{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T03:48:20Z","timestamp":1761709700277},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2020,2,15]],"date-time":"2020-02-15T00:00:00Z","timestamp":1581724800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,2,15]],"date-time":"2020-02-15T00:00:00Z","timestamp":1581724800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2020,11]]},"DOI":"10.1007\/s11227-020-03194-1","type":"journal-article","created":{"date-parts":[[2020,2,15]],"date-time":"2020-02-15T12:02:17Z","timestamp":1581768137000},"page":"9010-9030","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Three-dimensional rapid registration and reconstruction of multi-view rigid objects based on end-to-end deep surface model"],"prefix":"10.1007","volume":"76","author":[{"given":"Shengzan","family":"Yan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijun","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shushan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,2,15]]},"reference":[{"key":"3194_CR1","unstructured":"Diebel J, Thrun S (2005) An application of Markov random fields to range sensing. In: Advances in Neural Information Processing Systems, vol 24, no 05, pp 291\u2013298"},{"issue":"5","key":"3194_CR2","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1109\/TPAMI.2009.68","volume":"32","author":"J Zhuand","year":"2010","unstructured":"Zhuand J, Yang R (2010) Spatial\u2013temporal fusion for high accuracy depth maps using dynamic MRFs. IEEE Trans Pattern Anal Mach Intell 32(5):899\u2013909","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3194_CR3","unstructured":"Lu J, Min D, Pahwa RS, Do MN (2011) A review to MRF-based depth map super-resolution and enhancement. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 985\u2013988"},{"issue":"9","key":"3194_CR4","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1016\/j.bjps.2017.06.001","volume":"70","author":"ZM Jessop","year":"2017","unstructured":"Jessop ZM, Al-Sabah A, Gardiner MD, Combellack E, Hawkins K, Whitaker IS (2017) 3D bioprinting for reconstructive surgery: principles, applications and challenges. J Plast Reconstr Aesthet Surg 70(9):1155\u20131170","journal-title":"J Plast Reconstr Aesthet Surg"},{"issue":"2","key":"3194_CR5","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1111\/cgf.13382","volume":"37","author":"M Zollh\u00f6fer","year":"2018","unstructured":"Zollh\u00f6fer M, Thies J, Garrido P, Bradley D, Beeler T, P\u00e9rez P, Stamminger M, Nie\u00dfner M, Theobalt C (2018) State of the art on monocular 3D face reconstruction, tracking, and applications. Comput Graph Forum 37(2):523\u2013550","journal-title":"Comput Graph Forum"},{"key":"3194_CR6","first-page":"67","volume":"2","author":"R Carr","year":"2013","unstructured":"Carr R (2013) Coachella picks RBF for 2,200-Acre La Entrada\u2019s infrastructure. Natl Real Estate Invest Exclus Insight 2:67\u201368","journal-title":"Natl Real Estate Invest Exclus Insight"},{"issue":"1","key":"3194_CR7","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/0304-3991(92)90233-A","volume":"40","author":"P Penczek","year":"1992","unstructured":"Penczek P, Radermacher M, Frank J (1992) Three-dimensional reconstruction of single particles embedded in ice. Ultramicroscopy 40(1):33\u201353","journal-title":"Ultramicroscopy"},{"issue":"1","key":"3194_CR8","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/0022-5193(72)90180-4","volume":"36","author":"P Gilbert","year":"1972","unstructured":"Gilbert P (1972) Iterative methods for the three-dimensional reconstruction of an object from projections. J Theor Biol 36(1):105\u2013117","journal-title":"J Theor Biol"},{"issue":"2\u20133","key":"3194_CR9","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/S0925-7721(01)00017-7","volume":"19","author":"N Amenta","year":"2001","unstructured":"Amenta N, Choi S, Kolluri RK (2001) The power crust, unions of balls, and the medial axis transform. Comput Geom Theory Appl 19(2\u20133):127\u2013153","journal-title":"Comput Geom Theory Appl"},{"issue":"1","key":"3194_CR10","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1109\/TCYB.2015.2399351","volume":"46","author":"P Qian","year":"2015","unstructured":"Qian P, Jiang Y, Deng Z, Hu L, Sun S, Wang S, Muzic RF (2015) Cluster prototypes and fuzzy memberships jointly leveraged cross-domain maximum entropy clustering. IEEE Trans Cybern 46(1):181\u2013193","journal-title":"IEEE Trans Cybern"},{"issue":"5","key":"3194_CR11","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1109\/TNNLS.2015.2511179","volume":"28","author":"P Qian","year":"2016","unstructured":"Qian P, Jiang Y, Wang S, Su KH, Wang J, Hu L, Muzic RF (2016) Affinity and penalty jointly constrained spectral clustering with all-compatibility, flexibility, and robustness. IEEE Trans Neural Netw Learn Syst 28(5):1123\u20131138","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3194_CR12","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.knosys.2017.05.018","volume":"130","author":"P Qian","year":"2017","unstructured":"Qian P, Zhao K, Jiang Y, Su KH, Deng Z, Wang S, Muzic RF (2017) Knowledge-leveraged transfer fuzzy C-means for texture image segmentation with self-adaptive cluster prototype matching. Knowl Based Syst 130:33\u201350","journal-title":"Knowl Based Syst"},{"key":"3194_CR13","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.ins.2017.08.093","volume":"422","author":"P Qian","year":"2018","unstructured":"Qian P, Xi C, Xu M, Jiang Y, Su KH, Wang S, Muzic RF (2018) SSC-EKE: semi-supervised classification with extensive knowledge exploitation. Inf Sci 422:51\u201376","journal-title":"Inf Sci"},{"key":"3194_CR14","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.patcog.2015.08.009","volume":"50","author":"P Qian","year":"2016","unstructured":"Qian P, Sun S, Jiang Y, Su KH, Ni T, Wang S, Muzic RF (2016) Cross-domain, soft-partition clustering with diversity measure and knowledge reference. Pattern Recognit 50:155\u2013177","journal-title":"Pattern Recognit"},{"key":"3194_CR15","doi-asserted-by":"crossref","first-page":"28594","DOI":"10.1109\/ACCESS.2018.2825352","volume":"6","author":"P Qian","year":"2018","unstructured":"Qian P, Zhou J, Jiang Y, Liang F, Zhao K, Wang S, Su KH, Muzic RF (2018) Multi-view maximum entropy clustering by jointly leveraging inter-view collaborations and intra-view-weighted attributes. IEEE Access 6:28594\u201328610","journal-title":"IEEE Access"},{"issue":"3","key":"3194_CR16","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1109\/TSMCB.2011.2172604","volume":"42","author":"P Qian","year":"2012","unstructured":"Qian P, Chung FL, Wang S, Deng Z (2012) Fast graph-based relaxed clustering for large data sets using minimal enclosing ball. IEEE Trans Syst Man Cybern Part B (Cybern) 42(3):672\u2013687","journal-title":"IEEE Trans Syst Man Cybern Part B (Cybern)"},{"issue":"12","key":"3194_CR17","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.1109\/TNSRE.2017.2748388","volume":"25","author":"Y Jiang","year":"2017","unstructured":"Jiang Y, Wu D, Deng Z, Qian P, Wang J, Wang G, Chung FL, Choi KS, Wang S (2017) Seizure classification from EEG signals using transfer learning, semi-supervised learning and TSK fuzzy system. IEEE Trans Neural Syst Rehabil Eng 25(12):2270\u20132284","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"1","key":"3194_CR18","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TFUZZ.2016.2637405","volume":"25","author":"Y Jiang","year":"2017","unstructured":"Jiang Y, Deng Z, Chung FL, Wang G, Qian P, Choi KS, Wang S (2017) Recognition of epileptic EEG signals using a novel multiview TSK fuzzy system. IEEE Trans Fuzzy Syst 25(1):3\u201320","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"4","key":"3194_CR19","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1109\/TCYB.2014.2334595","volume":"45","author":"Y Jiang","year":"2014","unstructured":"Jiang Y, Chung FL, Wang S, Deng Z, Wang J, Qian P (2014) Collaborative fuzzy clustering from multiple weighted views. IEEE Trans Cybern 45(4):688\u2013701","journal-title":"IEEE Trans Cybern"},{"issue":"3","key":"3194_CR20","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1109\/TCYB.2014.2330844","volume":"45","author":"Y Jiang","year":"2015","unstructured":"Jiang Y, Chung FL, Ishibuchi H, Deng Z, Wang S (2015) Multitask TSK fuzzy system modeling by mining intertask common hidden structure. IEEE Trans Cybern 45(3):534\u2013547","journal-title":"IEEE Trans Cybern"},{"key":"3194_CR21","unstructured":"Murphy KP, Weiss Y, Jordan MI (1999) Loopy belief propagation for approximate inference: an empirical study. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, vol 12, no 9. Morgan Kaufmann Publishers Inc., Burlington, pp 467\u2013475"},{"key":"3194_CR22","unstructured":"Park J, Kim H, Tai Y-W, Brown MS, Kweo I (2010) High quality depth map upsampling. In: IEEE International Conference on Computer Vision (ICCV), pp 1623\u20131630"},{"key":"3194_CR23","doi-asserted-by":"crossref","unstructured":"Yang Q, Yang R, Davis J, Nister D (2007) Spatial-depth super resolution for range images. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 1\u20138","DOI":"10.1109\/CVPR.2007.383211"},{"key":"3194_CR24","unstructured":"Chan D, Buisman H et al (2008) A noise-aware filter for real-time depth upsampling. In: Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications, pp 209\u2013219"},{"issue":"8","key":"3194_CR25","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1109\/TPAMI.2009.161","volume":"32","author":"Y Furukawa","year":"2010","unstructured":"Furukawa Y (2010) Accurate, dense, and robust multiview stereopsis. IEEE Trans Pattern Anal Mach Intell 32(8):1362\u20131376","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3194_CR26","doi-asserted-by":"crossref","unstructured":"Dolson J, Baek J, Plagemann C, Thrun S (2010) Upsampling range data in dynamic environments. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1141\u20131148","DOI":"10.1109\/CVPR.2010.5540086"},{"issue":"9","key":"3194_CR27","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1109\/JSEN.2010.2101060","volume":"11","author":"S Foix","year":"2011","unstructured":"Foix S, Alenya G, Torras C (2011) Lock-in time-of-flight (TOF) cameras: a survey. Sens J IEEE 11(9):1917\u20131926","journal-title":"Sens J IEEE"},{"key":"3194_CR28","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/978-3-642-13408-1_20","volume-title":"Field and service robotics","author":"A Harrison","year":"2010","unstructured":"Harrison A, Newman P (2010) Image and sparse laser fusion for dense scene reconstruction. In: Howard A, Iagnemma K, Kelly A (eds) Field and service robotics. Springer, Berlin, pp 219\u2013228"},{"issue":"1","key":"3194_CR29","first-page":"1","volume":"27","author":"N Li","year":"2018","unstructured":"Li N, Gong X, Li H et al (2018) Nonuniform multiview color texture mapping of image sequence and three-dimensional model for faded cultural relics with sift feature points. J Electron Imaging 27(1):1\u201321","journal-title":"J Electron Imaging"},{"issue":"24","key":"3194_CR30","first-page":"1","volume":"24","author":"C Du","year":"2018","unstructured":"Du C, Du C, Huang L et al (2018) Reconstructing perceived images from human brain activities with Bayesian deep multiview learning. IEEE Trans Neural Netw Learn Syst 24(24):1\u201314","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3194_CR31","doi-asserted-by":"crossref","unstructured":"Yao Y, Luo Z, Li S (2018) MVSNet: depth inference for unstructured multi-view stereo. In: European Conference on Computer Vision","DOI":"10.1007\/978-3-030-01237-3_47"},{"key":"3194_CR32","unstructured":"Schoenberg JR, Nathan A, Campbell (2012) Segmentation of dense range information in complex urban scenes. In: 2010 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), vol 21, no 4, pp 2033\u20132038"},{"key":"3194_CR33","unstructured":"Vincent L, Jean S\u00e9bastien F, Edmond B (2018) Shape reconstruction using volume sweeping and learned photo consistency. In: European Conference on Computer Vision"},{"key":"3194_CR34","doi-asserted-by":"crossref","unstructured":"Riegler G, Ulusoy AO, Geiger A (2017) OctNet: learning deep 3D representations at high resolutions. In: IEEE Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR.2017.701"},{"key":"3194_CR35","doi-asserted-by":"crossref","unstructured":"Riegler G, Ulusoy AO, Bischof H (2017) OctNet fusion: learning depth fusion from data. In: International Conference on 3D Vision","DOI":"10.1109\/3DV.2017.00017"},{"key":"3194_CR36","doi-asserted-by":"crossref","unstructured":"Ji M, Gall J, Zheng H (2017) SurfaceNet: an end-to-end 3D neural network for multiview stereopsis. In: IEEE International Conference on Computer Vision","DOI":"10.1109\/ICCV.2017.253"},{"key":"3194_CR37","doi-asserted-by":"crossref","unstructured":"Zou C, Yumer E, Yang J et al (2017) 3D-PRNN: generating shape primitives with recurrent neural networks, vol 23 no 21, pp 993\u20131000","DOI":"10.1109\/ICCV.2017.103"},{"issue":"4","key":"3194_CR38","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1023\/A:1011093014141","volume":"33","author":"E Gringarten","year":"2001","unstructured":"Gringarten E, Deutsch CV (2001) Teacher\u2019s aide variogram inter-pretation and modeling. Math Geol 33(4):507\u2013534","journal-title":"Math Geol"},{"issue":"31","key":"3194_CR39","first-page":"980","volume":"33","author":"H Rebecq","year":"2017","unstructured":"Rebecq H, Gallego G, Mueggler E et al (2017) EMVS: event-based multi-view stereo\u20143D reconstruction with an event camera in real-time. Int J Comput Vis 33(31):980\u2013992","journal-title":"Int J Comput Vis"},{"issue":"7","key":"3194_CR40","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.3390\/s17071689","volume":"17","author":"Z Haopeng","year":"2017","unstructured":"Haopeng Z, Quanmao W, Zhiguo J (2017) 3D reconstruction of space objects from multi-views by a visible sensor. Sensors 17(7):1689\u20131698","journal-title":"Sensors"},{"issue":"1","key":"3194_CR41","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1038\/s41598-017-06475-7","volume":"7","author":"J Laviada","year":"2017","unstructured":"Laviada J, Arboleyaarboleya A, \u00c1lvarez Y et al (2017) Multiview three-dimensional reconstruction by millimetre-wave portable camera. Sci Rep 7(1):64\u201379","journal-title":"Sci Rep"},{"issue":"3","key":"3194_CR42","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1002\/jsid.538","volume":"25","author":"T Ebner","year":"2017","unstructured":"Ebner T, Feldmann I, Renault S et al (2017) Multi-view reconstruction of dynamic real-world objects and their integration in augmented and virtual reality applications. J Soc Inf Disp 25(3):151\u2013157","journal-title":"J Soc Inf Disp"},{"issue":"4","key":"3194_CR43","first-page":"61","volume":"16","author":"Q Wang","year":"2016","unstructured":"Wang Q, Lv H, Yue J et al (2016) Supervised multiview learning based on simultaneous learning of multiview intact and single view classifier. Neural Comput Appl 16(4):61\u201373","journal-title":"Neural Comput Appl"},{"issue":"7","key":"3194_CR44","doi-asserted-by":"crossref","first-page":"3331","DOI":"10.1109\/TIP.2017.2687101","volume":"26","author":"L Sun","year":"2017","unstructured":"Sun L, Chen K, Song M et al (2017) Robust, efficient depth reconstruction with hierarchical confidence-based matching. IEEE Trans Image Process 26(7):3331\u20133343","journal-title":"IEEE Trans Image Process"},{"issue":"7","key":"3194_CR45","first-page":"31","volume":"26","author":"L Huang","year":"2018","unstructured":"Huang L, Chao HY, Wang CD (2018) Multi-view intact space clustering. Pattern Recognit 26(7):31\u201343","journal-title":"Pattern Recognit"},{"key":"3194_CR46","doi-asserted-by":"crossref","unstructured":"Wiles O, Zisserman A (2017) SilNet: single- and multi-view reconstruction by learning from silhouettes. arXiv preprint arXiv:1711.07888","DOI":"10.5244\/C.31.99"},{"issue":"2\u20133","key":"3194_CR47","first-page":"130","volume":"133","author":"Z Yin","year":"2001","unstructured":"Yin Z, Zheng Y, Doerschuk PC (2001) An ab initio algorithm for low-resolution 3-D reconstructions from cryoelectron microscopy images. J Struct Biol 133(2\u20133):130\u2013142","journal-title":"J Struct Biol"},{"issue":"13","key":"3194_CR48","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1093\/bioinformatics\/bty241","volume":"34","author":"L Yu","year":"2018","unstructured":"Yu L, Fan X, Fa Z et al (2018) DLBI: deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy. Bioinformatics 34(13):284\u2013294","journal-title":"Bioinformatics"},{"key":"3194_CR49","first-page":"012008","volume":"787\u2013797","author":"Z Tang","year":"2016","unstructured":"Tang Z, Wang S, Huo J et al (2016) Bayesian framework with non-local and low-rank constraint for image reconstruction. J Phys Conf Ser 787\u2013797:012008","journal-title":"J Phys Conf Ser"},{"issue":"12","key":"3194_CR50","first-page":"31","volume":"10","author":"C Michelangelo","year":"2015","unstructured":"Michelangelo C, Gianvito P, Vladimir K et al (2015) Semi-supervised multi-view learning for gene network reconstruction. PLoS ONE 10(12):31\u201345","journal-title":"PLoS ONE"},{"issue":"4","key":"3194_CR51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3072959.3073629","volume":"36","author":"X Han","year":"2017","unstructured":"Han X, Gao C, Yu Y (2017) DeepSketch2Face: a deep learning based sketching system for 3D face and caricature modeling. ACM Trans Graph\u00a036(4):1\u201312","journal-title":"ACM Trans Graph"},{"issue":"11","key":"3194_CR52","doi-asserted-by":"crossref","first-page":"098","DOI":"10.1002\/jsid.617","volume":"25","author":"K Vodrahalli","year":"2017","unstructured":"Vodrahalli K, Bhowmik AK (2017) 3D computer vision based on machine learning with deep neural networks: a review. J Soc Inf Disp 25(11):098\u2013103","journal-title":"J Soc Inf Disp"},{"key":"3194_CR53","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.media.2018.06.003","volume":"48","author":"P Raphael","year":"2018","unstructured":"Raphael P, Mehrdad S, Simon J et al (2018) 3D freehand ultrasound without external tracking using deep learning. Med Image Anal 48:187\u2013202","journal-title":"Med Image Anal"},{"key":"3194_CR54","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.neucom.2016.11.062","volume":"257","author":"J Zhang","year":"2017","unstructured":"Zhang J, Li K, Liang Y et al (2017) Learning 3D faces from 2D images via stacked contractive autoencoder. Neurocomputing 257:67\u201378","journal-title":"Neurocomputing"},{"issue":"1","key":"3194_CR55","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1109\/TIP.2018.2863028","volume":"28","author":"S Bai","year":"2018","unstructured":"Bai S, Zhou Z, Wang J et al (2018) Automatic ensemble diffusion for 3D shape and image retrieval. IEEE Trans Image Process 28(1): 88\u2013101","journal-title":"IEEE Trans Image Process"},{"key":"3194_CR56","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.compind.2018.02.011","volume":"98","author":"MF Hansen","year":"2018","unstructured":"Hansen MF, Smith ML, Smith LN et al (2018) Automated monitoring of dairy cow body condition, mobility and weight using a single 3D video capture device. Comput Ind 98:14\u201322","journal-title":"Comput Ind"},{"issue":"4","key":"3194_CR57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3072959.3073602","volume":"36","author":"XB Peng","year":"2017","unstructured":"Peng XB, Berseth G, Yin K et al (2017) DeepLoco: dynamic locomotion skills using hierarchical deep reinforcement learning. ACM Trans Graph\u00a036(4):1\u201313","journal-title":"ACM Trans Graph"},{"issue":"45","key":"3194_CR58","first-page":"2081","volume":"65","author":"GW He","year":"2017","unstructured":"He GW, Wang TY, Chiang AS et al (2017) Soma detection in 3D images of neurons using machine learning technique. Neuroinformatics 65(45):2081\u20132099","journal-title":"Neuroinformatics"},{"key":"3194_CR59","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.patcog.2017.06.008","volume":"71","author":"W Zhou","year":"2017","unstructured":"Zhou W, Yu L, Zhou Y et al (2017) Blind quality estimator for 3D images based on binocular combination and extreme learning machine. Pattern Recognit 71:207\u2013217","journal-title":"Pattern Recognit"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-020-03194-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11227-020-03194-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-020-03194-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,14]],"date-time":"2021-02-14T09:48:55Z","timestamp":1613296135000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11227-020-03194-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,15]]},"references-count":59,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2020,11]]}},"alternative-id":["3194"],"URL":"https:\/\/doi.org\/10.1007\/s11227-020-03194-1","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,15]]},"assertion":[{"value":"15 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"We declare that there are no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Data will not be shared as the authors do not have permission to share data from the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Availability of data and materials"}}]}}