{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T15:19:39Z","timestamp":1782400779081,"version":"3.54.5"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T00:00:00Z","timestamp":1689292800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T00:00:00Z","timestamp":1689292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100007847","name":"Natural Science Foundation of Jilin Province","doi-asserted-by":"publisher","award":["62062015"],"award-info":[{"award-number":["62062015"]}],"id":[{"id":"10.13039\/100007847","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s00371-023-03002-w","type":"journal-article","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T08:02:01Z","timestamp":1689321721000},"page":"2975-2986","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["EBStereo: edge-based loss function for real-time stereo matching"],"prefix":"10.1007","volume":"40","author":[{"given":"Weijie","family":"Bi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongliu","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shenglian","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,14]]},"reference":[{"issue":"1","key":"3002_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1023\/A:1014573219977","volume":"47","author":"D Scharstein","year":"2002","unstructured":"Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7\u201342 (2002)","journal-title":"Int. J. Comput. Vis."},{"key":"3002_CR2","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354\u20133361. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"issue":"11","key":"3002_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-019-2803-x","volume":"63","author":"W Bao","year":"2020","unstructured":"Bao, W., Wang, W., Xu, Y., Guo, Y., Hong, S., Zhang, X.: Instereo2k: a large real dataset for stereo matching in indoor scenes. Sci. China Inf. Sci. 63(11), 1\u201311 (2020)","journal-title":"Sci. China Inf. Sci."},{"key":"3002_CR4","doi-asserted-by":"crossref","unstructured":"Scharstein, D., Hirschm\u00fcller, H., Kitajima, Y., Krathwohl, G., Ne\u0161i\u0107, N., Wang, X,, Westling, P.: High-resolution stereo datasets with subpixel-accurate ground truth. In: German Conference on Pattern Recognition, pp. 31\u201342. Springer (2014)","DOI":"10.1007\/978-3-319-11752-2_3"},{"key":"3002_CR5","doi-asserted-by":"crossref","unstructured":"Sivaraman, S., Trivedi, M.M.: A review of recent developments in vision-based vehicle detection. In: 2013 IEEE Intelligent Vehicles Symposium (IV), pp. 310\u2013315. IEEE (2013)","DOI":"10.1109\/IVS.2013.6629487"},{"key":"3002_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118573","volume":"211","author":"Y Tang","year":"2023","unstructured":"Tang, Y., Zhou, H., Wang, H., Zhang, Y.: Fruit detection and positioning technology for a Camellia oleifera C. Abel orchard based on improved yolov4-tiny model and binocular stereo vision. Expert Syst. Appl. 211, 118573 (2023)","journal-title":"Expert Syst. Appl."},{"key":"3002_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106107","volume":"184","author":"G Lin","year":"2021","unstructured":"Lin, G., Tang, Y., Zou, X., Wang, C.: Three-dimensional reconstruction of guava fruits and branches using instance segmentation and geometry analysis. Comput. Electron. Agric. 184, 106107 (2021)","journal-title":"Comput. Electron. Agric."},{"key":"3002_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.engstruct.2022.115158","volume":"274","author":"Y Tang","year":"2023","unstructured":"Tang, Y., Huang, Z., Chen, Z., Chen, M., Zhou, H., Zhang, H., Sun, J.: Novel visual crack width measurement based on backbone double-scale features for improved detection automation. Eng. Struct. 274, 115158 (2023)","journal-title":"Eng. Struct."},{"key":"3002_CR9","doi-asserted-by":"crossref","unstructured":"Kolmogorov, V., Zabih, R.: Computing visual correspondence with occlusions using graph cuts. In: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, vol. 2, pp. 508\u2013515. IEEE (2001)","DOI":"10.1109\/ICCV.2001.937668"},{"issue":"7","key":"3002_CR10","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1109\/TPAMI.2003.1206509","volume":"25","author":"J Sun","year":"2003","unstructured":"Sun, J., Zheng, N.-N., Shum, H.-Y.: Stereo matching using belief propagation. IEEE Trans. Pattern Anal. Mach. Intell. 25(7), 787\u2013800 (2003)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"3002_CR11","doi-asserted-by":"publisher","first-page":"1068","DOI":"10.1109\/TPAMI.2007.1043","volume":"29","author":"Y Deng","year":"2007","unstructured":"Deng, Y., Yang, Q., Lin, X., Tang, X.: Stereo correspondence with occlusion handling in a symmetric patch-based graph-cuts model. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1068\u20131079 (2007)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"3002_CR12","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1007\/s00034-009-9130-7","volume":"28","author":"J-C Yoo","year":"2009","unstructured":"Yoo, J.-C., Han, T.H.: Fast normalized cross-correlation. Circ. Syst. Signal Process. 28(6), 819\u2013843 (2009)","journal-title":"Circ. Syst. Signal Process."},{"issue":"7","key":"3002_CR13","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1109\/TCSVT.2009.2020478","volume":"19","author":"K Zhang","year":"2009","unstructured":"Zhang, K., Lu, J., Lafruit, G.: Cross-based local stereo matching using orthogonal integral images. IEEE Trans. Circuits Syst. Video Technol. 19(7), 1073\u20131079 (2009)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"2","key":"3002_CR14","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1109\/TPAMI.2007.1166","volume":"30","author":"H Hirschmuller","year":"2007","unstructured":"Hirschmuller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 328\u2013341 (2007)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3002_CR15","doi-asserted-by":"crossref","unstructured":"Humenberger, M., Engelke, T., Kubinger, W.: A census-based stereo vision algorithm using modified semi-global matching and plane fitting to improve matching quality. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, pp. 77\u201384. IEEE (2010)","DOI":"10.1109\/CVPRW.2010.5543769"},{"key":"3002_CR16","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/s00371-018-1491-0","volume":"35","author":"Y Li","year":"2019","unstructured":"Li, Y., Zhang, J., Zhong, Y., Wang, M.: An efficient stereo matching based on fragment matching. Vis. Comput. 35, 257\u2013269 (2019)","journal-title":"Vis. Comput."},{"key":"3002_CR17","doi-asserted-by":"crossref","unstructured":"Zbontar, J., LeCun, Y.: Computing the stereo matching cost with a convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1592\u20131599 (2015)","DOI":"10.1109\/CVPR.2015.7298767"},{"key":"3002_CR18","doi-asserted-by":"crossref","unstructured":"Mayer, N., Ilg, E., Hausser, P., Fischer, P., Cremers, D., Dosovitskiy, A., Brox, T.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4040\u20134048 (2016)","DOI":"10.1109\/CVPR.2016.438"},{"key":"3002_CR19","doi-asserted-by":"crossref","unstructured":"Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5410\u20135418 (2018)","DOI":"10.1109\/CVPR.2018.00567"},{"key":"3002_CR20","doi-asserted-by":"crossref","unstructured":"Pang, J., Sun, W., Ren, J.S., Yang, C., Yan, Q.: Cascade residual learning: a two-stage convolutional neural network for stereo matching. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 887\u2013895 (2017)","DOI":"10.1109\/ICCVW.2017.108"},{"key":"3002_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, F., Prisacariu, V., Yang, R., Torr, P.H.: Ga-net: Guided aggregation net for end-to-end stereo matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 185\u2013194 (2019)","DOI":"10.1109\/CVPR.2019.00027"},{"key":"3002_CR22","doi-asserted-by":"crossref","unstructured":"Kendall, A., Martirosyan, H., Dasgupta, S., Henry, P., Kennedy, R., Bachrach, A., Bry, A: End-to-end learning of geometry and context for deep stereo regression. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 66\u201375 (2017)","DOI":"10.1109\/ICCV.2017.17"},{"key":"3002_CR23","doi-asserted-by":"crossref","unstructured":"Xu, G., Cheng, J., Guo, P., Yang, X.: Attention concatenation volume for accurate and efficient stereo matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12981\u201312990 (2022)","DOI":"10.1109\/CVPR52688.2022.01264"},{"key":"3002_CR24","doi-asserted-by":"crossref","unstructured":"Shamsafar, F., Woerz, S., Rahim, R., Zell, A.: Mobilestereonet: towards lightweight deep networks for stereo matching. In: Proceedings of the Ieee\/cvf Winter Conference on Applications of Computer Vision, pp. 2417\u20132426 (2022)","DOI":"10.1109\/WACV51458.2022.00075"},{"key":"3002_CR25","doi-asserted-by":"crossref","unstructured":"Liu, B., Yu, H., Long, Y.: Local similarity pattern and cost self-reassembling for deep stereo matching networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 1647\u20131655 (2022)","DOI":"10.1609\/aaai.v36i2.20056"},{"issue":"11","key":"3002_CR26","doi-asserted-by":"publisher","first-page":"3881","DOI":"10.1007\/s00371-021-02228-w","volume":"38","author":"X Li","year":"2022","unstructured":"Li, X., Fan, Y., Lv, G., Ma, H.: Area-based correlation and non-local attention network for stereo matching. Vis. Comput. 38(11), 3881\u20133895 (2022)","journal-title":"Vis. Comput."},{"key":"3002_CR27","doi-asserted-by":"crossref","unstructured":"Findeisen, M., Hirtz, G.: Trinocular spherical stereo vision for indoor surveillance. In: 2014 Canadian Conference on Computer and Robot Vision, pp. 364\u2013370. IEEE (2014)","DOI":"10.1109\/CRV.2014.56"},{"key":"3002_CR28","unstructured":"Xu, B., Xu, Y., Yang, X., Jia, W., Guo, Y.: x Bilateral grid learning for stereo matching networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12497\u201312506 (2018)"},{"key":"3002_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Wu, Y.N., Zhu, S.-C.: Interpretable convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8827\u20138836 (2018)","DOI":"10.1109\/CVPR.2018.00920"},{"key":"3002_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":"3002_CR31","doi-asserted-by":"crossref","unstructured":"Xu, H., Zhang, J.: Aanet: adaptive aggregation network for efficient stereo matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1959\u20131968 (2020)","DOI":"10.1109\/CVPR42600.2020.00203"},{"issue":"4","key":"3002_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3072959.3073592","volume":"36","author":"M Gharbi","year":"2017","unstructured":"Gharbi, M., Chen, J., Barron, J.T., Hasinoff, S.W., Durand, F.: Deep bilateral learning for real-time image enhancement. ACM Trans. Graph. (TOG) 36(4), 1\u201312 (2017)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"3002_CR33","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Van Der\u00a0Smagt, P., Cremers, D., Brox, T.: Flownet: learning optical flow with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2758\u20132766 (2015)","DOI":"10.1109\/ICCV.2015.316"},{"key":"3002_CR34","doi-asserted-by":"crossref","unstructured":"Guo X, Yang K, Yang W, Wang X, Li H: Group-wise correlation stereo network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3273\u20133282 (2019)","DOI":"10.1109\/CVPR.2019.00339"},{"key":"3002_CR35","doi-asserted-by":"crossref","unstructured":"Khamis S, Fanello S, Rhemann C, Kowdle A, Valentin J, Izadi S: Stereonet: guided hierarchical refinement for real-time edge-aware depth prediction. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 573\u2013590 (2018)","DOI":"10.1007\/978-3-030-01267-0_35"},{"key":"3002_CR36","doi-asserted-by":"crossref","unstructured":"Duggal, S., Wang, S., Ma, W.-C., Hu, R., Urtasun, R.: Deeppruner: learning efficient stereo matching via differentiable patchmatch. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4384\u20134393 (2019)","DOI":"10.1109\/ICCV.2019.00448"},{"issue":"3","key":"3002_CR37","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1145\/1276377.1276506","volume":"26","author":"J Chen","year":"2007","unstructured":"Chen, J., Paris, S., Durand, F.: Real-time edge-aware image processing with the bilateral grid. ACM Trans. Graph. (TOG) 26(3), 103 (2007)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"3002_CR38","doi-asserted-by":"crossref","unstructured":"Paris, S., Durand, F.: A fast approximation of the bilateral filter using a signal processing approach. In: European Conference on Computer Vision, pp. 568\u2013580. Springer (2006)","DOI":"10.1007\/11744085_44"},{"issue":"6","key":"3002_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2980179.2982423","volume":"35","author":"J Chen","year":"2016","unstructured":"Chen, J., Adams, A., Wadhwa, N., Hasinoff, S.W.: Bilateral guided upsampling. ACM Trans. Graph. (TOG) 35(6), 1\u20138 (2016)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"3002_CR40","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Ren, W., Cao, X., Wang, T., Jia, X.: Ultra-high-definition image hdr reconstruction via collaborative bilateral learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4449\u20134458 (2021)","DOI":"10.1109\/ICCV48922.2021.00441"},{"key":"3002_CR41","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Ren, W., Cao, X., Hu, X., Wang, T., Song, F., Jia, X.: Ultra-high-definition image dehazing via multi-guided bilateral learning. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16180\u201316189. IEEE (2021)","DOI":"10.1109\/CVPR46437.2021.01592"},{"key":"3002_CR42","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1109\/LSP.2021.3066125","volume":"28","author":"Q Xu","year":"2021","unstructured":"Xu, Q., Wang, L., Wang, Y., Sheng, W., Deng, X.: Deep bilateral learning for stereo image super-resolution. IEEE Signal Process. Lett. 28, 613\u2013617 (2021)","journal-title":"IEEE Signal Process. Lett."},{"key":"3002_CR43","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":"3002_CR44","doi-asserted-by":"crossref","unstructured":"Menze, M., Heipke, C., Geiger, A.: Joint 3D estimation of vehicles and scene flow. In: ISPRS Annals of the Photogrammetry, Remote Densing and Spatial Information Sciences, vol. 2, p. 427 (2015)","DOI":"10.5194\/isprsannals-II-3-W5-427-2015"},{"key":"3002_CR45","doi-asserted-by":"crossref","unstructured":"Yang, G., Zhao, H., Shi, J., Deng, Z., Jia, J.: Segstereo: exploiting semantic information for disparity estimation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 636\u2013651 (2018)","DOI":"10.1007\/978-3-030-01234-2_39"},{"key":"3002_CR46","doi-asserted-by":"crossref","unstructured":"Song, X., Zhao, X., Hu, H., Fang, L.: Edgestereo: a context integrated residual pyramid network for stereo matching. In: Asian conference on computer vision, pp 20\u201335. Springer (2018)","DOI":"10.1007\/978-3-030-20873-8_2"},{"key":"3002_CR47","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Chen, Y., Bai, X., Yu, S., Yu, K., Li, Z., Yang, K.: Adaptive unimodal cost volume filtering for deep stereo matching. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 12926\u201312934 (2020)","DOI":"10.1609\/aaai.v34i07.6991"},{"key":"3002_CR48","doi-asserted-by":"crossref","unstructured":"Seif, G., Androutsos, D.: Edge-based loss function for single image super-resolution. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1468\u20131472. IEEE (2018)","DOI":"10.1109\/ICASSP.2018.8461664"},{"key":"3002_CR49","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","volume":"6","author":"J Canny","year":"1986","unstructured":"Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679\u2013698 (1986)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3002_CR50","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"3002_CR51","first-page":"22158","volume":"33","author":"X Cheng","year":"2020","unstructured":"Cheng, X., Zhong, Y., Harandi, M., Dai, Y., Chang, X., Li, H., Drummond, T., Ge, Z.: Hierarchical neural architecture search for deep stereo matching. Adv. Neural. Inf. Process. Syst. 33, 22158\u201322169 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3002_CR52","doi-asserted-by":"crossref","unstructured":"Wang, Q., Shi, S., Zheng, S., Zhao, K., Chu, X.: Fadnet: a fast and accurate network for disparity estimation. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 101\u2013107. IEEE (2020)","DOI":"10.1109\/ICRA40945.2020.9197031"},{"issue":"2","key":"3002_CR53","doi-asserted-by":"publisher","first-page":"2305","DOI":"10.1109\/LRA.2022.3143895","volume":"7","author":"K Shankar","year":"2022","unstructured":"Shankar, K., Tjersland, M., Ma, J., Stone, K., Bajracharya, M.: A learned stereo depth system for robotic manipulation in homes. IEEE Robot. Autom. Lett. 7(2), 2305\u20132312 (2022)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"3002_CR54","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wang, Z., Wang, Q., Zhang, J., Wei, G., Chu, X.: Ednet: efficient disparity estimation with cost volume combination and attention-based spatial residual. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5433\u20135442 (2021)","DOI":"10.1109\/CVPR46437.2021.00539"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-03002-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-023-03002-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-03002-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T04:21:26Z","timestamp":1729743686000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-023-03002-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,14]]},"references-count":54,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["3002"],"URL":"https:\/\/doi.org\/10.1007\/s00371-023-03002-w","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,14]]},"assertion":[{"value":"24 June 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 2023","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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}