{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T21:22:07Z","timestamp":1742937727664,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030872014"},{"type":"electronic","value":"9783030872021"}],"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\/978-3-030-87202-1_27","type":"book-chapter","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T19:03:23Z","timestamp":1632337403000},"page":"279-289","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Real-Time Rotated Convolutional Descriptor for Surgical Environments"],"prefix":"10.1007","author":[{"given":"Adam","family":"Schmidt","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Septimiu E.","family":"Salcudean","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"27_CR1","unstructured":"Balntas, V., Johns, E., Tang, L., Mikolajczyk, K.: PN-Net: Conjoined Triple Deep Network for Learning Local Image Descriptors. ArXiv160105030 Cs (2016)"},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"Balntas, V., Riba, E., Ponsa, D., Mikolajczyk, K.: Learning local feature descriptors with triplets and shallow convolutional neural networks. In: British Machine Vision Conference 2016 (2016)","DOI":"10.5244\/C.30.119"},{"key":"27_CR3","doi-asserted-by":"crossref","unstructured":"Barath, D., Noskova, J., Ivashechkin, M., Matas, J.: MAGSAC++, a fast, reliable and accurate robust estimator. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020","DOI":"10.1109\/CVPR42600.2020.00138"},{"key":"27_CR4","unstructured":"Bengio, Y., L\u00e9onard, N., Courville, A.: Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation. ArXiv13083432 Cs, Auguest 2013"},{"issue":"6","key":"27_CR5","doi-asserted-by":"publisher","first-page":"1580","DOI":"10.1007\/s11263-019-01280-3","volume":"128","author":"JW Bian","year":"2019","unstructured":"Bian, J.W., et al.: GMS: grid-based motion statistics for fast, ultra-robust feature correspondence. Int. J. Comput. Vis. 128(6), 1580\u20131593 (2019). https:\/\/doi.org\/10.1007\/s11263-019-01280-3","journal-title":"Int. J. Comput. Vis."},{"key":"27_CR6","unstructured":"Christiansen, P.H., Kragh, M.F., Brodskiy, Y., Karstoft, H.: UnsuperPoint: End-to-end Unsupervised Interest Point Detector and Descriptor. ArXiv190704011 Cs, July 2019"},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Dai, J., et al.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.89"},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"DeTone, D., Malisiewicz, T., Rabinovich, A.: SuperPoint: self-supervised interest point detection and description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2018)","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"27_CR9","doi-asserted-by":"publisher","first-page":"101867","DOI":"10.1109\/ACCESS.2020.2994440","volume":"8","author":"H Gong","year":"2020","unstructured":"Gong, H., Chen, L., Li, C., Zeng, J., Tao, X., Wang, Y.: Online tracking and relocation based on a new rotation-invariant haar-like statistical descriptor in endoscopic examination. IEEE Access 8, 101867\u2013101883 (2020)","journal-title":"IEEE Access"},{"key":"27_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1007\/978-3-319-66179-7_38","volume-title":"Medical Image Computing and Computer Assisted Intervention-MICCAI 2017","author":"MP Heinrich","year":"2017","unstructured":"Heinrich, M.P., Oktay, O.: BRIEFnet: deep pancreas segmentation using binary sparse convolutions. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 329\u2013337. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_38"},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Heinrich, M.P., Oktay, O., Bouteldja, N.: OBELISK-Net: fewer layers to solve 3D multi-organ segmentation with sparse deformable convolutions. In: Medical Image Analysis, vol. 54, May 2019","DOI":"10.1016\/j.media.2019.02.006"},{"key":"27_CR12","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial Transformer Networks. ArXiv150602025 Cs, February 2016"},{"key":"27_CR13","doi-asserted-by":"publisher","unstructured":"Jin, Y., et al.: Image matching across wide baselines: from paper to practice. Int. J. Comput. Vis. 6, 1\u201331 (2020). https:\/\/doi.org\/10.1007\/s11263-020-01385-0","DOI":"10.1007\/s11263-020-01385-0"},{"key":"27_CR14","doi-asserted-by":"crossref","unstructured":"Kalal, Z., Mikolajczyk, K., Matas, J.: Forward-backward error: automatic detection of tracking failures. In: 2010 20th International Conference on Pattern Recognition (ICPR), Auguest 2010","DOI":"10.1109\/ICPR.2010.675"},{"issue":"1","key":"27_CR15","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1109\/TRO.2020.3020739","volume":"37","author":"J Lamarca","year":"2021","unstructured":"Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.M.M.: DefSLAM: tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291\u2013303 (2021)","journal-title":"IEEE Trans. Robot."},{"key":"27_CR16","doi-asserted-by":"crossref","unstructured":"Levi, G., Hassner, T.: LATCH: learned arrangements of three patch codes. In: 2016 IEEE Winter Conference on Applications of Computer Vision (2016)","DOI":"10.1109\/WACV.2016.7477723"},{"issue":"2","key":"27_CR17","doi-asserted-by":"publisher","first-page":"2294","DOI":"10.1109\/LRA.2020.2970659","volume":"5","author":"Y Li","year":"2020","unstructured":"Li, Y., et al.: SuPer: a surgical perception framework for endoscopic tissue manipulation with surgical robotics. IEEE Robot. Autom. Lett. 5(2), 2294\u20132301 (2020)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Liu, X., et al.: Extremely dense point correspondences using a learned feature descriptor. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00490"},{"key":"27_CR19","doi-asserted-by":"crossref","unstructured":"Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150\u20131157, September 1999","DOI":"10.1109\/ICCV.1999.790410"},{"key":"27_CR20","doi-asserted-by":"crossref","unstructured":"Lu, J., Jayakumari, A., Richter, F., Li, Y., Yip, M.C.: SuPer Deep: A Surgical Perception Framework for Robotic Tissue Manipulation using Deep Learning for Feature Extraction. ArXiv200303472 Cs, September 2020","DOI":"10.1109\/ICRA48506.2021.9561249"},{"issue":"2","key":"27_CR21","doi-asserted-by":"publisher","first-page":"918","DOI":"10.1109\/LRA.2019.2892199","volume":"4","author":"A Marmol","year":"2019","unstructured":"Marmol, A., Banach, A., Peynot, T.: Dense-ArthroSLAM: dense intra-articular 3-D reconstruction with robust localization prior for arthroscopy. IEEE Robot. Autom. Lett. 4(2), 918\u2013925 (2019)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"27_CR22","unstructured":"Mishchuk, A., Mishkin, D., Radenovic, F., Matas, J.: Working hard to know your neighbor\u2019s margins: Local descriptor learning loss. ArXiv170510872 Cs, January 2018"},{"issue":"3","key":"27_CR23","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.media.2010.12.002","volume":"15","author":"R Richa","year":"2011","unstructured":"Richa, R., B\u00f3, A.P., Poignet, P.: Towards robust 3D visual tracking for motion compensation in beating heart surgery. Med. Image Anal. 15(3), 302\u2013315 (2011)","journal-title":"Med. Image Anal."},{"key":"27_CR24","unstructured":"Rodr\u00edguez, J.J.G., Lamarca, J., Morlana, J., Tard\u00f3s, J.D., Montiel, J.M.M.: SD-DefSLAM: Semi-Direct Monocular SLAM for Deformable and Intracorporeal Scenes. ArXiv201009409 Cs, October 2020"},{"key":"27_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","DOI":"10.1007\/11744023","volume-title":"Computer Vision \u2013 ECCV 2006","year":"2006","unstructured":"Leonardis, A., Bischof, H., Pinz, A. (eds.): ECCV 2006. LNCS, vol. 3951. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11744023"},{"key":"27_CR26","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: 2011 International Conference on Computer Vision (2011)","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"27_CR27","doi-asserted-by":"crossref","unstructured":"Sarlin, P.E., DeTone, D., Malisiewicz, T., Rabinovich, A.: SuperGlue: learning feature matching with graph neural networks. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020","DOI":"10.1109\/CVPR42600.2020.00499"},{"issue":"4","key":"27_CR28","doi-asserted-by":"publisher","first-page":"4068","DOI":"10.1109\/LRA.2018.2856519","volume":"3","author":"J Song","year":"2018","unstructured":"Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: real-time large-scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. Autom. Lett. 3(4), 4068\u20134075 (2018)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"27_CR29","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/j.patrec.2020.04.005","volume":"133","author":"I Su\u00e1rez","year":"2020","unstructured":"Su\u00e1rez, I., Sfeir, G., Buenaposada, J.M., Baumela, L.: BEBLID: boosted efficient binary local image descriptor. Pattern Recogn. Lett. 133, 366\u2013372 (2020)","journal-title":"Pattern Recogn. Lett."},{"key":"27_CR30","unstructured":"Tyszkiewicz, M.J., Fua, P., Trulls, E.: DISK: learning local features with policy gradient. ArXiv200613566 Cs, June 2020"},{"key":"27_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58577-8","volume-title":"Computer Vision \u2013 ECCV 2020","year":"2020","unstructured":"Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.): ECCV 2020. LNCS, vol. 12375. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58577-8"},{"key":"27_CR32","doi-asserted-by":"crossref","unstructured":"Xompero, A., Lanz, O., Cavallaro, A.: MORB: a multi-scale binary descriptor. In: 2018 25th IEEE International Conference on Image Processing (2018)","DOI":"10.1109\/ICIP.2018.8451024"},{"issue":"3","key":"27_CR33","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1109\/TCSVT.2019.2896095","volume":"30","author":"J Ye","year":"2020","unstructured":"Ye, J., Zhang, S., Huang, T., Rui, Y.: CDbin: compact discriminative binary descriptor learned with efficient neural network. IEEE Trans. Circ. Syst. Video Technol. 30(3), 862\u2013874 (2020)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"27_CR34","doi-asserted-by":"crossref","unstructured":"Ye, M., Johns, E., Handa, A., Zhang, L., Pratt, P., Yang, G.Z.: Self-Supervised Siamese Learning on Stereo Image Pairs for Depth Estimation in Robotic Surgery. ArXiv170508260 Cs, May 2017","DOI":"10.31256\/HSMR2017.14"},{"key":"27_CR35","doi-asserted-by":"publisher","unstructured":"Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.): ECCV 2016. LNCS, vol. 9909. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46454-1","DOI":"10.1007\/978-3-319-46454-1"},{"issue":"11","key":"27_CR36","doi-asserted-by":"publisher","first-page":"2169","DOI":"10.1109\/TMI.2012.2212718","volume":"31","author":"MC Yip","year":"2012","unstructured":"Yip, M.C., Lowe, D.G., Salcudean, S.E., Rohling, R.N., Nguan, C.Y.: Tissue tracking and registration for image-guided surgery. IEEE Trans. Med. Imaging 31(11), 2169\u20132182 (2012)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87202-1_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T23:13:01Z","timestamp":1673305981000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87202-1_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872014","9783030872021"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87202-1_27","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":"21 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 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":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/en\/","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 CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1622","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":"531","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":"33% - 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)"}},{"value":"The conference was held virtually.","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)"}}]}}