{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:21:54Z","timestamp":1771024914194,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2018,4,3]],"date-time":"2018-04-03T00:00:00Z","timestamp":1522713600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61170155"],"award-info":[{"award-number":["61170155"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science & Technology Commission of Shanghai Municipality","award":["15DZ1100502"],"award-info":[{"award-number":["15DZ1100502"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Image Video Proc."],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1186\/s13640-018-0261-2","type":"journal-article","created":{"date-parts":[[2018,4,4]],"date-time":"2018-04-04T00:58:19Z","timestamp":1522803499000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Foreign object debris material recognition based on convolutional neural networks"],"prefix":"10.1186","volume":"2018","author":[{"given":"Haoyu","family":"Xu","sequence":"first","affiliation":[]},{"given":"Zhenqi","family":"Han","sequence":"additional","affiliation":[]},{"given":"Songlin","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Han","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Yuchun","family":"Fang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,4,3]]},"reference":[{"key":"261_CR1","series-title":"FAA, AC (150\/5220) vol. 24","volume-title":"Airport foreign object debris (FOD) detection equipment","author":"MJ O'Donnell","year":"2009","unstructured":"M. J. O'Donnell, \u201cAirport foreign object debris (FOD) detection equipment,\u201d FAA, AC (150\/5220) vol. 24 (2009)."},{"key":"261_CR2","unstructured":"WIKI, Foreign object damage. \n                    https:\/\/en.wikipedia.org\/wiki\/Foreign_object_damage\n                    \n                  . Accessed Nov 2016."},{"key":"261_CR3","volume-title":"FOD Prevention Manual","author":"CAAC Airport Division","year":"2009","unstructured":"CAAC Airport Division, et al., \u201cFOD Prevention Manual,\u201d (2009)"},{"key":"261_CR4","first-page":"58","volume":"295","author":"H Zhang","year":"2015","unstructured":"H Zhang et al., The current status and inspiration of FOD industry in international civil aviation. Civ. Aviat. Manage. 295, 58\u201361 (2015)","journal-title":"Civ. Aviat. Manage."},{"key":"261_CR5","series-title":"In International Conference on Image and Graphics, LNCS Vol. 9217","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1007\/978-3-319-21978-3_26","volume-title":"A novel FOD classification system based on visual features","author":"Z Han","year":"2015","unstructured":"Z Han et al., A novel FOD classification system based on visual features, In International Conference on Image and Graphics, LNCS Vol. 9217 (2015), pp. 288\u2013296. \n                    https:\/\/doi.org\/10.1007\/978-3-319-21978-3_26"},{"key":"261_CR6","series-title":"In 10th International Conference on Communications and Networking in China","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1109\/CHINACOM.2015.7497985","volume-title":"Fusion of low-level feature for FOD classification","author":"Z Han","year":"2015","unstructured":"Z Han, Y Fang, H Xu, Fusion of low-level feature for FOD classification, In 10th International Conference on Communications and Networking in China (2015), pp. 465\u2013469. \n                    https:\/\/doi.org\/10.1109\/CHINACOM.2015.7497985"},{"key":"261_CR7","series-title":"In 32nd Annual Symposium of the German Association for Pattern Recognition, LNCS Vol. 6376","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1007\/978-3-642-15986-2_57","volume-title":"Learning of optimal illumination for material classification","author":"M Jehle","year":"2010","unstructured":"M Jehle, C Sommer, B J\u00e4hne, Learning of optimal illumination for material classification, In 32nd Annual Symposium of the German Association for Pattern Recognition, LNCS Vol. 6376 (2010), pp. 563\u2013572. \n                    https:\/\/doi.org\/10.1007\/978-3-642-15986-2_57"},{"issue":"1","key":"261_CR8","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1109\/TPAMI.2013.110","volume":"36","author":"C Liu","year":"2014","unstructured":"C Liu, J Gu, Discriminative illumination: per-pixel classification of raw materials based on optimal projections of spectral BRDF. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 86\u201398 (2014). \n                    https:\/\/doi.org\/10.1109\/TPAMI.2013.110","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"261_CR9","series-title":"In Computer Vision and Pattern Recognition","doi-asserted-by":"publisher","first-page":"1430","DOI":"10.1109\/CVPR.2013.188","volume-title":"Learning discriminative illumination and filters for raw material classification with optimal projections of bidirectional texture functions","author":"C Liu","year":"2013","unstructured":"C Liu, G Yang, J Gu, Learning discriminative illumination and filters for raw material classification with optimal projections of bidirectional texture functions, In Computer Vision and Pattern Recognition (2013), pp. 1430\u20131437. \n                    https:\/\/doi.org\/10.1109\/CVPR.2013.188"},{"key":"261_CR10","series-title":"In Computer Vision and Pattern Recognition","doi-asserted-by":"publisher","first-page":"3071","DOI":"10.1109\/CVPR.2015.7298926","volume-title":"Reflectance hashing for material recognition","author":"H Zhang","year":"2015","unstructured":"H Zhang, K Dana, K Nishino, Reflectance hashing for material recognition, In Computer Vision and Pattern Recognition (2015), pp. 3071\u20133080. \n                    https:\/\/doi.org\/10.1109\/CVPR.2015.7298926"},{"key":"261_CR11","series-title":"International Conference on Computer Analysis of Images and Patterns, LNCS. Vol. 9257","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/978-3-319-23117-4","volume-title":"Materials classification using sparse gray-scale bidirectional reflectance measurements","author":"J Filip","year":"2015","unstructured":"J Filip, P Somol, Materials classification using sparse gray-scale bidirectional reflectance measurements, International Conference on Computer Analysis of Images and Patterns, LNCS. Vol. 9257 (2015), pp. 289\u2013299. \n                    https:\/\/doi.org\/10.1007\/978-3-319-23117-4"},{"key":"261_CR12","series-title":"In European Conference on Computer Vision, LNCS. Vol. 3024","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/978-3-540-24673-2_21","volume-title":"On trhe significance of real-world conditions for material classification","author":"E Hayman","year":"2004","unstructured":"E Hayman, B Caputo, M Fritz, JO Eklundh, On trhe significance of real-world conditions for material classification, In European Conference on Computer Vision, LNCS. Vol. 3024 (2004), pp. 253\u2013266. \n                    https:\/\/doi.org\/10.1007\/978-3-540-24673-2_21"},{"issue":"1","key":"261_CR13","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.imavis.2009.05.005","volume":"28","author":"B Caputo","year":"2010","unstructured":"B Caputo, E Hayman, M Fritz, JO Eklundh, Classifying materials in the real world. Image Vis. Comput. 28(1), 150\u2013163 (2010). \n                    https:\/\/doi.org\/10.1016\/j.imavis.2009.05.005","journal-title":"Image Vis. Comput."},{"key":"261_CR14","series-title":"In 10th IEEE International Conference on Computer Vision","doi-asserted-by":"publisher","first-page":"1597","DOI":"10.1109\/ICCV.2005.54","volume-title":"Class-specific material categorization","author":"B Caputo","year":"2005","unstructured":"B Caputo, E Hayman, P Mallikarjuna, Class-specific material categorization, In 10th IEEE International Conference on Computer Vision, Proc. IEEE Int. Conf. Comput. Vision II (2005), pp. 1597\u20131604. \n                    https:\/\/doi.org\/10.1109\/ICCV.2005.54"},{"key":"261_CR15","series-title":"In European Conference on Computer Vision, LNCS Vol. 2352","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/3-540-47977-5_17","volume-title":"Classifying images of materials: achieving viewpoint and illumination independence","author":"M Varma","year":"2002","unstructured":"M Varma, A Zisserman, Classifying images of materials: achieving viewpoint and illumination independence, In European Conference on Computer Vision, LNCS Vol. 2352 (2002), pp. 255\u2013271. \n                    https:\/\/doi.org\/10.1007\/3-540-47977-5_17"},{"issue":"11","key":"261_CR16","doi-asserted-by":"publisher","first-page":"2032","DOI":"10.1109\/TPAMI.2008.182","volume":"31","author":"M Varma","year":"2009","unstructured":"M Varma, A Zisserman, A statistical approach to material classification using image patch exemplars. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 2032\u20132047 (2009). \n                    https:\/\/doi.org\/10.1109\/TPAMI.2008.182","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1-2","key":"261_CR17","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/s11263-005-4635-4","volume":"62","author":"M Varma","year":"2005","unstructured":"M Varma, A Zisserman, A statistical approach to texture classification from single images. Int. J. Comput. Vis. 62(1-2), 61\u201381 (2005). \n                    https:\/\/doi.org\/10.1023\/B:VISI.0000046589.39864.ee","journal-title":"Int. J. Comput. Vis."},{"issue":"3","key":"261_CR18","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1109\/TCSVT.2014.2359098","volume":"25","author":"L Liu","year":"2015","unstructured":"L Liu, PW Fieguth, D Hu, Y Wei, G Kuang, Fusing sorted random projections for robust texture and material classification. IEEE Trans. Circuits. Syst. Vid. Technol. 25(3), 482\u2013496 (2015). \n                    https:\/\/doi.org\/10.1109\/TCSVT.2014.2359098","journal-title":"IEEE Trans. Circuits. Syst. Vid. Technol."},{"key":"261_CR19","series-title":"In British Machine Vision Conference, Proc. BMVC","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5244\/C.25.48","volume-title":"Toward robust material recognition for everyday objects","author":"D Hu","year":"2011","unstructured":"D Hu, L Bo, Toward robust material recognition for everyday objects, In British Machine Vision Conference, Proc. BMVC (2011), pp. 1\u201311. \n                    https:\/\/doi.org\/10.5244\/C.25.48"},{"key":"261_CR20","series-title":"In 2010 IEEE Conference on Computer Vision and Pattern Recognition, Proc. CVPR","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1109\/CVPR.2010.5540207","volume-title":"Exploring features in a Bayesian framework for material recognition","author":"C Liu","year":"2010","unstructured":"C Liu, L Sharan, EH Adelson, R Rosenholtz, Exploring features in a Bayesian framework for material recognition, In 2010 IEEE Conference on Computer Vision and Pattern Recognition, Proc. CVPR (2010), pp. 239\u2013246. \n                    https:\/\/doi.org\/10.1109\/CVPR.2010.5540207"},{"issue":"3","key":"261_CR21","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1007\/s11263-013-0609-0","volume":"103","author":"L Sharan","year":"2013","unstructured":"L Sharan, C Liu, R Rosenholtz, EH Adelson, Recognizing materials using perceptually inspired features. Int. J. Comput. Vis. 103(3), 348\u2013371 (2013). \n                    https:\/\/doi.org\/10.1007\/s11263-013-0609-0","journal-title":"Int. J. Comput. Vis."},{"issue":"9","key":"261_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1167\/14.9.12","volume":"14","author":"L Sharan","year":"2014","unstructured":"L Sharan, R Rosenholtz, EH Adelson, Accuracy and speed of material categorization in real-world images. J. Vis. 14(9), 1\u201324 (2014). \n                    https:\/\/doi.org\/10.1167\/14.9.12","journal-title":"J. Vis."},{"key":"261_CR23","series-title":"In 2014 IEEE Conference on Computer Vision and Pattern Recognition, Proc. CVPR","doi-asserted-by":"publisher","first-page":"3606","DOI":"10.1109\/CVPR.2014.461","volume-title":"Describing textures in the wild","author":"M Cimpoi","year":"2014","unstructured":"M Cimpoi et al., Describing textures in the wild, In 2014 IEEE Conference on Computer Vision and Pattern Recognition, Proc. CVPR (2014), pp. 3606\u20133613. \n                    https:\/\/doi.org\/10.1109\/CVPR.2014.461"},{"key":"261_CR24","series-title":"In 2015 IEEE Conference on Computer Vision and Pattern Recognition, Proc. CVPR","doi-asserted-by":"publisher","first-page":"3828","DOI":"10.1109\/CVPR.2015.7299007","volume-title":"Deep filter banks for texture recognition and segmentation","author":"M Cimpoi","year":"2015","unstructured":"M Cimpoi, S Maji, A Vedaldi, Deep filter banks for texture recognition and segmentation, In 2015 IEEE Conference on Computer Vision and Pattern Recognition, Proc. CVPR (2015), pp. 3828\u20133836. \n                    https:\/\/doi.org\/10.1109\/CVPR.2015.7299007"},{"key":"261_CR25","series-title":"In 2015 IEEE Conference on Computer Vision and Pattern Recognition, Proc. CVPR","doi-asserted-by":"publisher","first-page":"3479","DOI":"10.1109\/CVPR.2015.7298970","volume-title":"Material recognition in the wild with the materials in context database","author":"S Bell","year":"2015","unstructured":"S Bell, P Upchurch, N Snavely, K Bala, Material recognition in the wild with the materials in context database, In 2015 IEEE Conference on Computer Vision and Pattern Recognition, Proc. CVPR (2015), pp. 3479\u20133487. \n                    https:\/\/doi.org\/10.1109\/CVPR.2015.7298970"},{"key":"261_CR26","volume-title":"Integrating deep features for material recognition","author":"Y Zhang","year":"2015","unstructured":"Y. Zhang et al., \u201cIntegrating deep features for material recognition,\u201d (2015) [\n                    arXiv:1511.06522\n                    \n                  ]."},{"issue":"1","key":"261_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.aei.2013.10.001","volume":"28","author":"H Son","year":"2014","unstructured":"H Son, C Kim, N Hwang, C Kim, Y Kang, Classification of major construction materials in construction environments using ensemble classifiers. Adv. Eng. Inform. 28(1), 1\u201310 (2014). \n                    https:\/\/doi.org\/10.1016\/j.aei.2013.10.001","journal-title":"Adv. Eng. Inform."},{"issue":"1","key":"261_CR28","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.aei.2013.11.002","volume":"28","author":"A Dimitrov","year":"2014","unstructured":"A Dimitrov, M Golparvar-Fard, Vision-based material recognition for automated monitoring of construction progress and generating building information modeling from unordered site image collections. Adv. Eng. Inform. 28(1), 37\u201349 (2014). \n                    https:\/\/doi.org\/10.1016\/j.aei.2013.11.002","journal-title":"Adv. Eng. Inform."},{"issue":"2-3","key":"261_CR29","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1023\/A:1008061730969","volume":"31","author":"JJ Koenderink","year":"1999","unstructured":"JJ Koenderink et al., Bidirectional reflection distribution function of thoroughly pitted surfaces. Int. J. Comput. Vis. 31(2-3), 129\u2013144 (1999). \n                    https:\/\/doi.org\/10.1023\/A:1008061730969","journal-title":"Int. J. Comput. Vis."},{"issue":"3","key":"261_CR30","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1007\/BF01679684","volume":"14","author":"M Oren","year":"1995","unstructured":"M Oren, Generalization of the Lambertian model and implications for machine vision. Int. J. Comput. Vis. 14(3), 227\u2013251 (1995). \n                    https:\/\/doi.org\/10.1007\/BF01679684","journal-title":"Int. J. Comput. Vis."},{"issue":"7","key":"261_CR31","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"GE Hinton et al., A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527\u20131554 (2006). \n                    https:\/\/doi.org\/10.1162\/neco.2006.18.7.1527","journal-title":"Neural Comput."},{"key":"261_CR32","volume-title":"Deep residual learning for image recognition","author":"K He","year":"2015","unstructured":"K. He et al., \u201cDeep residual learning for image recognition,\u201d (2015) [arXiv:1512.03385]."},{"issue":"3","key":"261_CR33","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"O Russakovsky et al., ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211\u2013252 (2015). \n                    https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int. J. Comput. Vis."},{"key":"261_CR34","series-title":"In 2009 IEEE Conference on Computer Vision and Pattern Recognition, Proc. CVPR","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1109\/CVPR.2009.5206848].","volume-title":"ImageNet: a large-scale hierarchical image database","author":"J Deng","year":"2009","unstructured":"J Deng et al., ImageNet: a large-scale hierarchical image database, In 2009 IEEE Conference on Computer Vision and Pattern Recognition, Proc. CVPR (2009), pp. 248\u2013255. \n                    https:\/\/doi.org\/10.1109\/CVPR.2009.5206848\n                    \n                  ]."},{"key":"261_CR35","series-title":"In 26th Annual Conference on Neural Information Processing Systems, Adv. Neural Inf. Proces. Syst","first-page":"1097","volume-title":"ImageNet classification with deep convolutional neural networks","author":"A Krizhevsky","year":"2012","unstructured":"A Krizhevsky, I Sutskever, GE Hinton, ImageNet classification with deep convolutional neural networks, In 26th Annual Conference on Neural Information Processing Systems, Adv. Neural Inf. Proces. Syst (2012), pp. 1097\u20131105"},{"key":"261_CR36","series-title":"In 2015 IEEE Conference on Computer Vision and Pattern Recognition, Proc. CVPR","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/CVPR.2015.7298594]","volume-title":"Going deeper with convolutions","author":"C Szegedy","year":"2015","unstructured":"C Szegedy et al., Going deeper with convolutions, In 2015 IEEE Conference on Computer Vision and Pattern Recognition, Proc. CVPR (2015), pp. 1\u20139. \n                    https:\/\/doi.org\/10.1109\/CVPR.2015.7298594\n                    \n                  ]."},{"issue":"1","key":"261_CR37","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1109\/TITS.2017.2749977","volume":"19","author":"C Yan","year":"2018","unstructured":"C Yan et al., Effective Uyghur language text detection in complex background images for traffic prompt identification. IEEE Trans. Intell. Transport. Syst. 19(1), 220\u2013229 (2018). \n                    https:\/\/doi.org\/10.1109\/TITS.2017.2749977","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"261_CR38","unstructured":"Labeled Faces in the Wild. \n                    http:\/\/vis-www.cs.umass.edu\/lfw\/results.html#glasssix\n                    \n                  . Accessed Sept 2017."},{"key":"261_CR39","unstructured":"Large Scale Visual Recognition Challenge 2017 (ILSVRC2017), Task 1a: Object detection with provided training data. \n                    http:\/\/image-net.org\/challenges\/LSVRC\/2017\/results\n                    \n                  . Accessed Sept 2017."},{"key":"261_CR40","volume-title":"Dual path networks","author":"Y Chen","year":"2017","unstructured":"Y. Chen, J. Li, H. Xiao et al., \u201cDual path networks,\u201d (2017) [arXiv:1707.01629]."},{"key":"261_CR41","series-title":"IEEE Transactions on Intelligent Transportation Systems","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2749965","volume-title":"Supervised hash coding with deep neural network for environment perception of intelligent vehicles","author":"C Yan","year":"2018","unstructured":"C Yan et al., Supervised hash coding with deep neural network for environment perception of intelligent vehicles, IEEE Transactions on Intelligent Transportation Systems (2018). \n                    https:\/\/doi.org\/10.1109\/TITS.2017.2749965"},{"key":"261_CR42","volume-title":"Very deep convolutional networks for large-scale image recognition","author":"K Simonyan","year":"2014","unstructured":"K. Simonyan and A. Zisserman, \u201cVery deep convolutional networks for large-scale image recognition,\u201d (2014) [arXiv:1409.1556]."},{"issue":"5","key":"261_CR43","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1109\/TNNLS.2014.2330900","volume":"26","author":"L Shao","year":"2015","unstructured":"L Shao, F Zhu, X Li, Transfer learning for visual categorization: a survey. IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1019\u20131034 (2015)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"1","key":"261_CR44","doi-asserted-by":"publisher","first-page":"26","DOI":"10.13328\/j.cnki.jos.004631","volume":"26","author":"F Zhuang","year":"2015","unstructured":"F Zhuang et al., Survey on transfer learning research. J. Software 26(1), 26\u201339 (2015). \n                    https:\/\/doi.org\/10.13328\/j.cnki.jos.004631","journal-title":"J. Software"},{"key":"261_CR45","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Proc. CVPR","first-page":"2892","volume-title":"Deeply learned face representations are sparse, selective, and robust","author":"Y Sun","year":"2015","unstructured":"Y Sun, X Wang, X Tang, Deeply learned face representations are sparse, selective, and robust, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Proc. CVPR (2015), pp. 2892\u20132900"},{"key":"261_CR46","series-title":"CLEF (Working Notes)","volume-title":"Fine-tuning deep convolutional networks for plant recognition","author":"AK Reyes","year":"2015","unstructured":"A. K. Reyes, et al. \u201cFine-tuning deep convolutional networks for plant recognition,\u201d CLEF (Working Notes), 2015."},{"key":"261_CR47","volume-title":"Overfeat: Integrated recognition, localization and detection using convolutional networks","author":"P Sermanet","year":"2013","unstructured":"P. Sermanet, et al. \u201cOverfeat: Integrated recognition, localization and detection using convolutional networks,\u201d [arXiv:1312.6229] (2013)."},{"issue":"7540","key":"261_CR48","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"V Mnih et al., Human-level control through deep reinforcement learning. Nature 518(7540), 529\u2013533 (2015)","journal-title":"Nature"},{"key":"261_CR49","series-title":"In 2014 ACM Conference on Multimedia, Proc. ACM Conf. Multimedia","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1145\/2647868.2654889","volume-title":"Caffe: convolutional architecture for fast feature embedding","author":"Y Jia","year":"2014","unstructured":"Y Jia et al., Caffe: convolutional architecture for fast feature embedding, In 2014 ACM Conference on Multimedia, Proc. ACM Conf. Multimedia (2014), pp. 675\u2013678. \n                    https:\/\/doi.org\/10.1145\/2647868.2654889"},{"issue":"5","key":"261_CR50","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1109\/LSP.2014.2310494","volume":"21","author":"C Yan","year":"2014","unstructured":"C Yan, Y Zhang, J Xu, et al., A highly parallel framework for HEVC coding unit partitioning tree decision on many-core processors. IEEE Signal. Process. Letters 21(5), 573\u2013576 (2014)","journal-title":"IEEE Signal. Process. Letters"},{"issue":"12","key":"261_CR51","doi-asserted-by":"publisher","first-page":"2077","DOI":"10.1109\/TCSVT.2014.2335852","volume":"24","author":"C Yan","year":"2014","unstructured":"C Yan et al., Efficient parallel framework for HEVC motion estimation on many-core processors. IEEE Trans. Circuits. Syst. Vid. Technol. 24(12), 2077\u20132089 (2014)","journal-title":"IEEE Trans. Circuits. Syst. Vid. Technol."},{"issue":"5","key":"261_CR52","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1049\/el.2013.3235","volume":"50","author":"C Yan","year":"2014","unstructured":"C Yan et al., Parallel deblocking filter for HEVC on many-core processor. Electron. Lett. 50(5), 367\u2013368 (2014)","journal-title":"Electron. Lett."},{"issue":"11","key":"261_CR53","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1049\/el.2014.0611","volume":"50","author":"C Yan","year":"2014","unstructured":"C Yan et al., Efficient parallel HEVC intra-prediction on many-core processor. Electron. Lett. 50(11), 805\u2013806 (2014)","journal-title":"Electron. Lett."}],"container-title":["EURASIP Journal on Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13640-018-0261-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13640-018-0261-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13640-018-0261-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T19:31:13Z","timestamp":1554233473000},"score":1,"resource":{"primary":{"URL":"https:\/\/jivp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13640-018-0261-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,3]]},"references-count":53,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,12]]}},"alternative-id":["261"],"URL":"https:\/\/doi.org\/10.1186\/s13640-018-0261-2","relation":{},"ISSN":["1687-5281"],"issn-type":[{"value":"1687-5281","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,3]]},"assertion":[{"value":"6 April 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 March 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 April 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Publisher\u2019s Note"}}],"article-number":"21"}}