{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:05:29Z","timestamp":1743127529430,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031064326"},{"type":"electronic","value":"9783031064333"}],"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\/978-3-031-06433-3_14","type":"book-chapter","created":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T18:03:24Z","timestamp":1652551404000},"page":"158-169","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Exploring the\u00a0Use of\u00a0Efficient Projection Kernels for\u00a0Motion Saliency Estimation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3140-9867","authenticated-orcid":false,"given":"Elena","family":"Nicora","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6482-4768","authenticated-orcid":false,"given":"Nicoletta","family":"Noceti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,15]]},"reference":[{"issue":"2","key":"14_CR1","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/s00138-010-0298-4","volume":"23","author":"MAR Ahad","year":"2012","unstructured":"Ahad, M.A.R., Tan, J.K., Kim, H., Ishikawa, S.: Motion history image: its variants and applications. Mach. Vis. Appl. 23(2), 255\u2013281 (2012)","journal-title":"Mach. Vis. Appl."},{"issue":"3","key":"14_CR2","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1109\/TPAMI.2007.62","volume":"29","author":"G Ben-Artzi","year":"2007","unstructured":"Ben-Artzi, G., Hel-Or, H., Hel-Or, Y.: The gray-code filter kernels. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 382\u2013393 (2007)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"14_CR3","first-page":"147","volume":"4","author":"T Bouwmans","year":"2011","unstructured":"Bouwmans, T.: Recent advanced statistical background modeling for foreground detection-a systematic survey. Recent Pat. Comput. Sci. 4(3), 147\u2013176 (2011)","journal-title":"Recent Pat. Comput. Sci."},{"issue":"10","key":"14_CR4","doi-asserted-by":"publisher","first-page":"2941","DOI":"10.1109\/TCSVT.2018.2870832","volume":"29","author":"R Cong","year":"2018","unstructured":"Cong, R., Lei, J., Fu, H., Cheng, M.M., Lin, W., Huang, Q.: Review of visual saliency detection with comprehensive information. IEEE Trans. Circ. Syst. Video Technol. 29(10), 2941\u20132959 (2018)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Faktor, A., Irani, M.: Video segmentation by non-local consensus voting. In: BMVC. vol. 2, p. 8 (2014)","DOI":"10.5244\/C.28.21"},{"key":"14_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/3-540-45103-X_50","volume-title":"Image Analysis","author":"G Farneb\u00e4ck","year":"2003","unstructured":"Farneb\u00e4ck, G.: Two-frame motion estimation based on polynomial expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363\u2013370. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/3-540-45103-X_50"},{"key":"14_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cviu.2015.02.008","volume":"134","author":"D Fortun","year":"2015","unstructured":"Fortun, D., Bouthemy, P., Kervrann, C.: Optical flow modeling and computation: a survey. Comput. Vis. Image Underst. 134, 1\u201321 (2015)","journal-title":"Comput. Vis. Image Underst."},{"issue":"12","key":"14_CR8","doi-asserted-by":"publisher","first-page":"2247","DOI":"10.1109\/TPAMI.2007.70711","volume":"29","author":"L Gorelick","year":"2007","unstructured":"Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. Trans. Pattern Anal. Mach. Intell. 29(12), 2247\u20132253 (2007)","journal-title":"Trans. Pattern Anal. Mach. Intell."},{"issue":"9","key":"14_CR9","doi-asserted-by":"publisher","first-page":"1430","DOI":"10.1109\/TPAMI.2005.184","volume":"27","author":"Y Hel-Or","year":"2005","unstructured":"Hel-Or, Y., Hel-Or, H.: Real-time pattern matching using projection kernels. IEEE Trans. Pattern Anal. Mach. Intell. 27(9), 1430\u20131445 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"11","key":"14_CR10","doi-asserted-by":"publisher","first-page":"1254","DOI":"10.1109\/34.730558","volume":"20","author":"L Itti","year":"1998","unstructured":"Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254\u20131259 (1998)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Jain, S.D., Xiong, B., Grauman, K.: FusionSeg: learning to combine motion and appearance for fully automatic segmentation of generic objects in videos. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2117\u20132126. IEEE (2017)","DOI":"10.1109\/CVPR.2017.228"},{"issue":"6","key":"14_CR12","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1109\/TPAMI.2015.2477814","volume":"38","author":"S Korman","year":"2015","unstructured":"Korman, S., Avidan, S.: Coherency sensitive hashing. IEEE Trans. Pattern Anal. Mach. Intell. 38(6), 1099\u20131112 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"14_CR13","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1109\/TPAMI.2005.102","volume":"27","author":"DS Lee","year":"2005","unstructured":"Lee, D.S.: Effective gaussian mixture learning for video background subtraction. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 827\u2013832 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"Li, F., Kim, T., Humayun, A., Tsai, D., Rehg, J.M.: Video segmentation by tracking many figure-ground segments. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2192\u20132199 (2013)","DOI":"10.1109\/ICCV.2013.273"},{"issue":"10","key":"14_CR15","doi-asserted-by":"publisher","first-page":"2243","DOI":"10.1109\/TIP.2009.2025559","volume":"18","author":"Y Moshe","year":"2009","unstructured":"Moshe, Y., Hel-Or, H.: Video block motion estimation based on gray-code kernels. IEEE Trans. Image Process. 18(10), 2243\u20132254 (2009)","journal-title":"IEEE Trans. Image Process."},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Moshe, Y., Hel-Or, H., Hel-Or, Y.: Foreground detection using spatiotemporal projection kernels. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3210\u20133217. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6248056"},{"issue":"12","key":"14_CR17","doi-asserted-by":"publisher","first-page":"1198","DOI":"10.1016\/j.cviu.2009.06.006","volume":"113","author":"N Noceti","year":"2009","unstructured":"Noceti, N., Delponte, E., Odone, F.: Spatio-temporal constraints for on-line 3D object recognition in videos. Comput. Vis. Image Underst. 113(12), 1198\u20131209 (2009)","journal-title":"Comput. Vis. Image Underst."},{"key":"14_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1007\/978-3-319-23234-8_62","volume-title":"Image Analysis and Processing \u2014 ICIAP 2015","author":"N Noceti","year":"2015","unstructured":"Noceti, N., Sciutti, A., Sandini, G.: Cognition helps vision: recognizing biological motion using invariant dynamic cues. In: Murino, V., Puppo, E. (eds.) ICIAP 2015. LNCS, vol. 9280, pp. 676\u2013686. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-23234-8_62"},{"issue":"6","key":"14_CR19","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1109\/TPAMI.2013.242","volume":"36","author":"P Ochs","year":"2013","unstructured":"Ochs, P., Malik, J., Brox, T.: Segmentation of moving objects by long term video analysis. IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1187\u20131200 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"14_CR20","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybernet. 9(1), 62\u201366 (1979)","journal-title":"IEEE Trans. Syst. Man Cybernet."},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Ouyang, W., Zhang, R., Cham, W.K.: Fast pattern matching using orthogonal Haar transform. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3050\u20133057. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5540058"},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Papazoglou, A., Ferrari, V.: Fast object segmentation in unconstrained video. In: Proceedings of the IEEE international conference on computer vision. pp. 1777\u20131784 (2013)","DOI":"10.1109\/ICCV.2013.223"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Perazzi, F., Khoreva, A., Benenson, R., Schiele, B., Sorkine-Hornung, A.: Learning video object segmentation from static images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2663\u20132672 (2017)","DOI":"10.1109\/CVPR.2017.372"},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., Sorkine-Hornung, A.: A benchmark dataset and evaluation methodology for video object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 724\u2013732 (2016)","DOI":"10.1109\/CVPR.2016.85"},{"key":"14_CR25","doi-asserted-by":"publisher","first-page":"58","DOI":"10.3389\/frobt.2019.00058","volume":"6","author":"F Rea","year":"2019","unstructured":"Rea, F., Vignolo, A., Sciutti, A., Noceti, N.: Human motion understanding for selecting action timing in collaborative human-robot interaction. Front. Robot. AI 6, 58 (2019)","journal-title":"Front. Robot. AI"},{"issue":"8","key":"14_CR26","doi-asserted-by":"publisher","first-page":"2415","DOI":"10.1109\/TIP.2015.2421435","volume":"24","author":"A Stagliano","year":"2015","unstructured":"Stagliano, A., Noceti, N., Verri, A., Odone, F.: Online space-variant background modeling with sparse coding. IEEE Trans. Image Process. 24(8), 2415\u20132428 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"14_CR27","doi-asserted-by":"crossref","unstructured":"Vignolo, A., Noceti, N., Rea, F., Sciutti, A., Odone, F., Sandini, G.: Detecting biological motion for human-robot interaction: a link between perception and action. Front. Robot. AI p. 14 (2017)","DOI":"10.3389\/frobt.2017.00014"},{"key":"14_CR28","doi-asserted-by":"crossref","unstructured":"Voigtlaender, P., Leibe, B.: Online adaptation of convolutional neural networks for video object segmentation. arXiv preprint arXiv:1706.09364 (2017)","DOI":"10.5244\/C.31.116"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Weinzaepfel, P., Revaud, J., Harchaoui, Z., Schmid, C.: Deepflow: large displacement optical flow with deep matching. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1385\u20131392 (2013)","DOI":"10.1109\/ICCV.2013.175"},{"key":"14_CR30","doi-asserted-by":"crossref","unstructured":"Werlberger, M., Pock, T., Bischof, H.: Motion estimation with non-local total variation regularization. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2464\u20132471. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5539945"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Xiao, F., Jae Lee, Y.: Track and segment: an iterative unsupervised approach for video object proposals. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 933\u2013942 (2016)","DOI":"10.1109\/CVPR.2016.107"},{"key":"14_CR32","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1109\/TIP.2019.2930152","volume":"29","author":"T Zhuo","year":"2019","unstructured":"Zhuo, T., Cheng, Z., Zhang, P., Wong, Y., Kankanhalli, M.: Unsupervised online video object segmentation with motion property understanding. IEEE Trans. Image Process. 29, 237\u2013249 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"14_CR33","doi-asserted-by":"crossref","unstructured":"Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, vol. 2, pp. 28\u201331. IEEE (2004)","DOI":"10.1109\/ICPR.2004.1333992"}],"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\/978-3-031-06433-3_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T12:09:44Z","timestamp":1709813384000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06433-3_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031064326","9783031064333"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06433-3_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 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)"}}]}}