{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T19:56:22Z","timestamp":1780775782952,"version":"3.54.1"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T00:00:00Z","timestamp":1629158400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T00:00:00Z","timestamp":1629158400000},"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":["Vis Comput"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s00371-021-02280-6","type":"journal-article","created":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T09:02:42Z","timestamp":1629190962000},"page":"2223-2237","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A novel backbone architecture for pedestrian detection based on the human visual system"],"prefix":"10.1007","volume":"38","author":[{"given":"Mahmoud","family":"Saeidi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abouzar","family":"Arabsorkhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,8,17]]},"reference":[{"issue":"6","key":"2280_CR1","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1109\/TPAMI.2015.2474388","volume":"38","author":"S Paisitkriangkrai","year":"2015","unstructured":"Paisitkriangkrai, S., Shen, C., van den Hengel, A.: Pedestrian detection with spatially pooled features and structured ensemble learning. IEEE Trans. Pattern Anal. Mach. Intell. 38(6), 1243\u20131257 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2280_CR2","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"2280_CR3","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Arxiv preprint arXiv:1409.1556 (2014)"},{"key":"2280_CR4","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"2280_CR5","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2280_CR6","unstructured":"Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229 (2013)"},{"key":"2280_CR7","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"issue":"9","key":"2280_CR8","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904\u20131916 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2280_CR9","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91\u201399 (2015)"},{"key":"2280_CR10","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2280_CR11","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"2280_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"2280_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Z., Chen, Z., Li, Z., Hu, W.: An efficient pedestrian detection method based on YOLOv2. Math. Probl. Eng. 2018 , 1\u201310 (2018)","DOI":"10.1155\/2018\/3518959"},{"key":"2280_CR14","doi-asserted-by":"crossref","unstructured":"Du, X., El-Khamy, M., Lee, J., Davis, L.: Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 953\u2013961. IEEE (2017)","DOI":"10.1109\/WACV.2017.111"},{"key":"2280_CR15","doi-asserted-by":"crossref","unstructured":"Perreault, H., Bilodeau, G.-A., Saunier, N., H\u00e9ritier, M.: Spotnet: self-attention multi-task network for object detection. In: 2020 17th Conference on Computer and Robot Vision (CRV), pp. 230\u2013237. IEEE (2020)","DOI":"10.1109\/CRV50864.2020.00038"},{"key":"2280_CR16","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. Arxiv preprint arXiv:1511.07122 (2015)"},{"key":"2280_CR17","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 764\u2013773 (2017)","DOI":"10.1109\/ICCV.2017.89"},{"key":"2280_CR18","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, pp. 2017\u20132025 (2015)"},{"key":"2280_CR19","doi-asserted-by":"crossref","unstructured":"Wang, X., Xiao, T., Jiang, Y., Shao, S., Sun, J., Shen, C.: Repulsion loss: detecting pedestrians in a crowd. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7774\u20137783 (2018)","DOI":"10.1109\/CVPR.2018.00811"},{"key":"2280_CR20","doi-asserted-by":"crossref","unstructured":"Pang, Y., Xie, J., Khan, M.H., Anwer, R.M., Khan, F.S., Shao, L.: Mask-guided attention network for occluded pedestrian detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4967\u20134975 (2019)","DOI":"10.1109\/ICCV.2019.00507"},{"issue":"4","key":"2280_CR21","first-page":"985","volume":"20","author":"J Li","year":"2017","unstructured":"Li, J., Liang, X., Shen, S., Xu, T., Feng, J., Yan, S.: Scale-aware fast R-CNN for pedestrian detection. IEEE Trans. Multimedia 20(4), 985\u2013996 (2017)","journal-title":"IEEE Trans. Multimedia"},{"key":"2280_CR22","doi-asserted-by":"crossref","unstructured":"Singh, B., Davis, L.S.: An analysis of scale invariance in object detection snip. In: Proceedings of the IEEE Conference On Computer Vision and Pattern Recognition, pp. 3578\u20133587 (2018)","DOI":"10.1109\/CVPR.2018.00377"},{"key":"2280_CR23","unstructured":"Singh, B., Najibi, M., Davis, L.S.: Sniper: Efficient multi-scale training. In: Advances in Neural Information Processing Systems, pp. 9310\u20139320 (2018)"},{"key":"2280_CR24","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, Y., Wang, S., Liang, T., Zhao, Q., Tang, Z., Ling, H.: CBNet: a novel composite backbone network architecture for object detection. In: Association for the Advancement of Artificial Intelligence (AAAI), pp. 11653\u201311660 (2020)","DOI":"10.1609\/aaai.v34i07.6834"},{"key":"2280_CR25","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade r-cnn: Delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154\u20136162 (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"2280_CR26","doi-asserted-by":"crossref","unstructured":"He, K., Girshick, R., Doll\u00e1r, P.: Rethinking imagenet pre-training. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4918\u20134927 (2019)","DOI":"10.1109\/ICCV.2019.00502"},{"key":"2280_CR27","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"2280_CR28","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263\u20137271 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"2280_CR29","unstructured":"Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. Arxiv preprint arXiv:1804.02767 (2018)"},{"key":"2280_CR30","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"2280_CR31","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot multibox detector. In: European Conference on Computer Vision, pp. 21\u201337. Springer (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"2280_CR32","doi-asserted-by":"crossref","unstructured":"Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734\u2013750 (2018)","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"2280_CR33","doi-asserted-by":"crossref","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: Centernet: keypoint triplets for object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6569\u20136578 (2019)","DOI":"10.1109\/ICCV.2019.00667"},{"key":"2280_CR34","doi-asserted-by":"crossref","unstructured":"Liu, W., Liao, S., Ren, W., Hu, W., Yu, Y.: High-level semantic feature detection: a new perspective for pedestrian detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5187\u20135196 (2019)","DOI":"10.1109\/CVPR.2019.00533"},{"key":"2280_CR35","doi-asserted-by":"crossref","unstructured":"Song, T., Sun, L., Xie, D., Sun, H., Pu, S.: Small-scale pedestrian detection based on topological line localization and temporal feature aggregation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 536\u2013551 (2018)","DOI":"10.1007\/978-3-030-01234-2_33"},{"key":"2280_CR36","doi-asserted-by":"publisher","first-page":"7389","DOI":"10.1109\/TIP.2020.3002345","volume":"29","author":"T Kong","year":"2020","unstructured":"Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: FoveaBox: beyound anchor-based object detection. IEEE Trans. Image Process. 29, 7389\u20137398 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"2280_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, L., Lin, L., Liang, X., He, K.: Is faster R-CNN doing well for pedestrian detection? In: European Conference on Computer Vision, pp. 443\u2013457. Springer (2016)","DOI":"10.1007\/978-3-319-46475-6_28"},{"key":"2280_CR38","doi-asserted-by":"crossref","unstructured":"Wang, S., Cheng, J., Liu, H., Tang, M.: Pcn: Part and context information for pedestrian detection with CNNs. Arxiv preprint arXiv:1804.04483 (2018)","DOI":"10.5244\/C.31.34"},{"key":"2280_CR39","doi-asserted-by":"crossref","unstructured":"Lin, C., Lu, J., Wang, G., Zhou, J.: Graininess-aware deep feature learning for pedestrian detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 732\u2013747 (2018)","DOI":"10.1007\/978-3-030-01240-3_45"},{"key":"2280_CR40","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492\u20131500 (2017)","DOI":"10.1109\/CVPR.2017.634"},{"key":"2280_CR41","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast r-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"issue":"4","key":"2280_CR42","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1109\/TPAMI.2011.155","volume":"34","author":"P Dollar","year":"2011","unstructured":"Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 743\u2013761 (2011)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2280_CR43","doi-asserted-by":"crossref","unstructured":"Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp. 886\u2013893. IEEE (2005)","DOI":"10.1109\/CVPR.2005.177"},{"key":"2280_CR44","doi-asserted-by":"crossref","unstructured":"Szeliski, R.: Computer Vision: Algorithms and Applications. Springer (2010)","DOI":"10.1007\/978-1-84882-935-0"},{"key":"2280_CR45","unstructured":"Kanade, T.: Three-Dimensional Machine Vision, vol. 21. Springer (2012)"},{"key":"2280_CR46","unstructured":"Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586\u2013591. IEEE Computer Society (1991)"},{"key":"2280_CR47","unstructured":"Sebe, N., Cohen, I., Garg, A., Huang, T.S.: Machine Learning in Computer Vision, vol. 29. Springer (2005)"},{"issue":"12","key":"2280_CR48","doi-asserted-by":"publisher","first-page":"1805","DOI":"10.1016\/S0167-8655(03)00005-9","volume":"24","author":"J Yang","year":"2003","unstructured":"Yang, J., Liu, L., Jiang, T., Fan, Y.: A modified Gabor filter design method for fingerprint image enhancement. Pattern Recogn. Lett. 24(12), 1805\u20131817 (2003)","journal-title":"Pattern Recogn. Lett."},{"key":"2280_CR49","doi-asserted-by":"crossref","unstructured":"Viola, P., Jones, M.: Robust real-time face detection. In: Null, p. 747. IEEE (2001)","DOI":"10.1109\/ICCV.2001.937709"},{"key":"2280_CR50","doi-asserted-by":"crossref","unstructured":"Wojek, C., Schiele, B.: A performance evaluation of single and multi-feature people detection. In: Joint Pattern Recognition Symposium, pp. 82\u201391. Springer (2008)","DOI":"10.1007\/978-3-540-69321-5_9"},{"key":"2280_CR51","doi-asserted-by":"crossref","unstructured":"Marin, J., V\u00e1zquez, D., L\u00f3pez, A.M., Amores, J., Leibe, B.: Random forests of local experts for pedestrian detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2592\u20132599 (2013)","DOI":"10.1109\/ICCV.2013.322"},{"issue":"8","key":"2280_CR52","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1109\/TPAMI.2014.2300479","volume":"36","author":"P Doll\u00e1r","year":"2014","unstructured":"Doll\u00e1r, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1532\u20131545 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2280_CR53","doi-asserted-by":"crossref","unstructured":"Zhang, S., Bauckhage, C., Cremers, A.B.: Informed haar-like features improve pedestrian detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 947\u2013954 (2014)","DOI":"10.1109\/CVPR.2014.126"},{"issue":"6088","key":"2280_CR54","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533\u2013536 (1986)","journal-title":"Nature"},{"key":"2280_CR55","unstructured":"Fei-Fei Li, R.K., Danfei, X.: CNN architectures. http:\/\/cs231n.stanford.edu\/slides\/2020\/lecture_9.pdf. Accessed 1 October 2020"},{"key":"2280_CR56","doi-asserted-by":"publisher","first-page":"10059","DOI":"10.1109\/ACCESS.2016.2639543","volume":"4","author":"HM Bui","year":"2016","unstructured":"Bui, H.M., Lech, M., Cheng, E., Neville, K., Burnett, I.S.: Object recognition using deep convolutional features transformed by a recursive network structure. IEEE Access 4, 10059\u201310066 (2016)","journal-title":"IEEE Access"},{"issue":"4","key":"2280_CR57","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1049\/ip-vis:19941301","volume":"141","author":"R Vaillant","year":"1994","unstructured":"Vaillant, R., Monrocq, C., Le Cun, Y.: Original approach for the localisation of objects in images. IEE Proc.-Vis., Image Signal Process. 141(4), 245\u2013250 (1994)","journal-title":"IEE Proc.-Vis., Image Signal Process."},{"issue":"2","key":"2280_CR58","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","volume":"104","author":"JR Uijlings","year":"2013","unstructured":"Uijlings, J.R., Van De Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154\u2013171 (2013)","journal-title":"Int. J. Comput. Vis."},{"key":"2280_CR59","unstructured":"Baars, B., Gage, N.M.: Fundamentals of Cognitive Neuroscience: A Beginner's Guide. Academic Press (2013)"},{"key":"2280_CR60","unstructured":"Gage, N.M., Baars, B.: Fundamentals of Cognitive Neuroscience: A Beginner's Guide. Academic Press (2018)"},{"key":"2280_CR61","unstructured":"Schieber, M., Squire, L., Baker, J.: Descending control of movement. In: Fundamental Neuroscience, 3rd edn. Academic Press (2008)"},{"key":"2280_CR62","unstructured":"Neuroscience, F.: In: Squire, L.R., Bloom, F.E., McConnell, S.K., Roberts, J.L., Spitzer, N.C., Zigmond, M.J. (eds.) Fundamental Neuroscience, 2nd edn.. Elsevier Science, San Diego (2003)"},{"key":"2280_CR63","doi-asserted-by":"crossref","unstructured":"Zhang, S., Benenson, R., Schiele, B.: Citypersons: a diverse dataset for pedestrian detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213\u20133221 (2017)","DOI":"10.1109\/CVPR.2017.474"},{"key":"2280_CR64","unstructured":"Kingma, D.P., Ba, J.A.: A method for stochastic optimization. Arxiv 434, 2014 (2019). arXiv:1412.6980"},{"issue":"4","key":"2280_CR65","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1109\/TPAMI.2017.2700460","volume":"40","author":"S Zhang","year":"2017","unstructured":"Zhang, S., Benenson, R., Omran, M., Hosang, J., Schiele, B.: Towards reaching human performance in pedestrian detection. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 973\u2013986 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2280_CR66","doi-asserted-by":"crossref","unstructured":"Zhang, S., Benenson, R., Omran, M., Hosang, J., Schiele, B.: How far are we from solving pedestrian detection? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1259\u20131267 (2016)","DOI":"10.1109\/CVPR.2016.141"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-021-02280-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-021-02280-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-021-02280-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T17:27:14Z","timestamp":1725643634000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-021-02280-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,17]]},"references-count":66,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["2280"],"URL":"https:\/\/doi.org\/10.1007\/s00371-021-02280-6","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,17]]},"assertion":[{"value":"5 August 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}