{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T21:47:28Z","timestamp":1742939248945,"version":"3.40.3"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030585648"},{"type":"electronic","value":"9783030585655"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-58565-5_31","type":"book-chapter","created":{"date-parts":[[2020,11,11]],"date-time":"2020-11-11T12:03:19Z","timestamp":1605096199000},"page":"514-530","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["On the Usage of the Trifocal Tensor in Motion Segmentation"],"prefix":"10.1007","author":[{"given":"Federica","family":"Arrigoni","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luca","family":"Magri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomas","family":"Pajdla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,12]]},"reference":[{"key":"31_CR1","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/s11263-019-01240-x","volume":"128","author":"F Arrigoni","year":"2020","unstructured":"Arrigoni, F., Fusiello, A.: Synchronization problems in computer vision with closed-form solutions. Int. J. Comput. Vis. 128, 26\u201352 (2020)","journal-title":"Int. J. Comput. Vis."},{"key":"31_CR2","doi-asserted-by":"crossref","unstructured":"Arrigoni, F., Pajdla, T.: Motion segmentation via synchronization. In: IEEE International Conference on Computer Vision Workshops (ICCVW) (2019)","DOI":"10.1109\/ICCVW.2019.00527"},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"Arrigoni, F., Pajdla, T.: Robust motion segmentation from pairwise matches. In: Proceedings of the International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00076"},{"key":"31_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/978-3-030-01270-0_14","volume-title":"Computer Vision \u2013 ECCV 2018","author":"D Barath","year":"2018","unstructured":"Barath, D., Matas, J.: Multi-class model fitting by energy minimization and mode-seeking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 229\u2013245. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01270-0_14"},{"key":"31_CR5","doi-asserted-by":"crossref","unstructured":"Chin, T.J., Suter, D., Wang, H.: Multi-structure model selection via kernel optimisation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3586\u20133593 (2010)","DOI":"10.1109\/CVPR.2010.5539931"},{"issue":"1","key":"31_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11263-011-0437-z","volume":"96","author":"A Delong","year":"2012","unstructured":"Delong, A., Osokin, A., Isack, H.N., Boykov, Y.: Fast approximate energy minimization with label costs. Int. J. Comput. Vis. 96(1), 1\u201327 (2012)","journal-title":"Int. J. Comput. Vis."},{"issue":"11","key":"31_CR7","doi-asserted-by":"publisher","first-page":"2765","DOI":"10.1109\/TPAMI.2013.57","volume":"35","author":"E Elhamifar","year":"2013","unstructured":"Elhamifar, E., Vidal, R.: Sparse subspace clustering: algorithm, theory, and applications. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2765\u20132781 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"31_CR8","first-page":"726","volume":"24","author":"M Fischler","year":"1987","unstructured":"Fischler, M., Bolles, R.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Morgan Kaufmann Readings Ser. 24, 726\u2013740 (1987)","journal-title":"Morgan Kaufmann Readings Ser."},{"key":"31_CR9","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"31_CR10","doi-asserted-by":"publisher","unstructured":"Hartley, R., Vidal, R.: The multibody trifocal tensor: motion segmentation from 3 perspective views. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. I-769-I-775, June 2004. https:\/\/doi.org\/10.1109\/CVPR.2004.1315109","DOI":"10.1109\/CVPR.2004.1315109"},{"key":"31_CR11","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511811685","volume-title":"Multiple View Geometry in Computer Vision","author":"RI Hartley","year":"2004","unstructured":"Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)","edition":"2"},{"issue":"2","key":"31_CR12","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1023\/A:1007936012022","volume":"22","author":"R Hartley","year":"1997","unstructured":"Hartley, R.: Lines and points in three views and the trifocal tensor. Int. J. Comput. Vis. 22(2), 125\u2013140 (1997)","journal-title":"Int. J. Comput. Vis."},{"issue":"9","key":"31_CR13","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1080\/03610927708827533","volume":"6","author":"PW Holland","year":"1977","unstructured":"Holland, P.W., Welsch, R.E.: Robust regression using iteratively reweighted least-squares. Commun. Stat. Theory Methods 6(9), 813\u2013827 (1977)","journal-title":"Commun. Stat. Theory Methods"},{"issue":"2","key":"31_CR14","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/s11263-011-0474-7","volume":"97","author":"H Isack","year":"2012","unstructured":"Isack, H., Boykov, Y.: Energy-based geometric multi-model fitting. Int. J. Comput. Vis. 97(2), 123\u2013147 (2012)","journal-title":"Int. J. Comput. Vis."},{"key":"31_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1007\/978-3-319-10599-4_14","volume-title":"Computer Vision \u2013 ECCV 2014","author":"P Ji","year":"2014","unstructured":"Ji, P., Li, H., Salzmann, M., Dai, Y.: Robust motion segmentation with unknown correspondences. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 204\u2013219. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10599-4_14"},{"key":"31_CR16","doi-asserted-by":"crossref","unstructured":"Ji, P., Li, H., Salzmann, M., Zhong, Y.: Robust multi-body feature tracker: a segmentation-free approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.417"},{"key":"31_CR17","doi-asserted-by":"crossref","unstructured":"Ji, P., Salzmann, M., Li, H.: Shape interaction matrix revisited and robustified: efficient subspace clustering with corrupted and incomplete data. In: Proceedings of the International Conference on Computer Vision, pp. 4687\u20134695 (2015)","DOI":"10.1109\/ICCV.2015.532"},{"key":"31_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/978-3-319-75786-5_28","volume-title":"Image and Video Technology","author":"LF Juli\u00e0","year":"2018","unstructured":"Juli\u00e0, L.F., Monasse, P.: A critical review of the trifocal tensor estimation. In: Paul, M., Hitoshi, C., Huang, Q. (eds.) PSIVT 2017. LNCS, vol. 10749, pp. 337\u2013349. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75786-5_28"},{"issue":"1","key":"31_CR19","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/S0167-8655(02)00194-0","volume":"24","author":"JB Kim","year":"2003","unstructured":"Kim, J.B., Kim, H.J.: Efficient region-based motion segmentation for a video monitoring system. Pattern Recogn. Lett. 24(1), 113\u2013128 (2003)","journal-title":"Pattern Recogn. Lett."},{"issue":"3","key":"31_CR20","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1007\/s10898-014-0247-2","volume":"62","author":"D Kuang","year":"2014","unstructured":"Kuang, D., Yun, S., Park, H.: SymNMF: nonnegative low-rank approximation of a similarity matrix for graph clustering. J. Global Optim. 62(3), 545\u2013574 (2014). https:\/\/doi.org\/10.1007\/s10898-014-0247-2","journal-title":"J. Global Optim."},{"issue":"2","key":"31_CR21","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1002\/nav.3800020109","volume":"2","author":"HW Kuhn","year":"1955","unstructured":"Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Res. Logistics Q. 2(2), 83\u201397 (1955)","journal-title":"Naval Res. Logistics Q."},{"issue":"4","key":"31_CR22","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1109\/TITS.2016.2596296","volume":"18","author":"T Lai","year":"2017","unstructured":"Lai, T., Wang, H., Yan, Y., Chin, T.J., Zhao, W.L.: Motion segmentation via a sparsity constraint. IEEE Trans. Intell. Transp. Syst. 18(4), 973\u2013983 (2017)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"31_CR23","doi-asserted-by":"crossref","unstructured":"Li, Z., Guo, J., Cheong, L.F., Zhou, S.Z.: Perspective motion segmentation via collaborative clustering. In: Proceedings of the International Conference on Computer Vision, pp. 1369\u20131376 (2013)","DOI":"10.1109\/ICCV.2013.173"},{"key":"31_CR24","unstructured":"Lin, Z., Chen, M., Ma, Y.: The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices. eprint arXiv:1009.5055 (2010)"},{"issue":"5","key":"31_CR25","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1109\/TPAMI.2012.88","volume":"26","author":"G Liu","year":"2013","unstructured":"Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y., Ma, Y.: Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intel. 26(5), 171\u2013184 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intel."},{"issue":"2","key":"31_CR26","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91\u2013110 (2004). https:\/\/doi.org\/10.1023\/B:VISI.0000029664.99615.94","journal-title":"Int. J. Comput. Vis."},{"key":"31_CR27","doi-asserted-by":"crossref","unstructured":"Magri, L., Fusiello, A.: Robust multiple model fitting with preference analysis and low-rank approximation. In: Proceedings of the British Machine Vision Conference, pp. 20.1-20.12. BMVA Press, September 2015","DOI":"10.5244\/C.29.20"},{"key":"31_CR28","doi-asserted-by":"crossref","unstructured":"Magri, L., Fusiello, A.: Multiple models fitting as a set coverage problem. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3318\u20133326, June 2016","DOI":"10.1109\/CVPR.2016.361"},{"key":"31_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1007\/978-3-642-21227-7_49","volume-title":"Image Analysis","author":"C Olsson","year":"2011","unstructured":"Olsson, C., Enqvist, O.: Stable structure from motion for unordered image collections. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 524\u2013535. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-21227-7_49"},{"issue":"6","key":"31_CR30","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1109\/TPAMI.2010.23","volume":"32","author":"KE Ozden","year":"2010","unstructured":"Ozden, K.E., Schindler, K., Van Gool, L.: Multibody structure-from-motion in practice. IEEE Trans. Pattern Anal. Mach. Intell. 32(6), 1134\u20131141 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"31_CR31","unstructured":"Pachauri, D., Kondor, R., Singh, V.: Solving the multi-way matching problem by permutation synchronization. In: Advances in Neural Information Processing Systems 26, pp. 1860\u20131868. Curran Associates, Inc. (2013)"},{"issue":"14","key":"31_CR32","doi-asserted-by":"publisher","first-page":"1870","DOI":"10.14778\/2556549.2556569","volume":"6","author":"A Pavan","year":"2013","unstructured":"Pavan, A., Tangwongsan, K., Tirthapura, S., Wu, K.L.: Counting and sampling triangles from a graph stream. Proc. VLDB Endowment 6(14), 1870\u20131881 (2013)","journal-title":"Proc. VLDB Endowment"},{"issue":"10","key":"31_CR33","doi-asserted-by":"publisher","first-page":"1832","DOI":"10.1109\/TPAMI.2009.191","volume":"32","author":"S Rao","year":"2010","unstructured":"Rao, S., Tron, R., Vidal, R., Ma, Y.: Motion segmentation in the presence of outlying, incomplete, or corrupted trajectories. Pattern Anal. Mach. Intell. 32(10), 1832\u20131845 (2010)","journal-title":"Pattern Anal. Mach. Intell."},{"key":"31_CR34","doi-asserted-by":"crossref","unstructured":"Rubino, C., Del Bue, A., Chin, T.J.: Practical motion segmentation for urban street view scenes. In: Proceedings of the IEEE International Conference on Robotics and Automation (2018)","DOI":"10.1109\/ICRA.2018.8460993"},{"key":"31_CR35","doi-asserted-by":"crossref","unstructured":"Sabzevari, R., Scaramuzza, D.: Monocular simultaneous multi-body motion segmentation and reconstruction from perspective views. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 23\u201330 (2014)","DOI":"10.1109\/ICRA.2014.6906585"},{"key":"31_CR36","doi-asserted-by":"crossref","unstructured":"Saputra, M.R.U., Markham, A., Trigoni, N.: Visual SLAM and structure from motion in dynamic environments: a survey. ACM Comput. Surveys 51(2), 37:1\u201337:36 (2018)","DOI":"10.1145\/3177853"},{"issue":"2","key":"31_CR37","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/s11263-007-0111-7","volume":"79","author":"K Schindler","year":"2008","unstructured":"Schindler, K., Suter, D., Wang, H.: A model-selection framework for multibody structure-and-motion of image sequences. Int. J. Comput. Vis. 79(2), 159\u2013177 (2008)","journal-title":"Int. J. Comput. Vis."},{"key":"31_CR38","unstructured":"Shen, Y., Huang, Q., Srebro, N., Sanghavi, S.: Normalized spectral map synchronization. In: Advances in Neural Information Processing Systems 29, pp. 4925\u20134933. Curran Associates, Inc. (2016)"},{"key":"31_CR39","doi-asserted-by":"crossref","unstructured":"Toldo, R., Fusiello, A.: Robust multiple structures estimation with J-Linkage. In: Proceedings of the European Conference on Computer Vision, pp. 537\u2013547 (2008)","DOI":"10.1007\/978-3-540-88682-2_41"},{"key":"31_CR40","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1007\/BFb0055687","volume-title":"Computer Vision \u2014 ECCV\u201998","author":"PHS Torr","year":"1998","unstructured":"Torr, P.H.S., Zisserman, A.: Concerning Bayesian motion segmentation, model averaging, matching and the trifocal tensor. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 511\u2013527. Springer, Heidelberg (1998). https:\/\/doi.org\/10.1007\/BFb0055687"},{"key":"31_CR41","unstructured":"Torr, P.H.S., Zisserman, A., Murray, D.W.: Motion clustering using the trilinear constraint over three views. In: Europe-China Workshop on Geometric Modelling and Invariants for Computer Vision, pp. 118\u2013125. Springer (1995)"},{"key":"31_CR42","doi-asserted-by":"crossref","unstructured":"Tron, R., Vidal, R.: A benchmark for the comparison of 3-D motion segmentation algorithms. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20138. IEEE (2007)","DOI":"10.1109\/CVPR.2007.382974"},{"key":"31_CR43","doi-asserted-by":"crossref","unstructured":"Tron, R., Zhou, X., Esteves, C., Daniilidis, K.: Fast multi-image matching via density-based clustering. In: Proceedings of the International Conference on Computer Vision, pp. 4077\u20134086 (2017)","DOI":"10.1109\/ICCV.2017.437"},{"issue":"12","key":"31_CR44","doi-asserted-by":"publisher","first-page":"1945","DOI":"10.1109\/TPAMI.2005.244","volume":"27","author":"R Vidal","year":"2005","unstructured":"Vidal, R., Ma, Y., Sastry, S.: Generalized principal component analysis (GPCA). IEEE Trans. Pattern Anal. Mach. Intell. 27(12), 1945\u20131959 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"31_CR45","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","volume":"17","author":"U Von Luxburg","year":"2007","unstructured":"Von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395\u2013416 (2007)","journal-title":"Stat. Comput."},{"issue":"1","key":"31_CR46","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1109\/LSP.2017.2773636","volume":"25","author":"Y Wang","year":"2018","unstructured":"Wang, Y., Liu, Y., Blasch, E., Ling, H.: Simultaneous trajectory association and clustering for motion segmentation. IEEE Signal Process. Lett. 25(1), 145\u2013149 (2018)","journal-title":"IEEE Signal Process. Lett."},{"key":"31_CR47","doi-asserted-by":"crossref","unstructured":"Xu, X., Cheong, L.F., Li, Z.: Motion segmentation by exploiting complementary geometric models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2859\u20132867 (2018)","DOI":"10.1109\/CVPR.2018.00302"},{"key":"31_CR48","doi-asserted-by":"crossref","unstructured":"Yan, J., Pollefeys, M.: A general framework for motion segmentation: independent, articulated, rigid, non-rigid, degenerate and nondegenerate. In: Proceedings of the European Conference on Computer Vision, pp. 94\u2013106 (2006)","DOI":"10.1007\/11744085_8"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58565-5_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:10:14Z","timestamp":1731283814000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58565-5_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585648","9783030585655"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58565-5_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"12 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","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":"The conference was held virtually due to the COVID-19 pandemic.","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)"}}]}}