{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T18:24:08Z","timestamp":1784658248841,"version":"3.55.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,1,20]],"date-time":"2024-01-20T00:00:00Z","timestamp":1705708800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,20]],"date-time":"2024-01-20T00:00:00Z","timestamp":1705708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Chinese Academy of Sciences-Youth Innovation Promotion Association","award":["2020220"],"award-info":[{"award-number":["2020220"]}]},{"DOI":"10.13039\/501100015286","name":"Hebei Provincial Key Research Projects","doi-asserted-by":"publisher","award":["2019B010155003"],"award-info":[{"award-number":["2019B010155003"]}],"id":[{"id":"10.13039\/501100015286","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013142","name":"Key Research and Development Project of Hainan Province","doi-asserted-by":"publisher","award":["20210201078GX"],"award-info":[{"award-number":["20210201078GX"]}],"id":[{"id":"10.13039\/501100013142","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s00371-023-03229-7","type":"journal-article","created":{"date-parts":[[2024,1,20]],"date-time":"2024-01-20T15:01:47Z","timestamp":1705762907000},"page":"8141-8153","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Defocus blur detection via adaptive cross-level feature fusion and refinement"],"prefix":"10.1007","volume":"40","author":[{"given":"Zijian","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peiyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haitao","family":"Nie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongbo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,20]]},"reference":[{"key":"3229_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-022-02614-y","author":"R Cai","year":"2022","unstructured":"Cai, R., Fang, M.: Blind image quality assessment by simulating the visual cortex. Vis. Comput. (2022). https:\/\/doi.org\/10.1007\/s00371-022-02614-y","journal-title":"Vis. Comput."},{"key":"3229_CR2","doi-asserted-by":"publisher","first-page":"1823","DOI":"10.1007\/s00371-019-01778-4","volume":"36","author":"Z Liu","year":"2020","unstructured":"Liu, Z., Xiang, Q., Tang, J., Wang, Y., Zhao, P.: Robust salient object detection for RGB images. Vis. Comput. 36, 1823\u20131835 (2020). https:\/\/doi.org\/10.1007\/s00371-019-01778-4","journal-title":"Vis. Comput."},{"issue":"Feb.","key":"3229_CR3","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.jvcir.2016.01.002","volume":"35","author":"X Zhang","year":"2016","unstructured":"Zhang, X., Wang, R., Jiang, X., Wang, W., Gao, W.: Spatially variant defocus blur map estimation and deblurring from a single image. J. Vis. Commun. Image Represent. 35(Feb.), 257\u2013264 (2016). https:\/\/doi.org\/10.1016\/j.jvcir.2016.01.002","journal-title":"J. Vis. Commun. Image Represent."},{"key":"3229_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-022-02713-w","author":"Z Jiang","year":"2022","unstructured":"Jiang, Z., Zhang, Z., Yu, Y., Liu, R.: Publisher Correction: Bilevel modeling investigated generative adversarial framework for image restoration. Vis. Comput. (2022). https:\/\/doi.org\/10.1007\/s00371-022-02713-w","journal-title":"Vis. Comput."},{"key":"3229_CR5","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2662206","author":"K Zhang","year":"2017","unstructured":"Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. (2017). https:\/\/doi.org\/10.1109\/TIP.2017.2662206","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"3229_CR6","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2016","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295\u2013307 (2016). https:\/\/doi.org\/10.1109\/TPAMI.2015.2439281","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3229_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-022-02499-x","author":"Z Liu","year":"2022","unstructured":"Liu, Z., Liu, J.: Hypergraph attentional convolutional neural network for salient object detection. Vis. Comput. (2022). https:\/\/doi.org\/10.1007\/s00371-022-02499-x","journal-title":"Vis. Comput."},{"key":"3229_CR8","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.patcog.2017.11.007","volume":"76","author":"P Li","year":"2018","unstructured":"Li, P., Wang, D., Wang, L., Lu, H.: Deep visual tracking: review and experimental comparison. Pattern Recognit. 76, 323\u2013338 (2018)","journal-title":"Pattern Recognit."},{"key":"3229_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2008.4587465","author":"R Liu","year":"2008","unstructured":"Liu, R., Li, Z., Jia, J.: Image partial blur detection and classification. IEEE Conf. Comput. Vis. Pattern Recognit. (2008). https:\/\/doi.org\/10.1109\/CVPR.2008.4587465","journal-title":"IEEE Conf. Comput. Vis. Pattern Recognit."},{"issue":"10","key":"3229_CR10","doi-asserted-by":"publisher","first-page":"2220","DOI":"10.1109\/TCYB.2015.2472478","volume":"46","author":"Y Pang","year":"2017","unstructured":"Pang, Y., Zhu, H., Li, X., Li, X., Pang, Y.: Classifying discriminative features for blur detection. IEEE Trans. Cybern. 46(10), 2220\u20132227 (2017). https:\/\/doi.org\/10.1109\/TCYB.2015.2472478","journal-title":"IEEE Trans. Cybern."},{"issue":"7","key":"3229_CR11","doi-asserted-by":"publisher","first-page":"3141","DOI":"10.1109\/TIP.2016.2555702","volume":"25","author":"E Saad","year":"2016","unstructured":"Saad, E., Hirakawa, K.: Defocus blur-invariant scale-space feature extractions. IEEE Trans. Image Process. 25(7), 3141\u20133156 (2016). https:\/\/doi.org\/10.1109\/TIP.2016.2555702","journal-title":"IEEE Trans. Image Process."},{"key":"3229_CR12","doi-asserted-by":"crossref","unstructured":"Su, B., Lu, S., Tan, C.L.: Blurred image region detection and classifification. ACM International Conference on Multimedia, pp 1397\u20131400 (2011)","DOI":"10.1145\/2072298.2072024"},{"issue":"6","key":"3229_CR13","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1007\/s11760-012-0381-6","volume":"7","author":"J Zhao","year":"2013","unstructured":"Zhao, J., Feng, H., Xu, Z., Li, Q., Tao, X.: Automatic blur region segmen tation approach using image matting. Signal Image Video Process. 7(6), 1173\u20131181 (2013). https:\/\/doi.org\/10.1007\/s11760-012-0381-6","journal-title":"Signal Image Video Process."},{"issue":"12","key":"3229_CR14","doi-asserted-by":"publisher","first-page":"4879","DOI":"10.1109\/TIP.2013.2279316","volume":"22","author":"X Zhu","year":"2013","unstructured":"Zhu, X., Cohen, S., Schiller, S., Milanfar, P.: Estimating spatially varying defocus blur from a single image. IEEE Trans. Image Process. 22(12), 4879\u20134891 (2013). https:\/\/doi.org\/10.1109\/TIP.2013.2279316","journal-title":"IEEE Trans. Image Process."},{"key":"3229_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2016.2611608","author":"C Tang","year":"2016","unstructured":"Tang, C., Wu, J., Hou, Y., Wang, P., Li, W.: A spectral and spatial approach of coarse-to-fine blurred image region detection. IEEE Signal Process. Lett. (2016). https:\/\/doi.org\/10.1109\/LSP.2016.2611608","journal-title":"IEEE Signal Process. Lett."},{"key":"3229_CR16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.379","author":"J Shi","year":"2014","unstructured":"Shi, J., Xu, L., Jia, J.: Discriminative blur detection features. IEEE Conf. Comput. Vis. Pattern Recognit. (2014). https:\/\/doi.org\/10.1109\/CVPR.2014.379","journal-title":"IEEE Conf. Comput. Vis. Pattern Recognit."},{"key":"3229_CR17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.145","author":"Y Zhang","year":"2013","unstructured":"Zhang, Y., Hirakawa, K.: Blur processing using double discrete wavelet transform. IEEE Conf. Comput. Vis. Pattern Recognit. (2013). https:\/\/doi.org\/10.1109\/CVPR.2013.145","journal-title":"IEEE Conf. Comput. Vis. Pattern Recognit."},{"issue":"3","key":"3229_CR18","doi-asserted-by":"publisher","first-page":"934","DOI":"10.1109\/TIP.2011.2169974","volume":"21","author":"CT Vu","year":"2012","unstructured":"Vu, C.T., Phan, T.D., Chandler, D.M.: $${ S}_{3}$$: a spectral and spatial measure of local perceived sharpness in natural images. IEEE Trans. Image Process. 21(3), 934\u2013945 (2012). https:\/\/doi.org\/10.1109\/TIP.2011.2169974","journal-title":"IEEE Trans. Image Process."},{"issue":"12","key":"3229_CR19","doi-asserted-by":"publisher","first-page":"4879","DOI":"10.1109\/TIP.2013.2279316","volume":"22","author":"X Zhu","year":"2013","unstructured":"Zhu, X., Cohen, S., Schiller, S., et al.: Estimating spatially varying defocus blur from a single image. IEEE Trans. Image Process. 22(12), 4879\u20134891 (2013). https:\/\/doi.org\/10.1109\/TIP.2013.2279316","journal-title":"IEEE Trans. Image Process."},{"key":"3229_CR20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.71","author":"SA Golestaneh","year":"2017","unstructured":"Golestaneh, S.A., Karam, L.J.: Spatially-varying blur detection based on multiscale fused and sorted transform coeffificients of gradient magnitudes. IEEE Conf. Comput. Vis. Pattern Recognit. (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.71","journal-title":"IEEE Conf. Comput. Vis. Pattern Recognit."},{"issue":"11","key":"3229_CR21","doi-asserted-by":"publisher","first-page":"1652","DOI":"10.1109\/LSP.2016.2611608","volume":"23","author":"C Tang","year":"2016","unstructured":"Tang, C., Wu, J., Hou, Y., Wang, P., Li, W.: A spectral and spatial approach of coarse-to-fifine blurred image region detection. IEEE Signal Process. Lett. 23(11), 1652\u20131656 (2016). https:\/\/doi.org\/10.1109\/LSP.2016.2611608","journal-title":"IEEE Signal Process. Lett."},{"issue":"4","key":"3229_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIP.2016.2528042","volume":"25","author":"Y Xin","year":"2016","unstructured":"Xin, Y., Eramian, M.: LBP-based segmentation of defocus blur. IEEE Trans. Image Process. 25(4), 1\u20131 (2016). https:\/\/doi.org\/10.1109\/TIP.2016.2528042","journal-title":"IEEE Trans. Image Process."},{"key":"3229_CR23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.295","author":"J Park","year":"2017","unstructured":"Park, J., Tai, Y.W., Cho, D., Kweon, I.S.: A unified approach of multi-scale deep and hand-crafted features for defocus estimation. IEEE Comput. Soc. (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.295","journal-title":"IEEE Comput. Soc."},{"key":"3229_CR24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00325","author":"W Zhao","year":"2018","unstructured":"Zhao, W., Zhao, F., Wang, D., Lu, H.: Defocus blur detection via multi-stream bottom-top-bottom fully convolutional network. IEEE Conf. Comput. Vis. Pattern Recognit. (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00325","journal-title":"IEEE Conf. Comput. Vis. Pattern Recognit."},{"key":"3229_CR25","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1109\/TIP.2021.3139243","volume":"31","author":"A Karaali","year":"2022","unstructured":"Karaali, A., Harte, N., Jung, C.R.: Deep multi-scale feature learning for defocus blur estimation. IEEE Trans. Image Process. 31, 1097\u20131106 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"3229_CR26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00911","author":"W Zhao","year":"2019","unstructured":"Zhao, W., Zheng, B., Lin, Q., Lu, H.: Enhancing diversity of defocus blur detectors via cross-ensemble network. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00911","journal-title":"IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR)"},{"key":"3229_CR27","doi-asserted-by":"publisher","first-page":"5155","DOI":"10.1109\/TIP.2018.2847421","volume":"27","author":"K Ma","year":"2018","unstructured":"Ma, K., Fu, H., Liu, T., Wang, Z., Tao, D.: Deep blur mapping: exploiting high-level semantics by deep neural networks. IEEE Trans. Image Process. 27, 5155\u20135166 (2018). https:\/\/doi.org\/10.1109\/TIP.2018.2847421","journal-title":"IEEE Trans. Image Process."},{"key":"3229_CR28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01250","author":"J Lee","year":"2019","unstructured":"Lee, J., Lee, S., Cho, S., Lee, S.: Deep defocus map estimation using domain adaptation. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.01250","journal-title":"IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR)"},{"key":"3229_CR29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00281","author":"C Tang","year":"2019","unstructured":"Tang, C., Zhu, X., Liu, X., Wang, L., Zomaya, A.: DeFusionNET: defocus blur detection via recurrently fusing and refining multi-scale deep features. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00281","journal-title":"IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR)"},{"issue":"7","key":"3229_CR30","doi-asserted-by":"publisher","first-page":"12063","DOI":"10.1609\/aaai.v34i07.6884","volume":"34","author":"C Tang","year":"2020","unstructured":"Tang, C., Liu, X., Zhu, X., Zhu, E., Sun, K., Wang, P., Wang, L., Zomaya, A.: R$$^{2}$$MRF: defocus blur detection via recurrently refining multi-scale residual features. Proc. AAAI Conf. Artif. Intell. 34(7), 12063\u201312070 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i07.6884","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"3229_CR31","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3084101","author":"W Zhao","year":"2021","unstructured":"Zhao, W., Hou, X., He, Y., Lu, H.: Defocus blur detection via boosting diversity of deep ensemble networks. IEEE Trans. Image Process. (2021). https:\/\/doi.org\/10.1109\/TIP.2021.3084101","journal-title":"IEEE Trans. Image Process."},{"key":"3229_CR32","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3065171","author":"J Li","year":"2021","unstructured":"Li, J., Fan, D., Yang, L., Gu, S., Lu, G., Xu, Y., Zhang, D.: Layer-output guided complementary attention learning for image defocus blur detection. IEEE Trans. Image Process. (2021). https:\/\/doi.org\/10.1109\/TIP.2021.3065171","journal-title":"IEEE Trans. Image Process."},{"key":"3229_CR33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00686","author":"W Zhao","year":"2021","unstructured":"Zhao, W., Shang, C., Lu, H.: Self-generated defocus blur detection via dual adversarial discriminators. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.00686","journal-title":"IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR)"},{"key":"3229_CR34","doi-asserted-by":"publisher","first-page":"3494","DOI":"10.1109\/TIP.2022.3171424","volume":"31","author":"Z Jiang","year":"2022","unstructured":"Jiang, Z., Xu, X., Zhang, L., Zhang, C., Foo, S., Zhu, C.: MA-GANet: a multi-attention generative adversarial network for defocus blur detection. IEEE Trans. Image Process. 31, 3494\u20133508 (2022). https:\/\/doi.org\/10.1109\/TIP.2022.3171424","journal-title":"IEEE Trans. Image Process."},{"key":"3229_CR35","doi-asserted-by":"publisher","unstructured":"Zhao, Z., Yang, H., Luo, H.: Hierarchical edge-aware network for defocus blur detection. Complex Intell. Syst. 8, 4265\u20134276 (2022). https:\/\/doi.org\/10.1007\/s40747-022-00711-y","DOI":"10.1007\/s40747-022-00711-y"},{"key":"3229_CR36","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.neucom.2022.06.023","volume":"501","author":"X Lin","year":"2022","unstructured":"Lin, X., Li, H., Cai, Q.: Hierarchical complementary residual attention learning for defocus blur detection. Neurocomputing 501, 88\u2013101 (2022). https:\/\/doi.org\/10.1016\/j.neucom.2022.06.023","journal-title":"Neurocomputing"},{"key":"3229_CR37","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.1109\/TIP.2023.3240856","volume":"32","author":"J Li","year":"2023","unstructured":"Li, J., Liang, B., Lu, X., Li, M., Lu, G., Xu, Y.: From global to local: multi-patch and multi-scale contrastive similarity learning for unsupervised defocus blur detection. IEEE Trans. Image Process. 32, 1158\u20131169 (2023). https:\/\/doi.org\/10.1109\/TIP.2023.3240856","journal-title":"IEEE Trans. Image Process."},{"key":"3229_CR38","doi-asserted-by":"crossref","unstructured":"Abuolaim, A., Brown, M.S.: Defocus Deblurring Using Dual-Pixel Data. European Conference on Computer Vision, Springer, pp 111\u2013126 (2020)","DOI":"10.1007\/978-3-030-58607-2_7"},{"key":"3229_CR39","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"3229_CR40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","journal-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit."},{"key":"3229_CR41","first-page":"12321","volume":"34","author":"J Wei","year":"2020","unstructured":"Wei, J., Wang, S., Huang, Q.: F3net: fusion, feedback and focus for salient object detection. AAAI Conf. Artif. Intell. 34, 12321\u201312328 (2020)","journal-title":"AAAI Conf. Artif. Intell."},{"key":"3229_CR42","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Kai, L., Li, F.F.: Imagenet: a large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition, pp 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"3229_CR43","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.487","author":"D Fan","year":"2017","unstructured":"Fan, D., Cheng, M., Liu, Y., Li, T., Borji, A.: Structure-measure: a new way to evaluate foreground maps. IEEE Int. Conf. Comput. Vis. (ICCV) (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.487","journal-title":"IEEE Int. Conf. Comput. Vis. (ICCV)"},{"key":"3229_CR44","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3319330","author":"K Zhang","year":"2023","unstructured":"Zhang, K., Wang, T., Luo, W., et al.: MC-Blur: a comprehensive benchmark for image deblurring. IEEE Trans. Circuits Syst. Video Technol. (2023). https:\/\/doi.org\/10.1109\/TCSVT.2023.3319330","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"3229_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, K., Luo, W., Zhong, Y., et al.: Deblurring by realistic blurring. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 2737\u20132746 (2020)","DOI":"10.1109\/CVPR42600.2020.00281"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-03229-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-023-03229-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-03229-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T16:14:27Z","timestamp":1730909667000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-023-03229-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,20]]},"references-count":45,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["3229"],"URL":"https:\/\/doi.org\/10.1007\/s00371-023-03229-7","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,20]]},"assertion":[{"value":"12 December 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}