{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T09:19:28Z","timestamp":1742980768962,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030129385"},{"type":"electronic","value":"9783030129392"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-12939-2_29","type":"book-chapter","created":{"date-parts":[[2019,2,14]],"date-time":"2019-02-14T19:07:18Z","timestamp":1550171238000},"page":"422-433","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Distance Transform to Segment Visually Indistinguishable Merged Objects"],"prefix":"10.1007","author":[{"given":"S\u00f6ren","family":"Klemm","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyi","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin","family":"Risse","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,2,14]]},"reference":[{"key":"29_CR1","doi-asserted-by":"publisher","unstructured":"Bai, M., Urtasun, R.: Deep watershed transform for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2858\u20132866 (2017). \n                      https:\/\/doi.org\/10.1109\/CVPR.2017.305","DOI":"10.1109\/CVPR.2017.305"},{"issue":"7","key":"29_CR2","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.tree.2014.05.004","volume":"29","author":"AI Dell","year":"2014","unstructured":"Dell, A.I., et al.: Automated image-based tracking and its application in ecology. Trends Ecol. Evol. 29(7), 417\u2013428 (2014). \n                      https:\/\/doi.org\/10.1016\/j.tree.2014.05.004","journal-title":"Trends Ecol. Evol."},{"key":"29_CR3","doi-asserted-by":"publisher","unstructured":"Fiaschi, L., et al.: Tracking indistinguishable translucent objects over time using weakly supervised structured learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2736\u20132743 (2014). \n                      https:\/\/doi.org\/10.1109\/CVPR.2014.356","DOI":"10.1109\/CVPR.2014.356"},{"key":"29_CR4","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256 (2010)"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Jia, Y., et al.: Caffe: Convolutional architecture for fast feature embedding. CoRR abs\/1408.5093 (2014)","DOI":"10.1145\/2647868.2654889"},{"key":"29_CR6","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs\/1412.6980 (2014)"},{"key":"29_CR7","unstructured":"Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. In: Proceedings of the 30th Annual Conference on Neural Information Processing Systems, pp. 972\u2013981 (2017)"},{"key":"29_CR8","unstructured":"Klemm, S., Scherzinger, A., Drees, D., Jiang, X.: Barista - a graphical tool for designing and training deep neural networks. CoRR abs\/1802.04626 (2018)"},{"key":"29_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/978-3-642-35289-8_3","volume-title":"Neural Networks: Tricks of the Trade","author":"Y LeCun","year":"2012","unstructured":"LeCun, Y., Bottou, L., Orr, G.B., M\u00fcller, K.: Efficient backprop. In: Montavon, G., Orr, G.B., M\u00fcller, K. (eds.) Neural Networks: Tricks of the Trade. Lecture Notes in Computer Science, vol. 7700, 2nd edn, pp. 9\u201348. Springer, Heidelberg (2012). \n                      https:\/\/doi.org\/10.1007\/978-3-642-35289-8_3","edition":"2"},{"key":"29_CR10","doi-asserted-by":"publisher","unstructured":"Michels, T., Berh, D., Jiang, X.: An RJMCMC-based method for tracking and resolving collisions of drosophila larvae. IEEE\/ACM Trans. Comput. Biol. Bioinform. (2017). \n                      https:\/\/doi.org\/10.1109\/TCBB.2017.2779141","DOI":"10.1109\/TCBB.2017.2779141"},{"issue":"7","key":"29_CR11","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1038\/nmeth.2994","volume":"11","author":"Alfonso P\u00e9rez-Escudero","year":"2014","unstructured":"P\u00e9rez-Escudero, A., Vicente-Page, J., Hinz, R.C., Arganda, S., de Polavieja, G.G.: idTracker: tracking individuals in a group by automatic identification of unmarked animals. Nat. Methods 11, (2014). \n                      https:\/\/doi.org\/10.1038\/nmeth.2994","journal-title":"Nature Methods"},{"issue":"3","key":"29_CR12","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/TBME.2016.2570598","volume":"64","author":"B Risse","year":"2017","unstructured":"Risse, B., Otto, N., Berh, D., Jiang, X., Kiel, M., Kl\u00e4mbt, C.: FIM\n                      \n                        \n                      \n                      $$^{2{\\rm c}}$$\n                    : multicolor, multipurpose imaging system to manipulate and analyze animal behavior. IEEE Trans. Biomed. Eng. 64(3), 610\u2013620 (2017). \n                      https:\/\/doi.org\/10.1109\/TBME.2016.2570598","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"29_CR13","doi-asserted-by":"crossref","unstructured":"Romero-Ferrero, F., Bergomi, M.G., Hinz, R., Heras, F.J.H., de Polavieja, G.G.: idtracker.ai: tracking all individuals in large collectives of unmarked animals. CoRR abs\/1803.04351 (2018)","DOI":"10.1101\/280735"},{"key":"29_CR14","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Lecture Notes in Computer Science","author":"Olaf Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241 (2015). \n                      https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"29_CR15","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/978-3-319-64689-3_29","volume-title":"Computer Analysis of Images and Patterns","author":"Aaron Scherzinger","year":"2017","unstructured":"Scherzinger, A., Klemm, S., Berh, D., Jiang, X.: CNN-based background subtraction for long-term in-vial FIM imaging. In: Proceedings of the 17th International Conference on Computer Analysis of Images and Patterns, pp. 359\u2013371 (2017). \n                      https:\/\/doi.org\/10.1007\/978-3-319-64689-3_29"},{"key":"29_CR16","doi-asserted-by":"publisher","unstructured":"Yurchenko, V., Lempitsky, V.S.: Parsing images of overlapping organisms with deep singling-out networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4752\u20134760 (2017). \n                      https:\/\/doi.org\/10.1109\/CVPR.2017.505","DOI":"10.1109\/CVPR.2017.505"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-12939-2_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,21]],"date-time":"2019-05-21T22:35:51Z","timestamp":1558478151000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-12939-2_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030129385","9783030129392"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-12939-2_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"14 February 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"GCPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"German Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Stuttgart","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 October 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"40","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dagm2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/gcprvmv2018.vis.uni-stuttgart.de\/","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"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"118","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"48","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"41% - 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"}},{"value":"2.92","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"6.27","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}