{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:28:02Z","timestamp":1743100082042,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030880804"},{"type":"electronic","value":"9783030880811"}],"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\/978-3-030-88081-1_40","type":"book-chapter","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T11:07:08Z","timestamp":1632913628000},"page":"536-548","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning Models for Architectural Fa\u00e7ade Detection in Spherical Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2670-3359","authenticated-orcid":false,"given":"Marcin","family":"Kutrzy\u0144ski","sequence":"first","affiliation":[]},{"given":"Bartosz","family":"\u017bak","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2539-9452","authenticated-orcid":false,"given":"Zbigniew","family":"Telec","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2956-6388","authenticated-orcid":false,"given":"Bogdan","family":"Trawi\u0144ski","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"40_CR1","unstructured":"Caffe2 model zoo. https:\/\/caffe2.ai\/docs\/zoo.html. Accessed 6 Aug 2021"},{"key":"40_CR2","unstructured":"Pyprt - python bindings for cityengine sdk. https:\/\/github.com\/Esri\/pyprt. Accessed 6 Aug 2021"},{"key":"40_CR3","unstructured":"scikit-posthocs. https:\/\/scikit-posthocs.readthedocs.io\/. Accessed 6 Aug 2021"},{"key":"40_CR4","unstructured":"Vgg image annotator (via). https:\/\/www.robots.ox.ac.uk\/~vgg\/software\/via\/via.html. Accessed 6 Aug 2021"},{"key":"40_CR5","unstructured":"py360convert. https:\/\/github.com\/sunset1995\/py360convert (2020). [Online; Accessed 15 Jan 2021"},{"key":"40_CR6","doi-asserted-by":"crossref","unstructured":"Bolya, D., Zhou, C., Xiao, F., Lee, Y.J.: YOLACT: real-time instance segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9157\u20139166 (2019)","DOI":"10.1109\/ICCV.2019.00925"},{"key":"40_CR7","doi-asserted-by":"crossref","unstructured":"Chen, X., Girshick, R., He, K., Doll\u00e1r, P.: TensorMask: a foundation for dense object segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2061\u20132069 (2019)","DOI":"10.1109\/ICCV.2019.00215"},{"key":"40_CR8","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast r-cnn. CoRR abs\/1504.08083 (2015). http:\/\/www.cv-foundation.org\/openaccess\/content_iccv_2015\/papers\/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf","DOI":"10.1109\/ICCV.2015.169"},{"key":"40_CR9","doi-asserted-by":"publisher","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980\u20132988 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.322","DOI":"10.1109\/ICCV.2017.322"},{"key":"40_CR10","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"40_CR11","doi-asserted-by":"publisher","unstructured":"Hern\u00e1ndez, J., Marcotegui, B.: Morphological segmentation of building fa\u00e7ade images. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 4029\u20134032 (2009). https:\/\/doi.org\/10.1109\/ICIP.2009.5413756","DOI":"10.1109\/ICIP.2009.5413756"},{"key":"40_CR12","doi-asserted-by":"crossref","unstructured":"Huang, Z., Huang, L., Gong, Y., Huang, C., Wang, X.: Mask scoring R-CNN. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6409\u20136418 (2019)","DOI":"10.1109\/CVPR.2019.00657"},{"key":"40_CR13","doi-asserted-by":"publisher","unstructured":"Kutrzy\u0144ski, M., \u017bak, B., Telec, Z., Trawi\u0144ski, B.: An approach to automatic detection of architectural fa\u00e7ades in spherical images. In: Intelligent Information and Database Systems: 13th Asian Conference, ACIIDS 2021, Phuket, Thailand, 7\u201310 April 2021, Proceedings 13, pp. 494\u2013504. Springer (2021). https:\/\/doi.org\/10.1007\/978-3-030-73280-6_39","DOI":"10.1007\/978-3-030-73280-6_39"},{"key":"40_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"TY Lin","year":"2014","unstructured":"Lin, T.Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"40_CR15","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(Oct), 2825\u20132830 (2011)"},{"key":"40_CR16","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)"},{"issue":"6","key":"40_CR17","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"40_CR18","doi-asserted-by":"publisher","first-page":"9","DOI":"10.9733\/jgg.061213.2","volume":"2","author":"E S\u00fcmer","year":"2013","unstructured":"S\u00fcmer, E., T\u00fcrker, M.: An automatic region growing based approach to extract facade textures from single ground-level building images. J. Geodesy Geoinf. 2(1), 9\u201317 (2013)","journal-title":"J. Geodesy Geoinf."},{"key":"40_CR19","doi-asserted-by":"publisher","unstructured":"Wendel, A., Donoser, M., Bischof, H.: Unsupervised facade segmentation using repetitive patterns, vol. 6376, pp. 51\u201360 (2010). https:\/\/doi.org\/10.1007\/978-3-642-15986-2_6","DOI":"10.1007\/978-3-642-15986-2_6"}],"container-title":["Lecture Notes in Computer Science","Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-88081-1_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T11:22:57Z","timestamp":1632914577000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-88081-1_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030880804","9783030880811"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88081-1_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rhodos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","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":"iccci2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"231","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":"58","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":"25% - 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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}