{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:20:56Z","timestamp":1760710856465,"version":"3.40.3"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T00:00:00Z","timestamp":1643414400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T00:00:00Z","timestamp":1643414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s00371-021-02393-y","type":"journal-article","created":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T15:02:48Z","timestamp":1643468568000},"page":"1137-1148","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Edge-enhanced instance segmentation by grid regions of interest"],"prefix":"10.1007","volume":"39","author":[{"given":"Ying","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyang","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dexin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,29]]},"reference":[{"key":"2393_CR1","doi-asserted-by":"publisher","unstructured":"Bai, M., Urtasun, R.: Deep watershed transform for instance segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2858\u20132866. https:\/\/doi.org\/10.1109\/CVPR.2017.305 (2017)","DOI":"10.1109\/CVPR.2017.305"},{"key":"2393_CR2","doi-asserted-by":"crossref","unstructured":"Bolya, D., Zhou, C., Xiao, F., Lee, Y.J.: YOLACT: real-time instance segmentation. CoRR abs\/1904.02689, arXiv:1904.02689, (2019)","DOI":"10.1109\/ICCV.2019.00925"},{"key":"2393_CR3","doi-asserted-by":"crossref","unstructured":"Chen, H., Sun, K., Tian, Z., Shen, C., Huang, Y., Yan, Y.: Blendmask: Top-down meets bottom-up for instance segmentation. CoRR abs\/2001.00309, arXiv:2001.00309, (2020)","DOI":"10.1109\/CVPR42600.2020.00860"},{"key":"2393_CR4","doi-asserted-by":"publisher","unstructured":"Chen, X., Girshick, R., He, K., Dollar, P.: Tensormask: A foundation for dense object segmentation. pp 2061\u20132069. https:\/\/doi.org\/10.1109\/ICCV.2019.00215 (2019)","DOI":"10.1109\/ICCV.2019.00215"},{"key":"2393_CR5","doi-asserted-by":"publisher","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\u201905), vol\u00a01, pp 886\u2013893 vol. 1. https:\/\/doi.org\/10.1109\/CVPR.2005.177 (2005)","DOI":"10.1109\/CVPR.2005.177"},{"issue":"2","key":"2393_CR6","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., van Gool, L., Williams, C., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vision 88(2), 303\u2013338 (2010). https:\/\/doi.org\/10.1007\/s11263-009-0275-4","journal-title":"Int. J. Comput. Vision"},{"key":"2393_CR7","doi-asserted-by":"publisher","unstructured":"Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp 1\u20138. https:\/\/doi.org\/10.1109\/CVPR.2008.4587597 (2008)","DOI":"10.1109\/CVPR.2008.4587597"},{"key":"2393_CR8","doi-asserted-by":"publisher","unstructured":"Felzenszwalb, P.F., Girshick, R.B., McAllester, D.: Cascade object detection with deformable part models. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 2241\u20132248, https:\/\/doi.org\/10.1109\/CVPR.2010.5539906 (2010)","DOI":"10.1109\/CVPR.2010.5539906"},{"issue":"9","key":"2393_CR9","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2010","unstructured":"Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627\u20131645 (2010). https:\/\/doi.org\/10.1109\/TPAMI.2009.167","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"9","key":"2393_CR10","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2010","unstructured":"Felzenszwalb, P.F., Girshick, R.B., McAllester, D.A., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627\u20131645 (2010). https:\/\/doi.org\/10.1109\/TPAMI.2009.167","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2393_CR11","doi-asserted-by":"publisher","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp 580\u2013587, https:\/\/doi.org\/10.1109\/CVPR.2014.81 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"2393_CR12","doi-asserted-by":"crossref","unstructured":"Girshick, R.B.: Fast R-CNN. CoRR abs\/1504.08083, arXiv:1504.08083, (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"2393_CR13","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2393_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.B.: Mask R-CNN. CoRR abs\/1703.06870, arXiv:1703.06870, (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"2393_CR15","doi-asserted-by":"publisher","unstructured":"Huang, R., Pedoeem, J., Chen, C.: Yolo-lite: A real-time object detection algorithm optimized for non-gpu computers. In: 2018 IEEE International Conference on Big Data (Big Data), pp 2503\u20132510, https:\/\/doi.org\/10.1109\/BigData.2018.8621865 (2018)","DOI":"10.1109\/BigData.2018.8621865"},{"key":"2393_CR16","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, Curran Associates Inc., Red Hook, NY, USA, NIPS\u201912, p 1097-1105 (2012)"},{"issue":"11","key":"2393_CR17","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998). https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc. IEEE"},{"key":"2393_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2012.08.028","volume":"108","author":"L Leng","year":"2013","unstructured":"Leng, L., Zhang, J.: Palmhash code vs. palmphasor code. Neurocomputing 108, 1\u201312 (2013). https:\/\/doi.org\/10.1016\/j.neucom.2012.08.028","journal-title":"Neurocomputing"},{"issue":"1","key":"2393_CR19","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s11042-015-3058-7","volume":"76","author":"L Leng","year":"2017","unstructured":"Leng, L., Li, M., Kim, C., Bi, X.: Dual-source discrimination power analysis for multi-instance contactless palmprint recognition. Multim. Tools Appl. 76(1), 333\u2013354 (2017). https:\/\/doi.org\/10.1007\/s11042-015-3058-7","journal-title":"Multim. Tools Appl."},{"key":"2393_CR20","doi-asserted-by":"publisher","unstructured":"Li, J., Zhao, X., Li, H.: Method for detecting road pavement damage based on deep learning. (2019). https:\/\/doi.org\/10.1117\/12.2514437","DOI":"10.1117\/12.2514437"},{"key":"2393_CR21","doi-asserted-by":"crossref","unstructured":"Li, Y., Qi, H., Dai, J., Ji, X., Wei, Y.: Fully convolutional instance-aware semantic segmentation. CoRR abs\/1611.07709, arXiv:1611.07709, (2016)","DOI":"10.1109\/CVPR.2017.472"},{"key":"2393_CR22","doi-asserted-by":"crossref","unstructured":"Lin, T., Maire, M., Belongie, S.J., Bourdev, L.D., Girshick, R.B., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., Zitnick, C.L.: Microsoft COCO: common objects in context. CoRR abs\/1405.0312, arXiv:1405.0312, (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"2393_CR23","doi-asserted-by":"crossref","unstructured":"Lin, T., Doll\u00e1r, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S.J.: Feature pyramid networks for object detection. CoRR abs\/1612.03144, arXiv:1612.03144, (2016)","DOI":"10.1109\/CVPR.2017.106"},{"key":"2393_CR24","doi-asserted-by":"crossref","unstructured":"Lin, T., Goyal, P., Girshick, R.B., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. CoRR abs\/1708.02002, arXiv:1708.02002, (2017)","DOI":"10.1109\/ICCV.2017.324"},{"issue":"12","key":"2393_CR25","doi-asserted-by":"publisher","first-page":"2368","DOI":"10.1109\/TPAMI.2011.131","volume":"33","author":"C Liu","year":"2011","unstructured":"Liu, C., Yuen, J., Torralba, A.: Nonparametric scene parsing via label transfer. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2368\u20132382 (2011). https:\/\/doi.org\/10.1109\/TPAMI.2011.131","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2393_CR26","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C., Berg, A.C.: SSD: single shot multibox detector. CoRR abs\/1512.02325, arXiv:1512.02325, (2015)","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"234","key":"2393_CR27","first-page":"11","volume":"234","author":"W Liu","year":"2017","unstructured":"Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., Alsaadi, F.E.: A survey of deep neural network architectures and their applications. Neurocomputing 234(234), 11\u201326 (2017)","journal-title":"Neurocomputing"},{"key":"2393_CR28","unstructured":"Qi, L., Zhang, X., Chen, Y., Chen, Y., Sun, J., Jia, J.: Pointins: Point-based instance segmentation. CoRR abs\/2003.06148, arXiv:2003.06148, (2020)"},{"key":"2393_CR29","unstructured":"Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. CoRR abs\/1506.01497, arXiv:1506.01497, (2015)"},{"key":"2393_CR30","doi-asserted-by":"publisher","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, IEEE Computer Society, pp 618\u2013626, https:\/\/doi.org\/10.1109\/ICCV.2017.74, (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"2393_CR31","doi-asserted-by":"publisher","unstructured":"Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, IEEE, pp 9626\u20139635. https:\/\/doi.org\/10.1109\/ICCV.2019.00972, (2019)","DOI":"10.1109\/ICCV.2019.00972"},{"key":"2393_CR32","doi-asserted-by":"publisher","unstructured":"Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol\u00a01, pp I\u2013I, https:\/\/doi.org\/10.1109\/CVPR.2001.990517 (2001)","DOI":"10.1109\/CVPR.2001.990517"},{"issue":"2","key":"2393_CR33","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","volume":"57","author":"P Viola","year":"2004","unstructured":"Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57(2), 137\u2013154 (2004). https:\/\/doi.org\/10.1023\/B:VISI.0000013087.49260.fb","journal-title":"Int. J. Comput. Vision"},{"key":"2393_CR34","doi-asserted-by":"publisher","unstructured":"Xie, E., Sun, P., Song, X., Wang, W., Liu, X., Liang, D., Shen, C., Luo, P.: Polarmask: Single shot instance segmentation with polar representation. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 12190\u201312199. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01221 (2020)","DOI":"10.1109\/CVPR42600.2020.01221"},{"issue":"3","key":"2393_CR35","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1109\/JSTSP.2020.2987729","volume":"14","author":"W Xu","year":"2020","unstructured":"Xu, W., Wang, J., Wang, Y., Xu, G., Lin, D., Dai, W., Wu, Y.: Where is the model looking at?\u2014concentrate and explain the network attention. IEEE J. Sel. Top Signal Process 14(3), 506\u2013516 (2020). https:\/\/doi.org\/10.1109\/JSTSP.2020.2987729","journal-title":"IEEE J. Sel. Top Signal Process"},{"key":"2393_CR36","unstructured":"Xu, W., Xian, Y., Wang, J., Schiele, B., Akata, Z.: Attribute prototype network for zero-shot learning. CoRR abs\/2008.08290, arXiv:2008.08290, (2020)"},{"key":"2393_CR37","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. arXiv:1311.2901, cite arxiv:1311.2901 (2013)"},{"key":"2393_CR38","doi-asserted-by":"publisher","unstructured":"Zhang, F., Li, M., Zhai, G., Liu, Y.: Multi-branch and multi-scale attention learning for fine-grained visual categorization. In: Lokoc J, Skopal T, Schoeffmann K, Mezaris V, Li X, Vrochidis S, Patras I (eds) MultiMedia Modeling - 27th International Conference, MMM 2021, Prague, Czech Republic, June 22-24, 2021, Proceedings, Part I, Springer, Lecture Notes in Computer Science, vol 12572, pp 136\u2013147. https:\/\/doi.org\/10.1007\/978-3-030-67832-6_12, (2021)","DOI":"10.1007\/978-3-030-67832-6_12"},{"issue":"4","key":"2393_CR39","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.3390\/s20041010","volume":"20","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Chu, J., Leng, L., Miao, J.: Mask-refined R-CNN: a network for refining object details in instance segmentation. Sensors 20(4), 1010 (2020). https:\/\/doi.org\/10.3390\/s20041010","journal-title":"Sensors"},{"issue":"18","key":"2393_CR40","doi-asserted-by":"publisher","first-page":"28201","DOI":"10.1007\/s11042-021-11094-6","volume":"80","author":"D Zhao","year":"2021","unstructured":"Zhao, D., Qi, Z., Yang, R., Wang, Z.: Attention-based dual context aggregation for image semantic segmentation. Multim Tools Appl 80(18), 28201\u201328216 (2021). https:\/\/doi.org\/10.1007\/s11042-021-11094-6","journal-title":"Multim Tools Appl"},{"key":"2393_CR41","doi-asserted-by":"publisher","unstructured":"Zhou, B., Khosla, A., Lapedriza, \u00c0., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, IEEE Computer Society, pp 2921\u20132929, https:\/\/doi.org\/10.1109\/CVPR.2016.319, (2016)","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-021-02393-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-021-02393-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-021-02393-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T12:06:20Z","timestamp":1744113980000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-021-02393-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,29]]},"references-count":41,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["2393"],"URL":"https:\/\/doi.org\/10.1007\/s00371-021-02393-y","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"type":"print","value":"0178-2789"},{"type":"electronic","value":"1432-2315"}],"subject":[],"published":{"date-parts":[[2022,1,29]]},"assertion":[{"value":"23 December 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2022","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 certify that there is no conflict of interest with any individual\/organization for the present work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}