{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T09:10:08Z","timestamp":1777626608141,"version":"3.51.4"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030012182","type":"print"},{"value":"9783030012199","type":"electronic"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-01219-9_48","type":"book-chapter","created":{"date-parts":[[2018,10,6]],"date-time":"2018-10-06T10:23:51Z","timestamp":1538821431000},"page":"812-828","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Joint Camera Spectral Sensitivity Selection and Hyperspectral Image Recovery"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6677-694X","authenticated-orcid":false,"given":"Ying","family":"Fu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7358-0603","authenticated-orcid":false,"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7434-5069","authenticated-orcid":false,"given":"Yinqiang","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4048-0531","authenticated-orcid":false,"given":"Debing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2587-1702","authenticated-orcid":false,"given":"Hua","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,10,7]]},"reference":[{"key":"48_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-319-10584-0_5","volume-title":"Computer Vision \u2013 ECCV 2014","author":"N Akhtar","year":"2014","unstructured":"Akhtar, N., Shafait, F., Mian, A.: Sparse spatio-spectral representation for hyperspectral image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 63\u201378. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10584-0_5"},{"issue":"6","key":"48_CR2","doi-asserted-by":"publisher","first-page":"2596","DOI":"10.1109\/TIP.2014.2316641","volume":"23","author":"HA Aly","year":"2014","unstructured":"Aly, H.A., Sharma, G.: A regularized model-based optimization framework for pan-sharpening. IEEE Trans. Image Process. 23(6), 2596\u20132608 (2014)","journal-title":"IEEE Trans. Image Process."},{"key":"48_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-3-319-46478-7_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"B Arad","year":"2016","unstructured":"Arad, B., Ben-Shahar, O.: Sparse recovery of hyperspectral signal from natural RGB images. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 19\u201334. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_2"},{"key":"48_CR4","doi-asserted-by":"crossref","unstructured":"Arad, B., Ben-Shahar, O.: Filter selection for hyperspectral estimation. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 3172\u20133180, October 2017","DOI":"10.1109\/ICCV.2017.342"},{"key":"48_CR5","doi-asserted-by":"crossref","unstructured":"Basedow, R.W., Carmer, D.C., Anderson, M.E.: HYDICE system: implementation and performance. In: SPIE\u2019s Symposium on OE\/Aerospace Sensing and Dual Use Photonics, pp. 258\u2013267 (1995)","DOI":"10.1117\/12.210881"},{"issue":"2","key":"48_CR6","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","volume":"1","author":"JM Bioucas-Dias","year":"2013","unstructured":"Bioucas-Dias, J.M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N.M., Chanussot, J.: Hyperspectral remote sensing data analysis and future challenges. IEEE Geosci. Remote Sens. Mag. 1(2), 6\u201336 (2013)","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"48_CR7","doi-asserted-by":"crossref","unstructured":"Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 60\u201365, June 2005","DOI":"10.1109\/CVPR.2005.38"},{"issue":"12","key":"48_CR8","doi-asserted-by":"publisher","first-page":"2423","DOI":"10.1109\/TPAMI.2011.80","volume":"33","author":"X Cao","year":"2011","unstructured":"Cao, X., Du, H., Tong, X., Dai, Q., Lin, S.: A prism-based system for multispectral bideo acquisition. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 33(12), 2423\u20132435 (2011)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (PAMI)"},{"key":"48_CR9","doi-asserted-by":"crossref","unstructured":"Chakrabarti, A., Zickler, T.: Statistics of real-world hyperspectral images. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 193\u2013200, June 2011","DOI":"10.1109\/CVPR.2011.5995660"},{"issue":"2\u20133","key":"48_CR10","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1007\/s11263-008-0176-y","volume":"86","author":"C Chi","year":"2010","unstructured":"Chi, C., Yoo, H., Ben-Ezra, M.: Multi-spectral imaging by optimized wide band illumination. Int. J. Comput. Vis. (IJCV) 86(2\u20133), 140\u2013151 (2010)","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"48_CR11","doi-asserted-by":"crossref","unstructured":"Dian, R., Fang, L., Li, S.: Hyperspectral image super-resolution via non-local sparse tensor factorization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5344\u20135353, June 2017","DOI":"10.1109\/CVPR.2017.411"},{"issue":"5","key":"48_CR12","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1109\/TIP.2016.2542360","volume":"25","author":"W Dong","year":"2016","unstructured":"Dong, W., et al.: Hyperspectral image super-resolution via non-negative structured sparse representation. IEEE Trans. Image Process. 25(5), 2337\u20132352 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"48_CR13","doi-asserted-by":"crossref","unstructured":"Dong, W., Zhang, L., Shi, G.: Centralized sparse representation for image restoration. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 1259\u20131266, November 2011","DOI":"10.1109\/ICCV.2011.6126377"},{"issue":"9","key":"48_CR14","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1364\/OE.9.000444","volume":"9","author":"BK Ford","year":"2001","unstructured":"Ford, B.K., Descour, M.R., Lynch, R.M.: Large-image-format computed tomography imaging spectrometer for fluorescence microscopy. Opt. Express 9(9), 444\u2013453 (2001)","journal-title":"Opt. Express"},{"issue":"14","key":"48_CR15","doi-asserted-by":"publisher","first-page":"14330","DOI":"10.1364\/OE.18.014330","volume":"18","author":"L Gao","year":"2010","unstructured":"Gao, L., Kester, R.T., Hagen, N., Tkaczyk, T.S.: Snapshot image mapping spectrometer (IMS) with high sampling density for hyperspectral microscopy. Opt. Express 18(14), 14330\u201314344 (2010)","journal-title":"Opt. Express"},{"key":"48_CR16","doi-asserted-by":"crossref","unstructured":"Gat, N., Scriven, G., Garman, J., Li, M.D., Zhang, J.: Development of four-dimensional imaging spectrometers (4D-IS). In: Proceeding of SPIE Optics + Photonics, vol. 6302, pp. 63020M\u201363020M-11 (2006)","DOI":"10.1117\/12.678082"},{"issue":"21","key":"48_CR17","doi-asserted-by":"publisher","first-page":"14013","DOI":"10.1364\/OE.15.014013","volume":"15","author":"ME Gehm","year":"2007","unstructured":"Gehm, M.E., John, R., Brady, D.J., Willett, R.M., Schulz, T.J.: Single-shot compressive spectral imaging with a dual-disperser architecture. Opt. Express 15(21), 14013\u201327 (2007)","journal-title":"Opt. Express"},{"key":"48_CR18","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256, May 2010"},{"issue":"2","key":"48_CR19","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1007\/s11263-013-0687-z","volume":"110","author":"S Han","year":"2014","unstructured":"Han, S., Sato, I., Okabe, T., Sato, Y.: Fast spectral reflectance recovery using DLP projector. Int. J. Comput. Vis. (IJCV) 110(2), 172\u2013184 (2014)","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"48_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630\u2013645. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38"},{"key":"48_CR21","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261\u20132269, July 2017","DOI":"10.1109\/CVPR.2017.243"},{"key":"48_CR22","doi-asserted-by":"crossref","unstructured":"Jia, Y., et al.: From RGB to spectrum for natural scenes via manifold-based mapping. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 4715\u20134723, October 2017","DOI":"10.1109\/ICCV.2017.504"},{"key":"48_CR23","doi-asserted-by":"crossref","unstructured":"Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of ACM Multimedia Conference (MM), pp. 675\u2013678, November 2014","DOI":"10.1145\/2647868.2654889"},{"key":"48_CR24","doi-asserted-by":"crossref","unstructured":"Jiang, J., Liu, D., Gu, J., Ssstrunk, S.: What is the space of spectral sensitivity functions for digital color cameras? In: IEEE Workshop on Applications of Computer Vision (WACV), pp. 168\u2013179 (2013)","DOI":"10.1109\/WACV.2013.6475015"},{"key":"48_CR25","doi-asserted-by":"crossref","unstructured":"Kawakami, R., Wright, J., Tai, Y.W., Matsushita, Y., Ben-Ezra, M., Ikeuchi, K.: High-resolution hyperspectral imaging via matrix factorization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2329\u20132336, June 2011","DOI":"10.1109\/CVPR.2011.5995457"},{"issue":"3","key":"48_CR26","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s11263-013-0632-1","volume":"105","author":"R Kawakami","year":"2013","unstructured":"Kawakami, R., Zhao, H., Tan, R.T., Ikeuchi, K.: Camera spectral sensitivity and white balance estimation from sky images. Int. J. Comput. Vis. (IJCV) 105(3), 187\u2013204 (2013)","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"48_CR27","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1646\u20131654, June 2016","DOI":"10.1109\/CVPR.2016.182"},{"issue":"7","key":"48_CR28","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.1016\/j.patcog.2010.12.019","volume":"44","author":"SJ Kim","year":"2011","unstructured":"Kim, S.J., Deng, F., Brown, M.S.: Visual enhancement of old documents with hyperspectral imaging. Pattern Recogn. 44(7), 1461\u20131469 (2011)","journal-title":"Pattern Recogn."},{"key":"48_CR29","unstructured":"Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: Proceedings of International Conference on Learning Representations (ICLR), May 2015"},{"issue":"2\u20133","key":"48_CR30","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","volume":"44","author":"FA Kruse","year":"1993","unstructured":"Kruse, F.A., et al.: The spectral image processing system (SIPS)-interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 44(2\u20133), 145\u2013163 (1993)","journal-title":"Remote Sens. Environ."},{"key":"48_CR31","doi-asserted-by":"crossref","unstructured":"Kwon, H., Tai, Y.W.: RGB-guided hyperspectral image upsampling. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 307\u2013315, December 2015","DOI":"10.1109\/ICCV.2015.43"},{"key":"48_CR32","doi-asserted-by":"crossref","unstructured":"Lanaras, C., Baltsavias, E., Schindler, K.: Hyperspectral super-resolution by coupled spectral unmixing. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 3586\u20133594, December 2015","DOI":"10.1109\/ICCV.2015.409"},{"issue":"6","key":"48_CR33","doi-asserted-by":"publisher","first-page":"233:1","DOI":"10.1145\/2661229.2661262","volume":"33","author":"X Lin","year":"2014","unstructured":"Lin, X., Liu, Y., Wu, J., Dai, Q.: Spatial-spectral encoded compressive hyperspectral imaging. ACM Trans. Graph. 33(6), 233:1\u2013233:11 (2014). (Proceedings of SIGGRAPH Asia)","journal-title":"ACM Trans. Graph."},{"key":"48_CR34","unstructured":"Loffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of International Conference on Machine Learning (ICML), pp. 448\u2013456, June 2015"},{"issue":"2","key":"48_CR35","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/s11263-013-0690-4","volume":"110","author":"C Ma","year":"2014","unstructured":"Ma, C., Cao, X., Tong, X., Dai, Q., Lin, S.: Acquisition of high spatial and spectral resolution video with a hybrid camera system. Int. J. Comput. Vis. (IJCV) 110(2), 141\u2013155 (2014)","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"48_CR36","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of International Conference on Machine Learning (ICML), pp. 807\u2013814, June 2010"},{"key":"48_CR37","doi-asserted-by":"crossref","unstructured":"Nguyen, H.V., Banerjee, A., Chellappa, R.: Tracking via object reflectance using a hyperspectral video camera. In: IEEE Conference on Computer Vision and Pattern Recognition - Workshops, pp. 44\u201351, June 2010","DOI":"10.1007\/978-3-642-11568-4_9"},{"key":"48_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1007\/978-3-319-10584-0_13","volume-title":"Computer Vision \u2013 ECCV 2014","author":"RMH Nguyen","year":"2014","unstructured":"Nguyen, R.M.H., Prasad, D.K., Brown, M.S.: Training-based spectral reconstruction from a single RGB image. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 186\u2013201. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10584-0_13"},{"issue":"12","key":"48_CR39","doi-asserted-by":"publisher","first-page":"1552","DOI":"10.1109\/TPAMI.2003.1251148","volume":"25","author":"Z Pan","year":"2003","unstructured":"Pan, Z., Healey, G., Prasad, M., Tromberg, B.: Face recognition in hyperspectral images. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 25(12), 1552\u20131560 (2003)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (PAMI)"},{"key":"48_CR40","doi-asserted-by":"crossref","unstructured":"Park, J.I., Lee, M.H., Grossberg, M.D., Nayar, S.K.: Multispectral imaging using multiplexed illumination. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 1\u20138, October 2007","DOI":"10.1109\/ICCV.2007.4409090"},{"key":"48_CR41","doi-asserted-by":"crossref","unstructured":"Porter, W.M., Enmark, H.T.: A system overview of the airborne visible\/infrared imaging spectrometer (AVIRIS). In: Annual Technical Symposium, pp. 22\u201331 (1987)","DOI":"10.1117\/12.942280"},{"key":"48_CR42","doi-asserted-by":"crossref","unstructured":"Robles-Kelly, A.: Single image spectral reconstruction for multimedia applications. In: Proceedings of ACM Multimedia Conference (MM), pp. 251\u2013260, October 2015","DOI":"10.1145\/2733373.2806223"},{"issue":"7","key":"48_CR43","doi-asserted-by":"publisher","first-page":"2367","DOI":"10.1016\/j.patcog.2010.01.016","volume":"43","author":"Y Tarabalka","year":"2010","unstructured":"Tarabalka, Y., Chanussot, J., Benediktsson, J.A.: Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recogn. 43(7), 2367\u20132379 (2010)","journal-title":"Pattern Recogn."},{"issue":"10","key":"48_CR44","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1364\/AO.47.000B44","volume":"47","author":"A Wagadarikar","year":"2008","unstructured":"Wagadarikar, A., John, R., Willett, R., Brady, D.: Single disperser design for coded aperture snapshot spectral imaging. Appl. Opt. 47(10), 44\u201351 (2008)","journal-title":"Appl. Opt."},{"issue":"4","key":"48_CR45","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1364\/AO.54.000848","volume":"54","author":"L Wang","year":"2015","unstructured":"Wang, L., Xiong, Z., Gao, D., Shi, G., Wu, F.: Dual-camera design for coded aperture snapshot spectral imaging. Appl. Opt. 54(4), 848\u2013858 (2015)","journal-title":"Appl. Opt."},{"issue":"10","key":"48_CR46","doi-asserted-by":"publisher","first-page":"2104","DOI":"10.1109\/TPAMI.2016.2621050","volume":"39","author":"L Wang","year":"2017","unstructured":"Wang, L., Xiong, Z., Shi, G., Wu, F., Zeng, W.: Adaptive nonlocal sparse representation for dual-camera compressive hyperspectral imaging. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 39(10), 2104\u20132111 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (PAMI)"},{"issue":"4","key":"48_CR47","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"48_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ifset.2013.04.014","volume":"19","author":"D Wu","year":"2013","unstructured":"Wu, D., Sun, D.W.: Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: a reviewpart I: fundamentals. Innov. Food Sci. Emerg. Technol. 19, 1\u201314 (2013)","journal-title":"Innov. Food Sci. Emerg. Technol."},{"issue":"5","key":"48_CR49","doi-asserted-by":"publisher","first-page":"3083","DOI":"10.1109\/TGRS.2015.2511197","volume":"54","author":"X Xu","year":"2016","unstructured":"Xu, X., Li, J., Huang, X., Dalla Mura, M., Plaza, A.: Multiple morphological component analysis based decomposition for remote sensing image classification. IEEE Trans. Geosci. Remote Sens. 54(5), 3083\u20133102 (2016)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"4","key":"48_CR50","doi-asserted-by":"publisher","first-page":"1990","DOI":"10.1109\/TGRS.2015.2493201","volume":"54","author":"X Xu","year":"2016","unstructured":"Xu, X., Wu, Z., Li, J., Plaza, A., Wei, Z.: Anomaly detection in hyperspectral images based on low-rank and sparse representation. IEEE Trans. Geosci. Remote Sens. 54(4), 1990\u20132000 (2016)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"48_CR51","doi-asserted-by":"crossref","unstructured":"Yamaguchi, M., et al.: High-fidelity video and still-image communication based on spectral information: natural vision system and its applications. In: Electronic Imaging, pp. 60620G\u201360620G-12 (2006)","DOI":"10.1117\/12.649454"},{"key":"48_CR52","doi-asserted-by":"crossref","unstructured":"Yang, J., Fu, X., Hu, Y., Huang, Y., Ding, X., John, P.: PanNet: a deep network architecture for pan-sharpening. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 1753\u20131761, October 2017","DOI":"10.1109\/ICCV.2017.193"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01219-9_48","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T18:47:23Z","timestamp":1775242043000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-01219-9_48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030012182","9783030012199"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01219-9_48","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"7 October 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","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":"8 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2018.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}