{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T18:31:56Z","timestamp":1742927516553,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030014483"},{"type":"electronic","value":"9783030014490"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"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":[[2018]]},"DOI":"10.1007\/978-3-030-01449-0_29","type":"book-chapter","created":{"date-parts":[[2018,9,24]],"date-time":"2018-09-24T09:12:43Z","timestamp":1537780363000},"page":"342-353","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Robust Geodesic Skeleton Estimation from Body Single Depth"],"prefix":"10.1007","author":[{"given":"Jaehwan","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Howon","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,9,25]]},"reference":[{"doi-asserted-by":"publisher","unstructured":"Shotton, J., et al.: Real-time human pose recognition in parts from a single depth image. In: Cipolla, R., Battiato, S., Farinella, G. (eds.) Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1297\u20131304. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-28661-2_5","key":"29_CR1","DOI":"10.1007\/978-3-642-28661-2_5"},{"key":"29_CR2","doi-asserted-by":"publisher","first-page":"2821","DOI":"10.1109\/TPAMI.2012.241","volume":"35","author":"J Shotton","year":"2013","unstructured":"Shotton, J., et al.: Efficient human pose estimation from single depth images. IEEE Trans. Pattern Anal. Mach. Intell. 35, 2821\u20132840 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"29_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. J. Mach. Learn. 45, 5\u201332 (2001)","journal-title":"J. Mach. Learn."},{"doi-asserted-by":"publisher","unstructured":"Gall, J., Lempitsky, V.: Class-specific hough forests for object detection. In: Criminisi, A., Shotton, J. (eds.) Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1022\u20131029. Springer, London (2009). https:\/\/doi.org\/10.1007\/978-1-4471-4929-3_11","key":"29_CR4","DOI":"10.1007\/978-1-4471-4929-3_11"},{"doi-asserted-by":"crossref","unstructured":"Tan, D.J., Ilic, S.: Multi-forest tracker: a chameleon in tracking. In: Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1202\u20131209 (2014)","key":"29_CR5","DOI":"10.1109\/CVPR.2014.157"},{"doi-asserted-by":"crossref","unstructured":"Dapogny, A., Bailly, K., Dubuisson, S.: Pairwise conditional random forests for facial expression recognition. In: Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 3783\u20133791 (2015)","key":"29_CR6","DOI":"10.1109\/ICCV.2015.431"},{"doi-asserted-by":"crossref","unstructured":"Girshick, R., Shotton, J., Kohli, P., Criminisi, A., Fitzgibbon, A.: Efficient regression of general-activity human poses from depth images. In: Proceedings of International Conference on Computer Vision, pp. 415\u2013422 (2011)","key":"29_CR7","DOI":"10.1109\/ICCV.2011.6126270"},{"doi-asserted-by":"crossref","unstructured":"Schwarz, L., Mkhitaryan, A., Mateus, D., Navab, N.: Estimating human 3d pose from time-of-flight images based on geodesic distances and optical flow. In: Proceedings of International Conference on Automatic Face and Gesture Recognition, Santa Barbara, CA, pp. 700\u2013706 (2011)","key":"29_CR8","DOI":"10.1109\/FG.2011.5771333"},{"doi-asserted-by":"crossref","unstructured":"Baak, A., M\u00fcller, M., Bharaj, G., Seidel, H., Theobalt, C.: A data-driven approach for real-time full body pose reconstruction from a depth camera. In: Proceedings of International Conference on Computer Vision, pp. 1092\u20131099 (2011)","key":"29_CR9","DOI":"10.1109\/ICCV.2011.6126356"},{"doi-asserted-by":"crossref","unstructured":"Kontschieder, P., Kohli, P., Shotton, J., Criminisi, A.: GeoF: geodesic forests for learning coupled predictors. In: Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 65\u201372 (2013)","key":"29_CR10","DOI":"10.1109\/CVPR.2013.16"},{"doi-asserted-by":"crossref","unstructured":"Glocker, B., Pauly, O., Konukoglu, E., Criminisi, A.: Joint classification-regression forests for spatially structured multi-object segmentation. In: Proceedings of European Conference on Computer Vision, Florence, Italy, pp. 870\u2013881 (2012)","key":"29_CR11","DOI":"10.1007\/978-3-642-33765-9_62"},{"doi-asserted-by":"crossref","unstructured":"Plagemann, C., Ganapathi, V., Koller, D., Thrun, S.: Real-time identification and localization of body parts from depth images. In: Proceedings of International Conference on Robotics and Automation, pp. 3108\u20133113 (2010)","key":"29_CR12","DOI":"10.1109\/ROBOT.2010.5509559"},{"unstructured":"Salvador, S., Chan, P.: FastDTW: toward accurate dynamic time warping in linear time and space. In: KDD Workshop on Mining Temporal and Sequential Data, pp. 70\u201380 (2004)","key":"29_CR13"},{"key":"29_CR14","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1002\/nav.3800020109","volume":"2","author":"H Kuhn","year":"1955","unstructured":"Kuhn, H.: The hungarian method for the assignment problem. Nav. Res. Logist. Q. 2, 83\u201397 (1955)","journal-title":"Nav. Res. Logist. Q."}],"container-title":["Lecture Notes in Computer Science","Advanced Concepts for Intelligent Vision Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01449-0_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T21:32:30Z","timestamp":1700947950000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-01449-0_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030014483","9783030014490"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01449-0_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"25 September 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACIVS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Concepts for Intelligent Vision Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poitiers","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"24 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acivs2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/acivs.org\/acivs2018\/","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":"self-made system","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"91","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":"52","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":"57% - 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":"1,8","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":"2,2","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)"}}]}}