{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T10:43:49Z","timestamp":1742985829780,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030727918"},{"type":"electronic","value":"9783030727925"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-72792-5_51","type":"book-chapter","created":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T16:34:22Z","timestamp":1619454862000},"page":"651-666","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Level Set Segmentation Based on the Prior Shape of Biological Feature"],"prefix":"10.1007","author":[{"given":"Ji","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Dongxu","family":"Ji","sequence":"additional","affiliation":[]},{"given":"Yuxiang","family":"Feng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,27]]},"reference":[{"key":"51_CR1","doi-asserted-by":"publisher","first-page":"20835","DOI":"10.1007\/s11042-019-7424-8","volume":"78","author":"M Afifi","year":"2019","unstructured":"Afifi, M.: 11K Hands: Gender recognition and biometric identification using a large dataset of hand images. Multimed. Tools Appl. 78, 20835\u201320854 (2019). https:\/\/doi.org\/10.1007\/s11042-019-7424-8","journal-title":"Multimed. Tools Appl."},{"issue":"3","key":"51_CR2","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1049\/iet-bmt.2017.0204","volume":"7","author":"A Sepas-Moghaddam","year":"2018","unstructured":"Sepas-Moghaddam, A., Pereira, F., Correia, P.L.: Ear recognition in a light field imaging framework: a new perspective. IET Biometrics 7(3), 224\u2013231 (2018)","journal-title":"IET Biometrics"},{"issue":"1","key":"51_CR3","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TCSVT.2003.818349","volume":"14","author":"AK Jain","year":"2004","unstructured":"Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circ. Syst. Video Technol. Spec. Issue Image Video Based Biometrics 14(1), 4\u201320 (2004)","journal-title":"IEEE Trans. Circ. Syst. Video Technol. Spec. Issue Image Video Based Biometrics"},{"issue":"7","key":"51_CR4","doi-asserted-by":"publisher","first-page":"1743","DOI":"10.1109\/TMC.2015.2465390","volume":"15","author":"E Eriksson","year":"2016","unstructured":"Eriksson, E., Dan, G., Fodor, V.: Predictive distributed visual analysis for video in wireless sensor networks. IEEE Trans. Mob. Comput. 15(7), 1743\u20131756 (2016)","journal-title":"IEEE Trans. Mob. Comput."},{"key":"51_CR5","first-page":"193","volume":"24","author":"A Hajdu","year":"2004","unstructured":"Hajdu, A., Kormos, J., Nagy, B., et al.: Choosing appropriate distance measurement in digital image segmentation. Annales Univ. Sci. Budapest. Sec. Comp. 24, 193\u2013208 (2004)","journal-title":"Annales Univ. Sci. Budapest. Sec. Comp."},{"key":"51_CR6","doi-asserted-by":"publisher","unstructured":"Paragios, N., Chen, Y., Faugeras, O. (eds.): Handbook of Mathematical Models in Computer Vision. Springer, New York (2006). https:\/\/doi.org\/10.1007\/0-387-28831-7","DOI":"10.1007\/0-387-28831-7"},{"key":"51_CR7","doi-asserted-by":"crossref","unstructured":"Dockins, K.: Template method. In: Design Patterns in PHP and Laravel (2017)","DOI":"10.1007\/978-1-4842-2451-9"},{"key":"51_CR8","doi-asserted-by":"crossref","unstructured":"Kuang, H., Cai, S., Ma, X., et al.: An effective skeleton extraction method based on kinect depth image. In: International Conference on Measuring Technology & Mechatronics Automation (2018)","DOI":"10.1109\/ICMTMA.2018.00052"},{"key":"51_CR9","doi-asserted-by":"crossref","unstructured":"Leventon, M.E., Grimson, W.E.L., Faugeras, O.: Statistical shape influence in geodesic active contours. In: Proceedings of the IEEE Conference on Compute Vision Pattern Recognition, vol. 1, pp. 316\u2013323 (2000)","DOI":"10.1109\/CVPR.2000.855835"},{"key":"51_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/3-540-47967-8_6","volume-title":"Computer Vision \u2014 ECCV 2002","author":"M Rousson","year":"2002","unstructured":"Rousson, M., Paragios, N.: Shape priors for level set representations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 78\u201392. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-47967-8_6"},{"key":"51_CR11","doi-asserted-by":"crossref","unstructured":"Pan, B., Wang, W., Yan, J., et al.: For prostate MRI segmentation: a prior-shape-based level set model combined with gradient and regional information. In: 2018 IEEE International Conference on Mechatronics and Automation (ICMA) (2018)","DOI":"10.1109\/ICMA.2018.8484342"},{"issue":"3","key":"51_CR12","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1023\/A:1020826424915","volume":"50","author":"D Cremers","year":"2002","unstructured":"Cremers, D., Tischhauser, E., Weickert, J., Schnorr, C.: Diffusion snakes: introducing statistical shape knowledge into the Mumford-Shah functional. Int. J. Comput. Vis. 50(3), 295\u20133133 (2002)","journal-title":"Int. J. Comput. Vis."},{"issue":"1","key":"51_CR13","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/s11263-005-3676-z","volume":"66","author":"D Cremers","year":"2006","unstructured":"Cremers, D., Sochen, N., Schnorr, C.: A multiphase dynamic labeling model for variational recognition-driven image segmentation. Int. J. Comput. Vis. 66(1), 67\u201381 (2006)","journal-title":"Int. J. Comput. Vis."},{"issue":"1","key":"51_CR14","doi-asserted-by":"publisher","first-page":"l33","DOI":"10.1109\/TGRS.2008.2002027","volume":"47","author":"K Karantzalos","year":"2009","unstructured":"Karantzalos, K., Paragios, N.: Recognition-driven two-dimensional competing priors toward automatic and accurate building detection. IEEE Trans. Geosci. Remote Sens. 47(1), l33-144 (2009)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"5","key":"51_CR15","doi-asserted-by":"publisher","first-page":"2283","DOI":"10.1109\/TGRS.2009.2039220","volume":"48","author":"K Karantzalos","year":"2010","unstructured":"Karantzalos, K., Paragios, N.: Large-scale building reconstruction through information fusion and 3-D priors. IEEE Trans. Geosci. Remote Sens. 48(5), 2283\u20132296 (2010)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"51_CR16","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1006\/cviu.1995.1004","volume":"61","author":"TF Cootes","year":"1995","unstructured":"Cootes, T.F., Taylor, C.J., Cooper, D.H., Grahan, J.: Active shape models their training and application. Comput. Vis. Image Underst. 61(1), 38\u201359 (1995)","journal-title":"Comput. Vis. Image Underst."},{"issue":"2","key":"51_CR17","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1109\/T-C.1975.224183","volume":"24","author":"AKC Wong","year":"1975","unstructured":"Wong, A.K.C., Liu, T.S.: Typicality, diversity, and feature pattern of an ensemble. IEEE Trans. Comput. 24(2), 158\u2013181 (1975)","journal-title":"IEEE Trans. Comput."},{"issue":"3","key":"51_CR18","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1049\/iet-bmt.2017.0240","volume":"7","author":"Z Emersic","year":"2018","unstructured":"Emersic, Z., Gabriel, L.L., Struc, V., et al.: Convolutional encoder-decoder networks for pixel-wise ear detection and segmentation. IET Biometrics 7(3), 175\u2013184 (2018)","journal-title":"IET Biometrics"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Simulation Tools and Techniques"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-72792-5_51","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T09:30:47Z","timestamp":1724923847000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-72792-5_51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030727918","9783030727925"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72792-5_51","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"27 April 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SIMUtools","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Simulation Tools and Techniques","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guiyang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"simutools2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/simutools.eai-conferences.org\/2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"354","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"125","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to COVID 19 pandemic the conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}