{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:52:49Z","timestamp":1772121169287,"version":"3.50.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031064296","type":"print"},{"value":"9783031064302","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-06430-2_29","type":"book-chapter","created":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T08:03:16Z","timestamp":1652688196000},"page":"347-357","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Connected Components Labeling on\u00a0Bitonal Images"],"prefix":"10.1007","author":[{"given":"Federico","family":"Bolelli","sequence":"first","affiliation":[]},{"given":"Stefano","family":"Allegretti","sequence":"additional","affiliation":[]},{"given":"Costantino","family":"Grana","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,17]]},"reference":[{"key":"29_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/978-3-030-29888-3_4","volume-title":"Computer Analysis of Images and Patterns","author":"S Allegretti","year":"2019","unstructured":"Allegretti, S., Bolelli, F., Cancilla, M., Pollastri, F., Canalini, L., Grana, C.: How does Connected Components Labeling with Decision Trees perform on GPUs? In: Computer Analysis of Images and Patterns. vol. 11678, pp. 39\u201351 (2019)"},{"key":"29_CR2","doi-asserted-by":"publisher","unstructured":"Allegretti, S., Bolelli, F., Grana, C.: Optimized block-based algorithms to label connected components on GPUs. IEEE Trans. Parallel Distrib. Syst., 423\u2013438 (2019). https:\/\/doi.org\/10.1109\/TPDS.2019.2934683","DOI":"10.1109\/TPDS.2019.2934683"},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Allegretti, S., Bolelli, F., Pollastri, F., Longhitano, S., Pellacani, G., Grana, C.: Supporting skin lesion diagnosis with content-based image retrieval. In: 2020 25th International Conference on Pattern Recognition (ICPR), January 2021. IEEE (2021)","DOI":"10.1109\/ICPR48806.2021.9412419"},{"issue":"1","key":"29_CR4","first-page":"1999","volume":"29","author":"F Bolelli","year":"2019","unstructured":"Bolelli, F., Allegretti, S., Baraldi, L., Grana, C.: Spaghetti Labeling: Directed Acyclic Graphs for Block-Based Connected Components Labeling. IEEE Trans. Image Process. 29(1), 1999\u20132012 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Bolelli, F., Baraldi, L., Cancilla, M., Grana, C.: Connected components labeling on DRAGs. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 121\u2013126 (2018)","DOI":"10.1109\/ICPR.2018.8545505"},{"key":"29_CR6","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/978-3-319-73165-0_15","volume-title":"Digital Libraries and Multimedia Archives","author":"F Bolelli","year":"2018","unstructured":"Bolelli, F., Borghi, G., Grana, C.: XDOCS: An Application to Index Historical Documents. In: Digital Libraries and Multimedia Archives. pp. 151\u2013162. Springer (2018)"},{"issue":"2","key":"29_CR7","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/s11554-018-0756-1","volume":"17","author":"F Bolelli","year":"2018","unstructured":"Bolelli, F., Cancilla, M., Baraldi, L., Grana, C.: Towards reliable experiments on the performance of Connected Components Labeling algorithms. J. Real Time Image Proc. 17(2), 229\u2013244 (2018)","journal-title":"J. Real Time Image Proc."},{"key":"29_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/978-3-030-29888-3_8","volume-title":"Computer Analysis of Images and Patterns","author":"L Canalini","year":"2019","unstructured":"Canalini, L., Pollastri, F., Bolelli, F., Cancilla, M., Allegretti, S., Grana, C.: Skin lesion segmentation ensemble with diverse training strategies. In: Computer Analysis of Images and Patterns, pp. 89\u2013101 (2019)"},{"key":"29_CR9","doi-asserted-by":"crossref","unstructured":"Chang, W.Y., Chiu, C.C.: An efficient scan algorithm for block-based connected component labeling. In: 22nd Mediterranean Conference on Control and Automation, pp. 1008\u20131013 (2014)","DOI":"10.1109\/MED.2014.6961506"},{"issue":"9","key":"29_CR10","doi-asserted-by":"publisher","first-page":"23763","DOI":"10.3390\/s150923763","volume":"15","author":"WY Chang","year":"2015","unstructured":"Chang, W.Y., Chiu, C.C., Yang, J.H.: Block-Based Connected-Component Labeling Algorithm Using Binary Decision Trees. Sensors 15(9), 23763\u201323787 (2015)","journal-title":"Sensors"},{"key":"29_CR11","doi-asserted-by":"publisher","first-page":"11500","DOI":"10.1109\/ACCESS.2022.3144840","volume":"10","author":"M Cipriano","year":"2022","unstructured":"Cipriano, Marco: Deep Segmentation of the Mandibular Canal: A New 3D Annotated Dataset of CBCT Volumes. IEEE Access 10, 11500\u201311510 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3144840","journal-title":"IEEE Access"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Cipriano, M., Allegretti, S., Bolelli, F., Pollastri, F., Grana, C.: Improving segmentation of the inferior alveolar nerve through deep label propagation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1\u201310. IEEE (2022)","DOI":"10.1109\/CVPR52688.2022.02046"},{"issue":"2","key":"29_CR13","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1145\/128749.128750","volume":"39","author":"MB Dillencourt","year":"1992","unstructured":"Dillencourt, M.B., Samet, H., Tamminen, M.: A General Approach to Connected-Component Labeling for Arbitrary Image Representations. J. ACM 39(2), 253\u2013280 (1992)","journal-title":"J. ACM"},{"key":"29_CR14","doi-asserted-by":"crossref","unstructured":"Fabbri, M., et al.: MOTSynth: how can synthetic data help pedestrian detection and tracking? In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10849\u201310859 (2021)","DOI":"10.1109\/ICCV48922.2021.01067"},{"key":"29_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1007\/978-3-319-48680-2_38","volume-title":"Advanced Concepts for Intelligent Vision Systems","author":"C Grana","year":"2016","unstructured":"Grana, Costantino, Baraldi, Lorenzo, Bolelli, Federico: Optimized connected components labeling with pixel prediction. In: Blanc-Talon, Jacques, Distante, Cosimo, Philips, Wilfried, Popescu, Dan, Scheunders, Paul (eds.) ACIVS 2016. LNCS, vol. 10016, pp. 431\u2013440. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-48680-2_38"},{"key":"29_CR16","doi-asserted-by":"crossref","unstructured":"Grana, C., Bolelli, F., Baraldi, L., Vezzani, R.: YACCLAB - yet another connected components labeling benchmark. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 3109\u20133114 (2016)","DOI":"10.1109\/ICPR.2016.7900112"},{"issue":"6","key":"29_CR17","doi-asserted-by":"publisher","first-page":"1596","DOI":"10.1109\/TIP.2010.2044963","volume":"19","author":"C Grana","year":"2010","unstructured":"Grana, C., Borghesani, D., Cucchiara, R.: Optimized Block-based Connected Components Labeling with Decision Trees. IEEE Trans. Image Process. 19(6), 1596\u20131609 (2010)","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"29_CR18","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/s11042-010-0561-8","volume":"55","author":"C Grana","year":"2011","unstructured":"Grana, C., Borghesani, D., Cucchiara, R.: Automatic segmentation of digitalized historical manuscripts. Multimedia Tools and Applications 55(3), 483\u2013506 (2011)","journal-title":"Multimedia Tools Appl."},{"issue":"16","key":"29_CR19","doi-asserted-by":"publisher","first-page":"2302","DOI":"10.1016\/j.patrec.2012.08.015","volume":"33","author":"C Grana","year":"2012","unstructured":"Grana, C., Montangero, M., Borghesani, D.: Optimal decision trees for local image processing algorithms. Pattern Recognition Letters 33(16), 2302\u20132310 (2012)","journal-title":"Pattern Recogn. Lett."},{"key":"29_CR20","doi-asserted-by":"crossref","unstructured":"He, L., Chao, Y., Suzuki, K.: A linear-time two-scan labeling algorithm. In: 2007 IEEE International Conference on Image Processing, pp. 241\u2013244 (2007)","DOI":"10.1109\/ICIP.2007.4379810"},{"key":"29_CR21","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.patcog.2017.04.018","volume":"70","author":"L He","year":"2017","unstructured":"He, L., Ren, X., Gao, Q., Zhao, X., Yao, B., Chao, Y.: The connected-component labeling problem: A review of state-of-the-art algorithms. Pattern Recogn. 70, 25\u201343 (2017)","journal-title":"Pattern Recogn."},{"issue":"2","key":"29_CR22","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1109\/TIP.2013.2289968","volume":"23","author":"L He","year":"2014","unstructured":"He, L., Zhao, X., Chao, Y., Suzuki, K.: Configuration-transition-based connected-component labeling. IEEE Trans. Image Process. 23(2), 943\u2013951 (2014)","journal-title":"IEEE Trans. Image Process."},{"key":"29_CR23","doi-asserted-by":"crossref","unstructured":"Hennequin, A., Lacassagne, L., Cabaret, L., Meunier, Q.: A new direct connected component labeling and analysis algorithms for GPUs. In: 2018 Conference on Design and Architectures for Signal and Image Processing (DASIP), pp. 76\u201381. IEEE (2018)","DOI":"10.1109\/DASIP.2018.8596835"},{"issue":"2","key":"29_CR24","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/s11554-009-0134-0","volume":"6","author":"L Lacassagne","year":"2011","unstructured":"Lacassagne, L., Zavidovique, B.: Light speed labeling: efficient connected component labeling on risc architectures. J. Real-Time Image Proc. 6(2), 117\u2013135 (2011)","journal-title":"J. Real-Time Image Proc."},{"key":"29_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1007\/978-3-030-01216-8_34","volume-title":"Computer Vision \u2013 ECCV 2018","author":"IH Laradji","year":"2018","unstructured":"Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the Blobs: Counting by Localization with Point Supervision. In: Computer Vision \u2013 ECCV 2018. pp. 547\u2013562 (2018)"},{"key":"29_CR26","doi-asserted-by":"crossref","unstructured":"Pham, H.V., Bhaduri, B., Tangella, K., Best-Popescu, C., Popescu, G.: Real time blood testing using quantitative phase imaging. PLOS ONE 8(2), e55676 (2013)","DOI":"10.1371\/journal.pone.0055676"},{"key":"29_CR27","doi-asserted-by":"publisher","unstructured":"Playne, D., Hawick, K.: A new algorithm for parallel connected-component labelling on GPUs. IEEE Trans. Parallel Distrib. Syst. 29(6), 1217\u20131230 (2018). https:\/\/doi.org\/10.1109\/TPDS.2018.2799216","DOI":"10.1109\/TPDS.2018.2799216"},{"key":"29_CR28","doi-asserted-by":"crossref","unstructured":"Pollastri, F., Bolelli, F., Paredes, R., Grana, C.: Augmenting data with GANs to segment melanoma skin lesions. Multimedia Tools Appl. 79(21\u201322), 15575\u201315592 (2019)","DOI":"10.1007\/s11042-019-7717-y"},{"key":"29_CR29","unstructured":"Porrello, A., Abati, D., Calderara, S., Cucchiara, R.: Classifying signals on irregular domains via convolutional cluster pooling. In: The 22nd International Conference on Artificial Intelligence and Statistics. pp. 1388\u20131397. PMLR (2019)"},{"key":"29_CR30","doi-asserted-by":"crossref","unstructured":"Rosenfeld, A., Pfaltz, J.L.: Sequential operations in digital picture processing. J. ACM 13(4), 471\u2013494 (1966)","DOI":"10.1145\/321356.321357"},{"key":"29_CR31","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.patcog.2018.10.017","volume":"87","author":"F Uslu","year":"2019","unstructured":"Uslu, F., Bharath, A.A.: A recursive Bayesian approach to describe retinal vasculature geometry. Pattern Recognition 87, 157\u2013169 (2019)","journal-title":"Pattern Recogn."},{"key":"29_CR32","unstructured":"Wu, K., Otoo, E., Suzuki, K.: Two strategies to speed up connected component labeling algorithms. Pattern Anal. Appl. 0(LBNL-59102) (2005)"},{"key":"29_CR33","doi-asserted-by":"crossref","unstructured":"Zavalishin, S., Safonov, I., Bekhtin, Y., Kurilin, I.: Block equivalence algorithm for labeling 2D and 3D images on GPU. Electron. Imaging 2016(2), 1\u20137 (2016). Society for Imaging Science and Technology","DOI":"10.2352\/ISSN.2470-1173.2016.2.VIPC-240"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing \u2013 ICIAP 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06430-2_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T13:49:59Z","timestamp":1710337799000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06430-2_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031064296","9783031064302"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06430-2_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"17 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lecce","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap2021.org\/","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":"Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","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":"168","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":"55% - 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":"4","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)"}}]}}