{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T19:43:17Z","timestamp":1760902997214,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":56,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032060068","type":"print"},{"value":"9783032060075","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-06007-5_10","type":"book-chapter","created":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T19:03:36Z","timestamp":1760900616000},"page":"163-177","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Objective Assessment of\u00a0Cleft Surgical Outcomes: A GAN-Inpainting Based Approach"],"prefix":"10.1007","author":[{"given":"Daniel Anojan","family":"Atputharuban","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christoph","family":"Theopold","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aonghus","family":"Lawlor","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,20]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","unstructured":"Ali, H., et al.: Correction: the role of generative adversarial networks in brain MRI: a scoping review. Insights Imaging 13 (12 2022). https:\/\/doi.org\/10.1186\/s13244-022-01268-7","DOI":"10.1186\/s13244-022-01268-7"},{"issue":"4","key":"10_CR2","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1597\/1545-1569_1991_028_0385_doamfr_2.3.co_2","volume":"28","author":"C Asher-Mcdade","year":"1991","unstructured":"Asher-Mcdade, C., Roberts, C., Shaw, W.C., Gallager, C.: Development of a method for rating nasolabial appearance in patients with clefts of the lip and palate. Cleft Palate Craniofac. J. 28(4), 385\u2013391 (1991)","journal-title":"Cleft Palate Craniofac. J."},{"key":"10_CR3","doi-asserted-by":"publisher","unstructured":"Atputharuban, D., Theopold, C., Lawlor, A.: Enhancing surgical visualization: feasibility study on GAN-based image generation for post operative cleft palate images. In: Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods, pp. 939\u2013945 (2024). https:\/\/doi.org\/10.5220\/0012576900003654","DOI":"10.5220\/0012576900003654"},{"key":"10_CR4","doi-asserted-by":"publisher","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional siamese networks for object tracking. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 850\u2013865. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-48881-3_56","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Best-Rowden, L., Hoole, Y., Jain, A.: Automatic face recognition of newborns, infants, and toddlers: a longitudinal evaluation. In: 2016 International Conference of the Biometrics Special Interest Group (BIOSIG), pp.\u00a01\u20138. IEEE (2016)","DOI":"10.1109\/BIOSIG.2016.7736912"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Bromley, J., Guyon, I., LeCun, Y., S\u00e4ckinger, E., Shah, R.: Signature verification using a \u201cSiamese\u201d time delay neural network. In: Advances in Neural Information Processing Systems, vol. 6 (1993)","DOI":"10.1142\/9789812797926_0003"},{"key":"10_CR7","doi-asserted-by":"publisher","unstructured":"Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: VGGFACE2: a dataset for recognising faces across pose and age (2017). https:\/\/doi.org\/10.48550\/ARXIV.1710.08092, https:\/\/arxiv.org\/abs\/1710.08092","DOI":"10.48550\/ARXIV.1710.08092"},{"key":"10_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpa.2023.100517","volume":"17","author":"S Chen","year":"2023","unstructured":"Chen, S., Atapour-Abarghouei, A., Ho, E.S., Shum, H.P.: INCLG: inpainting for non-cleft lip generation with a multi-task image processing network. Softw. Impacts 17, 100517 (2023). https:\/\/doi.org\/10.1016\/j.simpa.2023.100517","journal-title":"Softw. Impacts"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Chen, S., Atapour-Abarghouei, A., Shum, H.P.H.: Hint: high-quality inpainting transformer with mask-aware encoding and enhanced attention (2024). https:\/\/arxiv.org\/abs\/2402.14185","DOI":"10.1109\/TMM.2024.3369897"},{"key":"10_CR10","unstructured":"Deb, D., Nain, N., Jain, A.K.: Longitudinal study of child face recognition (2017). https:\/\/arxiv.org\/abs\/1711.03990"},{"key":"10_CR11","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"10_CR12","doi-asserted-by":"publisher","unstructured":"Deng, Y., Hui, S., Meng, R., Zhou, S., Wang, J.: Hourglass attention network for image inpainting, pp. 483\u2013501 (2022). https:\/\/doi.org\/10.1007\/978-3-031-19797-0_28","DOI":"10.1007\/978-3-031-19797-0_28"},{"issue":"4","key":"10_CR13","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1111\/ocr.12663","volume":"26","author":"I Duggal","year":"2023","unstructured":"Duggal, I., Talwar, A., Duggal, R., Chaudhari, P.K., Samrit, V.: Comparative evaluation of nasolabial appearance of unilateral cleft lip and palate patients by professional, patient and layperson using 2 aesthetic scoring systems: A cross sectional study. Orthod. Craniofacial Res. 26(4), 660\u2013666 (2023). https:\/\/doi.org\/10.1111\/ocr.12663","journal-title":"Orthod. Craniofacial Res."},{"key":"10_CR14","volume":"2012","author":"R Freitas","year":"2012","unstructured":"Freitas, R., et al.: Beyond fifty years of Millard\u2019s rotation-advancement technique in cleft lip closure: Are there many \u201cMillards\u2019\u2019? Plastic Surg. Int. 2012, 731029 (2012)","journal-title":"Plastic Surg. Int."},{"key":"10_CR15","doi-asserted-by":"publisher","first-page":"1108114","DOI":"10.3389\/frobt.2023.1108114","volume":"10","author":"H Haberfehlner","year":"2023","unstructured":"Haberfehlner, H., et al.: Towards automated video-based assessment of dystonia in Dyskinetic cerebral palsy: a novel approach using markerless motion tracking and machine learning. Front. Robot. AI 10, 1108114 (2023)","journal-title":"Front. Robot. AI"},{"key":"10_CR16","doi-asserted-by":"publisher","first-page":"32406","DOI":"10.1109\/ACCESS.2022.3160174","volume":"10","author":"A Hassanpour","year":"2022","unstructured":"Hassanpour, A., et al.: E2F-GAN: eyes-to-face inpainting via edge-aware coarse-to-fine GANs. IEEE Access 10, 32406\u201332417 (2022)","journal-title":"IEEE Access"},{"key":"10_CR17","doi-asserted-by":"publisher","unstructured":"Hayajneh, A., Shaqfeh, M., Serpedin, E., Stotland, M.A.: Unsupervised anomaly appraisal of cleft faces using a StyleGAN2-based model adaptation technique. arXiv (2022). https:\/\/doi.org\/10.48550\/arxiv.2211.06659","DOI":"10.48550\/arxiv.2211.06659"},{"key":"10_CR18","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015). https:\/\/doi.org\/10.48550\/ARXIV.1512.03385, https:\/\/arxiv.org\/abs\/1512.03385","DOI":"10.48550\/ARXIV.1512.03385"},{"key":"10_CR19","doi-asserted-by":"publisher","unstructured":"Jin, X., Lei, J., Ge, S., Song, C., Yu, H., Wu, C.: Double-blinded finder: a two-side secure children face recognition system. Wirel. Netw. 28, 1\u201310 (2022). https:\/\/doi.org\/10.1007\/s11276-019-02199-w","DOI":"10.1007\/s11276-019-02199-w"},{"key":"10_CR20","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. CoRR abs\/1812.04948 (2018). http:\/\/arxiv.org\/abs\/1812.04948"},{"key":"10_CR21","first-page":"1755","volume":"10","author":"DE King","year":"2009","unstructured":"King, D.E.: DLIB-ML: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755\u20131758 (2009)","journal-title":"J. Mach. Learn. Res."},{"key":"10_CR22","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2017). https:\/\/arxiv.org\/abs\/1412.6980"},{"key":"10_CR23","unstructured":"Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition (2015)"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Kuprashevich, M., Tolstykh, I.: MIVOLO: multi-input transformer for age and gender estimation (2023)","DOI":"10.1007\/978-3-031-54534-4_15"},{"key":"10_CR25","unstructured":"Lahiri, A., Jain, A., Nadendla, D., Biswas, P.K.: Improved techniques for GAN based facial inpainting (2018)"},{"issue":"4","key":"10_CR26","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1002\/ajmg.c.31381","volume":"163","author":"EJ Leslie","year":"2013","unstructured":"Leslie, E.J., Marazita, M.L.: Genetics of cleft lip and cleft palate. Am. J. Med. Genet. C Semin. Med. Genet. 163(4), 246\u2013258 (2013). https:\/\/doi.org\/10.1002\/ajmg.c.31381","journal-title":"Am. J. Med. Genet. C Semin. Med. Genet."},{"key":"10_CR27","doi-asserted-by":"publisher","unstructured":"Li, Y., Cheng, J., Mei, H., Ma, H., Chen, Z., Li, Y.: CLPNet: cleft lip and palate surgery support with deep learning. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3666\u20133672 (2019). https:\/\/doi.org\/10.1109\/EMBC.2019.8857799","DOI":"10.1109\/EMBC.2019.8857799"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Liu, G., Reda, F.A., Shih, K.J., Wang, T.C., Tao, A., Catanzaro, B.: Image inpainting for irregular holes using partial convolutions (2018). https:\/\/arxiv.org\/abs\/1804.07723","DOI":"10.1007\/978-3-030-01252-6_6"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Liu, Y., Shi, H., Shen, H., Si, Y., Wang, X., Mei, T.: A new dataset and boundary-attention semantic segmentation for face parsing. In: AAAI, pp. 11637\u201311644 (2020)","DOI":"10.1609\/aaai.v34i07.6832"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.425"},{"issue":"3","key":"10_CR31","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/s00268-009-0333-7","volume":"34","author":"WP Magee","year":"2010","unstructured":"Magee, W.P., Burg, R.V., Hatcher, K.W.: Cleft lip and palate as a cost-effective health care treatment in the developing world. World J. Surg. 34(3), 420\u2013427 (2010). https:\/\/doi.org\/10.1007\/s00268-009-0333-7","journal-title":"World J. Surg."},{"issue":"1","key":"10_CR32","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1097\/prs.0000000000008063","volume":"148","author":"M McCullough","year":"2021","unstructured":"McCullough, M., et al.: Convolutional neural network models for automatic preoperative severity assessment in unilateral cleft lip. Plast. Reconstr. Surg. 148(1), 162\u2013169 (2021). https:\/\/doi.org\/10.1097\/prs.0000000000008063","journal-title":"Plast. Reconstr. Surg."},{"key":"10_CR33","doi-asserted-by":"crossref","unstructured":"Medvedev, I., Shadmand, F., Gon\u00e7alves, N.: Young labeled faces in the wild (YLFW): a dataset for children faces recognition (2023)","DOI":"10.1109\/FG59268.2024.10582021"},{"issue":"1","key":"10_CR34","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1186\/s12984-024-01406-w","volume":"21","author":"A Mobbs","year":"2024","unstructured":"Mobbs, A., Kahn, M., Williams, G., Mentiplay, B.F., Pua, Y.H., Clark, R.A.: Machine learning for automating subjective clinical assessment of gait impairment in people with acquired brain injury-a comparison of an image extraction and classification system to expert scoring. J. Neuroeng. Rehabil. 21(1), 124 (2024)","journal-title":"J. Neuroeng. Rehabil."},{"key":"10_CR35","doi-asserted-by":"publisher","unstructured":"Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., Zafeiriou, S.: AGEDB: the first manually collected, in-the-wild age database, pp. 1997\u20132005 (2017). https:\/\/doi.org\/10.1109\/CVPRW.2017.250","DOI":"10.1109\/CVPRW.2017.250"},{"issue":"5","key":"10_CR36","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1597\/15-274","volume":"54","author":"DG Mosmuller","year":"2016","unstructured":"Mosmuller, D.G., et al.: the development of the cleft aesthetic rating scale: a new rating scale for the assessment of nasolabial appearance in complete unilateral cleft lip and palate patients. Cleft Palate Craniofac. J. 54(5), 555\u2013561 (2016). https:\/\/doi.org\/10.1597\/15-274","journal-title":"Cleft Palate Craniofac. J."},{"key":"10_CR37","doi-asserted-by":"crossref","unstructured":"Nazeri, K., Ng, E., Joseph, T., Qureshi, F., Ebrahimi, M.: EdgeConnect: structure guided image inpainting using edge prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops (2019)","DOI":"10.1109\/ICCVW.2019.00408"},{"issue":"3","key":"10_CR38","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1008542","volume":"17","author":"MJ Panaggio","year":"2021","unstructured":"Panaggio, M.J., Abrams, D.M., Yang, F., Banerjee, T., Shah, N.R.: Can subjective pain be inferred from objective physiological data? Evidence from patients with sickle cell disease. PLoS Comput. Biol. 17(3), e1008542 (2021)","journal-title":"PLoS Comput. Biol."},{"key":"10_CR39","unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch. In: NIPS (2017). https:\/\/openreview.net\/forum?id=BJJsrmfCZ"},{"key":"10_CR40","doi-asserted-by":"publisher","unstructured":"Patcas, R., et al.: Facial attractiveness of cleft patients: a direct comparison between artificial-intelligence-based scoring and conventional rater groups. Eur. J. Orthod. (2019). https:\/\/doi.org\/10.1093\/ejo\/cjz007","DOI":"10.1093\/ejo\/cjz007"},{"issue":"1","key":"10_CR41","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1597\/10-178","volume":"49","author":"F Rahimov","year":"2012","unstructured":"Rahimov, F., Jugessur, A., Murray, J.C.: Genetics of nonsyndromic orofacial clefts. Cleft Palate Craniofac. J. 49(1), 73\u201391 (2012)","journal-title":"Cleft Palate Craniofac. J."},{"key":"10_CR42","doi-asserted-by":"crossref","unstructured":"Rosero, K., Salman, A.N., Sisman, B., Hallac, R.R., Busso, C.: Enhanced facial landmarks detection for patients with repaired cleft lip and palate. In: 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1\u201310. IEEE (2024)","DOI":"10.1109\/FG59268.2024.10582022"},{"key":"10_CR43","doi-asserted-by":"publisher","unstructured":"Sayadi, L.R., Hamdan, U.S., Zhangli, Q., Hu, J., Vyas, R.M.: Harnessing the power of artificial intelligence to teach cleft lip surgery. Plastic Reconstruct. Surgery - Global Open (2022). https:\/\/doi.org\/10.1097\/gox.0000000000004451","DOI":"10.1097\/gox.0000000000004451"},{"key":"10_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107700","volume":"113","author":"M Shorfuzzaman","year":"2021","unstructured":"Shorfuzzaman, M., Hossain, M.S.: MetaCovid: A Siamese neural network framework with contrastive loss for n-shot diagnosis of Covid-19 patients. Pattern Recogn. 113, 107700 (2021)","journal-title":"Pattern Recogn."},{"key":"10_CR45","doi-asserted-by":"crossref","unstructured":"Sola, S., Gera, D.: Unmasking your expression: Expression-conditioned GAN for masked face inpainting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 5908\u20135916 (2023)","DOI":"10.1109\/CVPRW59228.2023.00628"},{"key":"10_CR46","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701\u20131708 (2014)","DOI":"10.1109\/CVPR.2014.220"},{"key":"10_CR47","doi-asserted-by":"publisher","unstructured":"Tran, M.T., Kim, S.H., Yang, H.J., Lee, G.S.: Multi-task learning for medical image inpainting based on organ boundary awareness. Appl. Sci. 11(9) (2021). https:\/\/doi.org\/10.3390\/app11094247, https:\/\/www.mdpi.com\/2076-3417\/11\/9\/4247","DOI":"10.3390\/app11094247"},{"key":"10_CR48","doi-asserted-by":"publisher","unstructured":"Wadde, D., Chowdhar, D., Venkatakrishnan, D., Ghodake, D., Sachdev, S., Chhapane, D.: Protocols in the management of cleft lip and palate: a systematic review. J. Stomatol. Oral Maxillofacial Surg. 124 (2022). https:\/\/doi.org\/10.1016\/j.jormas.2022.11.014","DOI":"10.1016\/j.jormas.2022.11.014"},{"issue":"4","key":"10_CR49","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004). https:\/\/doi.org\/10.1109\/TIP.2003.819861","journal-title":"IEEE Trans. Image Process."},{"key":"10_CR50","doi-asserted-by":"crossref","unstructured":"Watson, D.S., et al.: Clinical applications of machine learning algorithms: beyond the black box. BMJ 364, l886 (2019)","DOI":"10.1136\/bmj.l886"},{"issue":"1","key":"10_CR51","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1093\/cid\/cix731","volume":"66","author":"J Wiens","year":"2017","unstructured":"Wiens, J., Shenoy, E.S.: Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clin. Infect. Dis. 66(1), 149\u2013153 (2017). https:\/\/doi.org\/10.1093\/cid\/cix731","journal-title":"Clin. Infect. Dis."},{"key":"10_CR52","doi-asserted-by":"crossref","unstructured":"Yilmaz, H.N., Ozbilen, E.O., and, T.U.: The prevalence of cleft lip and palate patients: a single-center experience for 17 years. Turkish J. Orthod. 32(3), 139\u2013144 (2019)","DOI":"10.5152\/TurkJOrthod.2019.18094"},{"key":"10_CR53","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"10_CR54","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: Face inpainting based on GAN by facial prediction and fusion as guidance information. Appl. Soft Comput. 111, 107626 (2021). https:\/\/api.semanticscholar.org\/CorpusID:237658209","DOI":"10.1016\/j.asoc.2021.107626"},{"key":"10_CR55","doi-asserted-by":"publisher","unstructured":"Zheng, C., Cham, T.J., Cai, J.: Pluralistic free-from image completion. Int. J. Comput. Vis., 1\u201320 (2021). https:\/\/doi.org\/10.1007\/s11263-021-01502-7","DOI":"10.1007\/s11263-021-01502-7"},{"key":"10_CR56","doi-asserted-by":"crossref","unstructured":"Zheng, Y., et al.: General facial representation learning in a visual-linguistic manner. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18697\u201318709 (2022)","DOI":"10.1109\/CVPR52688.2022.01814"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition Applications and Methods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-06007-5_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T19:03:41Z","timestamp":1760900621000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-06007-5_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,20]]},"ISBN":["9783032060068","9783032060075"],"references-count":56,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-06007-5_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,20]]},"assertion":[{"value":"20 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPRAM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition Applications and Methods","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rome","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 February 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 February 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpram2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}