{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T15:29:27Z","timestamp":1726068567876},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030414030"},{"type":"electronic","value":"9783030414047"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-41404-7_25","type":"book-chapter","created":{"date-parts":[[2020,2,22]],"date-time":"2020-02-22T07:02:58Z","timestamp":1582354978000},"page":"355-365","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Confidence Map Based 3D Cost Aggregation with Multiple Minimum Spanning Trees for Stereo Matching"],"prefix":"10.1007","author":[{"given":"Yuhao","family":"Xiao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dingding","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guijin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaowei","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongbing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyang","family":"Ji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,2,23]]},"reference":[{"key":"25_CR1","unstructured":"Scharstein, D., Szeliski, R.H.H.: Middlebury stereo evaluation. Version 3. \nhttp:\/\/vision.middlebury.edu\/stereo\/eval3\/"},{"key":"25_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1007\/978-3-319-57240-6_14","volume-title":"Mathematical Morphology and Its Applications to Signal and Image Processing","author":"S Drouyer","year":"2017","unstructured":"Drouyer, S., Beucher, S., Bilodeau, M., Moreaud, M., Sorbier, L.: Sparse stereo disparity map densification using hierarchical image segmentation. In: Angulo, J., Velasco-Forero, S., Meyer, F. (eds.) ISMM 2017. LNCS, vol. 10225, pp. 172\u2013184. Springer, Cham (2017). \nhttps:\/\/doi.org\/10.1007\/978-3-319-57240-6_14"},{"issue":"2","key":"25_CR3","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1023\/B:VISI.0000022288.19776.77","volume":"59","author":"PF Felzenszwalb","year":"2004","unstructured":"Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167\u2013181 (2004)","journal-title":"Int. J. Comput. Vis."},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Kim, K.R., Kim, C.S.: Adaptive smoothness constraints for efficient stereo matching using texture and edge information. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3429\u20133433. IEEE (2016)","DOI":"10.1109\/ICIP.2016.7532996"},{"issue":"12","key":"25_CR5","doi-asserted-by":"publisher","first-page":"3411","DOI":"10.1364\/AO.56.003411","volume":"56","author":"L Li","year":"2017","unstructured":"Li, L., Yu, X., Zhang, S., Zhao, X., Zhang, L.: 3D cost aggregation with multiple minimum spanning trees for stereo matching. Appl. Opt. 56(12), 3411\u20133420 (2017)","journal-title":"Appl. Opt."},{"issue":"3","key":"25_CR6","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1109\/TCSVT.2016.2628782","volume":"28","author":"L Li","year":"2016","unstructured":"Li, L., Zhang, S., Yu, X., Zhang, L.: PMSC: PatchMatch-based superpixel cut for accurate stereo matching. IEEE Trans. Circuits Syst. Video Technol. 28(3), 679\u2013692 (2016)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"25_CR7","doi-asserted-by":"publisher","unstructured":"Mao, W., Wang, M., Zhou, J., Gong, M.: Semi-dense stereo matching using dual CNNs. In: IEEE Winter Conference on Applications of Computer Vision, pp. 1588\u20131597 (2019). \nhttps:\/\/doi.org\/10.1109\/WACV.2019.00174","DOI":"10.1109\/WACV.2019.00174"},{"issue":"6","key":"25_CR8","doi-asserted-by":"publisher","first-page":"2936","DOI":"10.1109\/TIP.2019.2892668","volume":"28","author":"MG Mozerov","year":"2019","unstructured":"Mozerov, M.G., van de Weijer, J.: One-view occlusion detection for stereo matching with a fully connected CRF model. IEEE Trans. Image Process. 28(6), 2936\u20132947 (2019)","journal-title":"IEEE Trans. Image Process."},{"issue":"12","key":"25_CR9","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/LSP.2016.2637355","volume":"24","author":"H Park","year":"2016","unstructured":"Park, H., Lee, K.M.: Look wider to match image patches with convolutional neural networks. IEEE Signal Process. Lett. 24(12), 1788\u20131792 (2016)","journal-title":"IEEE Signal Process. Lett."},{"key":"25_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/978-3-319-11752-2_3","volume-title":"Pattern Recognition","author":"D Scharstein","year":"2014","unstructured":"Scharstein, D., et al.: High-resolution stereo datasets with subpixel-accurate ground truth. In: Jiang, X., Hornegger, J., Koch, R. (eds.) GCPR 2014. LNCS, vol. 8753, pp. 31\u201342. Springer, Cham (2014). \nhttps:\/\/doi.org\/10.1007\/978-3-319-11752-2_3"},{"issue":"2","key":"25_CR11","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1587\/transinf.E95.D.699","volume":"95","author":"C Shi","year":"2012","unstructured":"Shi, C., Wang, G., Pei, X., He, B., Lin, X.: Stereo matching using local plane fitting in confidence-based support window. IEICE Trans. Inf. Syst. 95(2), 699\u2013702 (2012)","journal-title":"IEICE Trans. Inf. Syst."},{"issue":"4","key":"25_CR12","doi-asserted-by":"publisher","first-page":"1412","DOI":"10.1109\/TIP.2015.2393054","volume":"24","author":"C Shi","year":"2015","unstructured":"Shi, C., Wang, G., Yin, X., Pei, X., He, B., Lin, X.: High-accuracy stereo matching based on adaptive ground control points. IEEE Trans. Image Process. 24(4), 1412\u20131423 (2015)","journal-title":"IEEE Trans. Image Process."},{"issue":"11","key":"25_CR13","doi-asserted-by":"publisher","first-page":"2725","DOI":"10.1109\/TPAMI.2017.2766072","volume":"40","author":"T Taniai","year":"2018","unstructured":"Taniai, T., Matsushita, Y., Sato, Y., Naemura, T.: Continuous 3D label stereo matching using local expansion moves. IEEE Trans. Pattern Anal. Mach. Intell. 40(11), 2725\u20132739 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR14","doi-asserted-by":"publisher","first-page":"18745","DOI":"10.1109\/ACCESS.2017.2754318","volume":"5","author":"X Ye","year":"2017","unstructured":"Ye, X., Li, J., Wang, H., Huang, H., Zhang, X.: Efficient stereo matching leveraging deep local and context information. IEEE Access 5, 18745\u201318755 (2017)","journal-title":"IEEE Access"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Zbontar, J., LeCun, Y.: Computing the stereo matching cost with a convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1592\u20131599 (2015)","DOI":"10.1109\/CVPR.2015.7298767"},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, C., Li, Z., Cheng, Y., Cai, R., Chao, H., Rui, Y.: MeshStereo: a global stereo model with mesh alignment regularization for view interpolation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2057\u20132065 (2015)","DOI":"10.1109\/ICCV.2015.238"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-41404-7_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,2,22]],"date-time":"2020-02-22T07:09:12Z","timestamp":1582355352000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-41404-7_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030414030","9783030414047"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-41404-7_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"23 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Auckland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acpr2019a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.acpr2019.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":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"214","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":"125","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":"58% - 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":"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)"}},{"value":"for ACPR 2019 Workshops volume accepted 17 full papers and 6 short papers","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)"}}]}}