_2","doi-asserted-by":"crossref","unstructured":"DasA. PalU. BlumensteinM. and BallesterM.A.F. Sclera recognition \u2013 a survey in 2013 2nd IAPR Asian Conference on Pattern Recognition IEEE nov 2013.","DOI":"10.1109\/ACPR.2013.168"},{"key":"e_1_3_1_6_2","doi-asserted-by":"crossref","unstructured":"DasA. PalU. FerrerM.A. BlumensteinM. StepecD. RotP. EmersicZ. PeerP. StrucV. KumarS.V.A. and HarishB.S. SSERBC 2017: Sclera segmentation and eye recognition benchmarking competition in 2017 IEEE International Joint Conference on Biometrics (.CB). IEEE oct 2017.","DOI":"10.1109\/BTAS.2017.8272764"},{"key":"e_1_3_1_7_2","doi-asserted-by":"crossref","unstructured":"DasA. PalU. FerrerM.A. BlumensteinM. StepecD. RotP. EmersicZ. PeerP. and StrucV. SSBC 2018: Sclera segmentation benchmarking competition in 2018 International Conference on Biometrics (ICB). IEEE feb 2018.","DOI":"10.1109\/ICB2018.2018.00053"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.26438\/ijcse\/v6i11.739748"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2009.05.014"},{"key":"e_1_3_1_10_2","doi-asserted-by":"crossref","unstructured":"TonsenM. ZhangX. SuganoY. and BullingA. Labelled pupils in the wild in Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications \u2013 ETRA \u201916. ACM Press 2016.","DOI":"10.1145\/2857491.2857520"},{"key":"e_1_3_1_11_2","unstructured":"GarbinS.J. ShenY. SchuetzI. CavinR. HughesG. and TalathiS.S. Openeds: Open eye dataset 2019."},{"key":"e_1_3_1_12_2","doi-asserted-by":"crossref","unstructured":"HeK. ZhangX. RenS. and SunJ. Deep residual learning for image recognition in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE jun 2016.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_1_13_2","doi-asserted-by":"crossref","unstructured":"LongJ. ShelhamerE. and DarrellT. Fully convolutional networks for semantic segmentation in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE jun 2015.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_3_1_14_2","doi-asserted-by":"crossref","unstructured":"LucioD.R. LarocaR. SeveroE. BrittoA.S. and MenottiD. Fully convolutional networks and generative adversarial networks applied to sclera segmentation in 2018 IEEE 9th International Conference on Biometrics Theory Applications and Systems (BTAS) IEEE oct 2018.","DOI":"10.1109\/BTAS.2018.8698597"},{"key":"e_1_3_1_15_2","doi-asserted-by":"crossref","unstructured":"HuynhV.T. KimS.-H. LeeG.-S. and YangH.-J. Eye semantic segmentation with a lightweight model in 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW) IEEE oct 2019.","DOI":"10.1109\/ICCVW.2019.00457"},{"key":"e_1_3_1_16_2","doi-asserted-by":"crossref","unstructured":"BoutrosF. DamerN. KirchbuchnerF. and KuijperA. Eye-MMS: Miniature multi-scale segmentation network of key eye-regions in embedded applications in 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW) IEEE oct 2019.","DOI":"10.1109\/ICCVW.2019.00452"},{"key":"e_1_3_1_17_2","doi-asserted-by":"crossref","unstructured":"ChaudharyA.K. KothariR. AcharyaM. DangiS. NairN. BaileyR. KananC. DiazG. and PelzJ.B. RITnet: Real-time semantic segmentation of the eye for gaze tracking in 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW) IEEE oct 2019.","DOI":"10.1109\/ICCVW.2019.00568"},{"key":"e_1_3_1_18_2","doi-asserted-by":"crossref","unstructured":"PerryJ. and FernandezA. MinENet: A dilated CNN for semantic segmentation of eye features in 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW) IEEE oct 2019.","DOI":"10.1109\/ICCVW.2019.00453"},{"key":"e_1_3_1_19_2","doi-asserted-by":"crossref","unstructured":"McMurroughC.D. MetsisV. RichJ. and MakedonF. An eye tracking dataset for point of gaze detection in Proceedings of the Symposium on Eye Tracking Research and Applications \u2013 ETRA \u201912. ACM Press 2012.","DOI":"10.1145\/2168556.2168622"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-019-00220-4"},{"key":"e_1_3_1_21_2","unstructured":"LuoB. ShenJ. WangY. and PanticM. The ibug eye segmentation dataset 2019."},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2019.05.016"},{"key":"e_1_3_1_23_2","doi-asserted-by":"crossref","unstructured":"RotP. EmersicZ. StrucV. and PeerP. Deep multi-class eye segmentation for ocular biometrics in 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI) IEEE jul 2018.","DOI":"10.1109\/IWOBI.2018.8464133"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/210\/1\/012031"},{"key":"e_1_3_1_25_2","doi-asserted-by":"crossref","unstructured":"BadrinarayananV. KendallA. and CipollaR. Segnet: Adeep convolutional encoder-decoder architecture for image segmentation IEEE Transactions onPattern Analysis and Machine Intelligence 2017.","DOI":"10.1109\/TPAMI.2016.2644615"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-189858","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-189858","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-189858","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T01:52:44Z","timestamp":1769997164000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-189858"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,25]]},"references-count":24,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,11,17]]}},"alternative-id":["10.3233\/JIFS-189858"],"URL":"https:\/\/doi.org\/10.3233\/jifs-189858","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,25]]}}}