{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T08:59:12Z","timestamp":1774947552240,"version":"3.50.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2022,4,24]],"date-time":"2022-04-24T00:00:00Z","timestamp":1650758400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,24]],"date-time":"2022-04-24T00:00:00Z","timestamp":1650758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100002635","name":"Inha University","doi-asserted-by":"publisher","award":["65324-01"],"award-info":[{"award-number":["65324-01"]}],"id":[{"id":"10.13039\/501100002635","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s00521-022-07269-3","type":"journal-article","created":{"date-parts":[[2022,4,24]],"date-time":"2022-04-24T13:02:31Z","timestamp":1650805351000},"page":"15543-15554","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Image based rainfall amount estimation for auto-wiping of vehicles"],"prefix":"10.1007","volume":"34","author":[{"given":"Seung Hoon","family":"Lee","sequence":"first","affiliation":[]},{"given":"Jung Ho","family":"Jeon","sequence":"additional","affiliation":[]},{"given":"Dong Yoon","family":"Choi","sequence":"additional","affiliation":[]},{"given":"Jong Min","family":"Park","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8742-3433","authenticated-orcid":false,"given":"Byung Cheol","family":"Song","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,24]]},"reference":[{"key":"7269_CR1","unstructured":"Ishikawa J (10 Aug. 2010) Raindrop detecting device and method of selecting wiping mode for vehicle. U.S. Patent No. 7,772,793"},{"key":"7269_CR2","doi-asserted-by":"crossref","unstructured":"VanDam SA (14 Jul 1998) Windshield wiper rain sensor system. U.S. Patent No. 5,780,719","DOI":"10.1038\/nsb0198-14"},{"key":"7269_CR3","doi-asserted-by":"crossref","unstructured":"Cord A, Aubert D (2011) Towards rain detection through use of in-vehicle multipurpose cameras In: Proc IEEE IV Symp, Baden-Baden, Germany, pp 399\u2013404","DOI":"10.1109\/IVS.2011.5940484"},{"key":"7269_CR4","doi-asserted-by":"crossref","unstructured":"Vijay CS, Bhat R, Ragavan V (2018) Raindrop detection considering extremal regions and salient features In: Proceedings electronic imaging, pp 348-1-348-6","DOI":"10.2352\/ISSN.2470-1173.2018.17.AVM-348"},{"key":"7269_CR5","doi-asserted-by":"crossref","unstructured":"Qian R, Tan RT, Yang W, Su J, Liu J (2018) Attentive generative adversarial network for raindrop removal from a single image In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2482-2491","DOI":"10.1109\/CVPR.2018.00263"},{"issue":"1","key":"7269_CR6","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Transactions Syst Man Cybern 9(1):62\u201366","journal-title":"IEEE Transactions Syst Man Cybern"},{"key":"7269_CR7","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1016\/j.procs.2015.06.090","volume":"54","author":"N Dhanachandra","year":"2015","unstructured":"Dhanachandra N, Manglem K, Chanu YJ (2015) Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Computer Sci 54:764\u2013771","journal-title":"Procedia Computer Sci"},{"issue":"1","key":"7269_CR8","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/0165-1684(94)90059-0","volume":"38","author":"L Najman","year":"1994","unstructured":"Najman L, Schmitt M (1994) Watershed of a continuous function. Signal Process 38(1):99\u2013112","journal-title":"Signal Process"},{"issue":"4","key":"7269_CR9","first-page":"321","volume":"1","author":"M Kass","year":"1988","unstructured":"Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Computer V 1(4):321\u2013331","journal-title":"Int J Computer V"},{"issue":"11","key":"7269_CR10","doi-asserted-by":"publisher","first-page":"1222","DOI":"10.1109\/34.969114","volume":"23","author":"Y Boykov","year":"2001","unstructured":"Boykov Y, Veksler O, Zabih R (2001) Fast approximate energy minimization via graph cuts. IEEE Transactions Pattern Anal Mach Intell 23(11):1222\u20131239","journal-title":"IEEE Transactions Pattern Anal Mach Intell"},{"key":"7269_CR11","doi-asserted-by":"crossref","unstructured":"Plath N, Toussaint M, Nakajima S (2009) Multi-class image segmentation using conditional random fields and global classification In: Proceedings of the 26th Annual International Conference on Machine Learning, pp 817\u2013824","DOI":"10.1145\/1553374.1553479"},{"key":"7269_CR12","doi-asserted-by":"crossref","unstructured":"Minaee S, Boykov YY, Porikli F, Plaza AJ, Kehtarnavaz N, Terzopoulos D (2021) Image segmentation using deep learning: a survey IEEE Transactions Pattern Anal Mach Intell (Early Access)","DOI":"10.1109\/TPAMI.2021.3059968"},{"key":"7269_CR13","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation In: International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"7269_CR14","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431-3440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"7269_CR15","doi-asserted-by":"crossref","unstructured":"Fan DP, Ji GP, Zhou T, Chen G, Fu H, Shen J, Shao L (2020) Pranet: parallel reverse attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention Springer, Cham, pp 263\u2013273","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"7269_CR16","doi-asserted-by":"crossref","unstructured":"Jha D, Riegler MA, Johansen D, Halvorsen P, Johansen HD (2020) DoubleU-Net: a deep convolutional neural network for medical image segmentation In: arXiv preprint arXiv:2006.04868","DOI":"10.1109\/CBMS49503.2020.00111"},{"issue":"10","key":"7269_CR17","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","volume":"38","author":"Z Gu","year":"2019","unstructured":"Gu Z, Cheng J, Fu H, Zhou K, Hao H, Zhao Y, Liu J (2019) Ce-net: context encoder network for 2d medical image segmentation. Proc IEEE Transactions Med Imag 38(10):2281\u20132292","journal-title":"Proc IEEE Transactions Med Imag"},{"issue":"3","key":"7269_CR18","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1109\/TNNLS.2021.3054746","volume":"32","author":"N Paluru","year":"2021","unstructured":"Paluru N et al (2021) Anam-Net: anamorphic depth embedding-based lightweight CNN for segmentation of anomalies in COVID-19 chest CT images. IEEE Transactions Neural Netw Learn Syst 32(3):932\u2013946","journal-title":"IEEE Transactions Neural Netw Learn Syst"},{"key":"7269_CR19","doi-asserted-by":"publisher","first-page":"104815","DOI":"10.1016\/j.compbiomed.2021.104815","volume":"137","author":"M Yeung","year":"2021","unstructured":"Yeung M, Sala E, Sch\u00f6nlieb C-B, Rundo L (2021) Focus U-Net: a novel dual attention-gated CNN for polyp segmentation during colonoscopy. Computers Biol Med 137:104815","journal-title":"Computers Biol Med"},{"key":"7269_CR20","unstructured":"Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. In: arXiv preprint arXiv:1511.07122"},{"key":"7269_CR21","doi-asserted-by":"crossref","unstructured":"Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation In: Proceedings of the European conference on computer vision, pp 801-818","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"7269_CR22","unstructured":"Liu W, Rabinovich A, Berg AC (2015) Parsenet: looking wider to see better. In: arXiv preprint arXiv:1506.04579"},{"key":"7269_CR23","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, ... Polosukhin I (2017) Attention is all you need Adv Neural Information Process Syst pp 5998-6008"},{"key":"7269_CR24","doi-asserted-by":"crossref","unstructured":"Meng Z, Gaur Y, Li J, Gong Y (2019) Character-aware attention-based end-to-end speech recognition In: Proceedings of the IEEE Automatic Speech Recognition and Understanding Workshop, pp 949-955","DOI":"10.1109\/ASRU46091.2019.9004018"},{"key":"7269_CR25","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3146-3154","DOI":"10.1109\/CVPR.2019.00326"},{"key":"7269_CR26","doi-asserted-by":"crossref","unstructured":"Zhang F, Chen Y, Li Z, Hong Z, Liu J, Ma F, ... Ding E, (2019) Acfnet: attentional class feature network for semantic segmentation In: Proceedings of the IEEE International Conference on Computer Vision, pp 6798-6807","DOI":"10.1109\/ICCV.2019.00690"},{"key":"7269_CR27","doi-asserted-by":"crossref","unstructured":"Strudel R, Garcia R, Laptev I, Schmid C (2021) Segmenter: transformer for semantic segmentation In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 7262\u20137272","DOI":"10.1109\/ICCV48922.2021.00717"},{"key":"7269_CR28","unstructured":"Vaswani A et al. (2017) Attention is all you need Advances in neural information processing systems. In: Proceedings of the  advances in neural  information processing systems, pp 5998\u20136008"},{"key":"7269_CR29","unstructured":"Dosovitskiy A et al. (2021) An Image is Worth 16x16 Words: transformers for image recognition at scale ICLR.  In: Proceedings of the international conference on learning representations, pp 1\u201321"},{"key":"7269_CR30","doi-asserted-by":"crossref","unstructured":"Choi S, Kim JT, Choo J (2020) Cars can\u2019t fly up in the sky: improving urban-scene segmentation via height-driven attention networks In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR42600.2020.00939"},{"key":"7269_CR31","doi-asserted-by":"crossref","unstructured":"You S, Tan RT, Kawakami R, Ikeuchi K (2013) Adherent raindrop detection and removal in video In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1035\u20131042","DOI":"10.1109\/CVPR.2013.138"},{"key":"7269_CR32","doi-asserted-by":"crossref","unstructured":"Ito K, Noro K, Aoki T (2015) An adherent raindrop detection method using MSER In: 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), pp 105\u2013109","DOI":"10.1109\/APSIPA.2015.7415468"},{"key":"7269_CR33","doi-asserted-by":"crossref","unstructured":"Ishizuka J, Onoguchi K (2016) Detection of raindrop with various shapes on aWindshield ICPRAM, pp 475\u2013483","DOI":"10.5220\/0005796004750483"},{"key":"7269_CR34","first-page":"802","volume":"28","author":"X Shi","year":"2015","unstructured":"Shi X, Chen Z, Wang H, Yeung DY, Wong WK, Woo WC (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting. Proc Adv Neural Information Process Syst 28:802\u2013810","journal-title":"Proc Adv Neural Information Process Syst"},{"key":"7269_CR35","doi-asserted-by":"crossref","unstructured":"Lin J, Dai L (2020) X-NET for single image raindrop removal. In: Proceeding of the IEEE International Conference on Image Processing, pp 1003-1007","DOI":"10.1109\/ICIP40778.2020.9191073"},{"key":"7269_CR36","doi-asserted-by":"crossref","unstructured":"Alletto S, Carlin C, Rigazio L, Ishii Y, Tsukizawa S (2019) Adherent raindrop removal with self-supervised attention maps and spatio-temporal generative adversarial networks In: Proceedings of the IEEE International Conference on Computer Vision Workshops","DOI":"10.1109\/ICCVW.2019.00286"},{"key":"7269_CR37","unstructured":"Welch G, Bishop G (1995) An introduction to the Kalman filter. In: Proceedings of the  SIGGRAPH, Course 8, pp 127-132"},{"key":"7269_CR38","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, ... Glocker B (2018) Attention u-net: learning where to look for the pancreas In: arXiv preprint arXiv:1804.03999"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07269-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07269-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07269-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T18:22:42Z","timestamp":1662056562000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07269-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,24]]},"references-count":38,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["7269"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07269-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,24]]},"assertion":[{"value":"5 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}