{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T21:57:44Z","timestamp":1763416664561,"version":"3.45.0"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819530571"},{"type":"electronic","value":"9789819530588"}],"license":[{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-3058-8_32","type":"book-chapter","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T21:52:53Z","timestamp":1763416373000},"page":"342-353","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Matching Ancient Dunhuang Manuscripts Based on\u00a0Multi-dimensional Feature Fusion"],"prefix":"10.1007","author":[{"given":"Yanping","family":"Xiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqi","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingkun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teer","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yutong","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,18]]},"reference":[{"key":"32_CR1","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Luo, M.: Dunhuang Buddhist scripture fragment stitching example. Zhejiang Univ. J. Humanit. Soc. Sci. 46(3) (2016). https:\/\/doi.org\/10.3785\/j.issn.1008-942X.CN33-6000\/C.2016.01.252","DOI":"10.3785\/j.issn.1008-942X.CN33-6000\/C.2016.01.252"},{"key":"32_CR2","doi-asserted-by":"publisher","unstructured":"Zhang, Y., et al.: Reconnecting the broken civilization: patchwork integration of fragments from ancient manuscripts. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 1157\u20131166 (2023). https:\/\/doi.org\/10.1145\/3581783.3613804","DOI":"10.1145\/3581783.3613804"},{"key":"32_CR3","doi-asserted-by":"publisher","unstructured":"Zhang, Y., et al.: LLMCO4MR: LLMS-aided neural combinatorial optimization for ancient manuscript restoration from fragments with case studies on Dunhuang. In: European Conference on Computer Vision, pp. 253\u2013269. Springer (2025). https:\/\/doi.org\/10.1007\/978-3-031-73226-3_15","DOI":"10.1007\/978-3-031-73226-3_15"},{"key":"32_CR4","doi-asserted-by":"publisher","unstructured":"Zhang, Y., et\u00a0al.: PhiloGPT: a philology-oriented large language model for ancient Chinese manuscripts with Dunhuang as case study. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 2784\u20132801 (2024). https:\/\/doi.org\/10.18653\/v1\/2024.emnlp-main.163","DOI":"10.18653\/v1\/2024.emnlp-main.163"},{"issue":"8","key":"32_CR5","doi-asserted-by":"publisher","first-page":"4000","DOI":"10.1109\/tip.2019.2903298","volume":"28","author":"C Le","year":"2019","unstructured":"Le, C., Li, X.: JIGSAWNet: shredded image reassembly using convolutional neural network and loop-based composition. IEEE Trans. Image Process. 28(8), 4000\u20134015 (2019). https:\/\/doi.org\/10.1109\/tip.2019.2903298","journal-title":"IEEE Trans. Image Process."},{"key":"32_CR6","doi-asserted-by":"publisher","first-page":"28554","DOI":"10.1109\/access.2024.3368004","volume":"12","author":"Y Cao","year":"2024","unstructured":"Cao, Y., Fang, Z., Tian, H., Wei, R.: 2D irregular fragment reassembly with deep learning assistance. IEEE Access 12, 28554\u201328563 (2024). https:\/\/doi.org\/10.1109\/access.2024.3368004","journal-title":"IEEE Access"},{"key":"32_CR7","doi-asserted-by":"publisher","unstructured":"Zhou, R., Xia, D., Zhang, Y., Pang, H., Yang, X., Li, C.: PairingNet: a learning-based pair-searching and-matching network for image fragments. In: European Conference on Computer Vision, pp. 234\u2013251. Springer (2025). https:\/\/doi.org\/10.1007\/978-3-031-73202-7_14","DOI":"10.1007\/978-3-031-73202-7_14"},{"key":"32_CR8","doi-asserted-by":"publisher","unstructured":"Kong, W., Kimia, B.B.: On solving 2D and 3D puzzles using curve matching. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol.\u00a02. IEEE (2001). https:\/\/doi.org\/10.1109\/cvpr.2001.991015","DOI":"10.1109\/cvpr.2001.991015"},{"issue":"9","key":"32_CR9","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1109\/tpami.2002.1033215","volume":"24","author":"HC da Gama Leitao","year":"2002","unstructured":"da Gama Leitao, H.C., Stolfi, J.: A multiscale method for the reassembly of two-dimensional fragmented objects. IEEE Trans. Pattern Anal. Mach. Intell. 24(9), 1239\u20131251 (2002). https:\/\/doi.org\/10.1109\/tpami.2002.1033215","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"32_CR10","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1109\/tip.2009.2035840","volume":"19","author":"E Tsamoura","year":"2009","unstructured":"Tsamoura, E., Pitas, I.: Automatic color based reassembly of fragmented images and paintings. IEEE Trans. Image Process. 19(3), 680\u2013690 (2009). https:\/\/doi.org\/10.1109\/tip.2009.2035840","journal-title":"IEEE Trans. Image Process."},{"key":"32_CR11","doi-asserted-by":"publisher","unstructured":"Xu, C., Yan, J., Yang, H.: Image stitching method based on image edge detection and sift algorithm. In: Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering, pp. 425\u2013431 (2021). https:\/\/doi.org\/10.1145\/3501409.3501487","DOI":"10.1145\/3501409.3501487"},{"issue":"5","key":"32_CR12","doi-asserted-by":"publisher","first-page":"1154","DOI":"10.1109\/icme.2010.5582544","volume":"13","author":"H Liu","year":"2011","unstructured":"Liu, H., Cao, S., Yan, S.: Automated assembly of shredded pieces from multiple photos. IEEE Trans. Multimedia 13(5), 1154\u20131162 (2011). https:\/\/doi.org\/10.1109\/icme.2010.5582544","journal-title":"IEEE Trans. Multimedia"},{"issue":"5","key":"32_CR13","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1016\/j.gmod.2014.03.001","volume":"76","author":"K Zhang","year":"2014","unstructured":"Zhang, K., Li, X.: A graph-based optimization algorithm for fragmented image reassembly. Graph. Models 76(5), 484\u2013495 (2014). https:\/\/doi.org\/10.1016\/j.gmod.2014.03.001","journal-title":"Graph. Models"},{"key":"32_CR14","doi-asserted-by":"publisher","unstructured":"Zhang, Y.: The patching-up and study on the fragments of Xinpusajing, Quanshanjing and Jiuzhuzhongshengkunanjing in Dunhuang manuscripts. Fudan J. (Soc. Sci.) 57(6), 12\u201320 (2015). https:\/\/doi.org\/10.3785\/j.issn.1008-942X.CN33-6000\/C.2016.01.251","DOI":"10.3785\/j.issn.1008-942X.CN33-6000\/C.2016.01.251"},{"key":"32_CR15","unstructured":"Zhang, Y.: The patching-up and study on the fragments of Xinpusajing, Quanshanjing and Jiuzhuzhongshengkunanjing in Dunhuang manuscripts. Fudan J. (Soc. Sci.) 57(6), 12\u201320 (2015). https:\/\/doi.org\/CNKI:SUN:FDDX.0.2015-06-003"},{"key":"32_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108065","volume":"119","author":"N Derech","year":"2021","unstructured":"Derech, N., Tal, A., Shimshoni, I.: Solving archaeological puzzles. Pattern Recogn. 119, 108065 (2021). https:\/\/doi.org\/10.1016\/j.patcog.2021.108065","journal-title":"Pattern Recogn."},{"key":"32_CR17","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.culher.2015.11.005","volume":"19","author":"P Savino","year":"2016","unstructured":"Savino, P., Tonazzini, A.: Digital restoration of ancient color manuscripts from geometrically misaligned recto-verso pairs. J. Cult. Herit. 19, 511\u2013521 (2016). https:\/\/doi.org\/10.1016\/j.culher.2015.11.005","journal-title":"J. Cult. Herit."},{"issue":"6","key":"32_CR18","doi-asserted-by":"publisher","first-page":"1439","DOI":"10.1007\/s10596-022-10175-1","volume":"26","author":"C Panagiotakis","year":"2022","unstructured":"Panagiotakis, C., Markaki, S., Kokinou, E., Papadakis, H.: Coastline matching via a graph-based approach. Comput. Geosci. 26(6), 1439\u20131448 (2022). https:\/\/doi.org\/10.1007\/s10596-022-10175-1","journal-title":"Comput. Geosci."},{"key":"32_CR19","doi-asserted-by":"publisher","unstructured":"Besl, P.J., McKay, N.D.: Method for registration of 3-D shapes. In: Sensor fusion IV: Control Paradigms and Data Structures, vol.\u00a01611, pp. 586\u2013606. SPIE (1992). https:\/\/doi.org\/10.1109\/34.121791","DOI":"10.1109\/34.121791"},{"key":"32_CR20","doi-asserted-by":"publisher","unstructured":"Hossieni, S.S., Shabani, M.A., Irandoust, S., Furukawa, Y.: PuzzleFusion: unleashing the power of diffusion models for spatial puzzle solving. In: Advances in Neural Information Processing Systems, vol. 36 (2024). https:\/\/doi.org\/10.48550\/arXiv.2211.13785","DOI":"10.48550\/arXiv.2211.13785"},{"key":"32_CR21","doi-asserted-by":"publisher","first-page":"8619","DOI":"10.1109\/access.2024.3352601","volume":"12","author":"Z Du","year":"2024","unstructured":"Du, Z., Liang, Y.: Object detection of remote sensing image based on multi-scale feature fusion and attention mechanism. IEEE Access 12, 8619\u20138632 (2024). https:\/\/doi.org\/10.1109\/access.2024.3352601","journal-title":"IEEE Access"},{"key":"32_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2024.103820","volume":"129","author":"J Dong","year":"2024","unstructured":"Dong, J., Wang, Y., Yang, Y., Yang, M., Chen, J.: MCDNet: multilevel cloud detection network for remote sensing images based on dual-perspective change-guided and multi-scale feature fusion. Int. J. Appl. Earth Obs. Geoinf. 129, 103820 (2024). https:\/\/doi.org\/10.1016\/j.jag.2024.103820","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"32_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106990","volume":"100","author":"J Du","year":"2025","unstructured":"Du, J., Li, W., Peng, Y., Zong, Q.: Image fusion by multiple features in the propagated filtering domain. Biomed. Signal Process. Control 100, 106990 (2025). https:\/\/doi.org\/10.1016\/j.bspc.2024.106990","journal-title":"Biomed. Signal Process. Control"},{"key":"32_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107060","volume":"100","author":"TM Shibu","year":"2025","unstructured":"Shibu, T.M., Madan, N., Paramanandham, N., Kumar, A., Santosh, A.: Multi-modal brain image fusion using multi feature guided fusion network. Biomed. Signal Process. Control 100, 107060 (2025). https:\/\/doi.org\/10.1016\/j.bspc.2024.107060","journal-title":"Biomed. Signal Process. Control"},{"key":"32_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107074","volume":"100","author":"L Hou","year":"2025","unstructured":"Hou, L., Yan, Z., Desrosiers, C., Liu, H.: MFCPNet: real time medical image segmentation network via multi-scale feature fusion and channel pruning. Biomed. Signal Process. Control 100, 107074 (2025). https:\/\/doi.org\/10.1016\/j.bspc.2024.107074","journal-title":"Biomed. Signal Process. Control"},{"issue":"8","key":"32_CR26","doi-asserted-by":"publisher","first-page":"2330","DOI":"10.11834\/jig.220896","volume":"28","author":"Y Zheng","year":"2023","unstructured":"Zheng, Y., Li, X., Yin, Z., Gao, G., Weng, Y.: Automatic collation of Dunhuang ancient book fragments with multi-feature fusion. J. Image Graph. 28(8), 2330\u20132342 (2023). https:\/\/doi.org\/10.11834\/jig.220896","journal-title":"J. Image Graph."},{"key":"32_CR27","doi-asserted-by":"publisher","unstructured":"Li, G., Muller, M., Thabet, A., Ghanem, B.: DeepGCNs: can GCNs go as deep as CNNs? In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9267\u20139276 (2019). https:\/\/doi.org\/10.1109\/iccv.2019.00936","DOI":"10.1109\/iccv.2019.00936"},{"key":"32_CR28","doi-asserted-by":"publisher","unstructured":"Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018). https:\/\/doi.org\/10.48550\/arXiv.1807.03748","DOI":"10.48550\/arXiv.1807.03748"},{"key":"32_CR29","doi-asserted-by":"publisher","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020). https:\/\/doi.org\/10.48550\/arXiv.2002.05709","DOI":"10.48550\/arXiv.2002.05709"}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3058-8_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T21:52:56Z","timestamp":1763416376000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3058-8_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,18]]},"ISBN":["9789819530571","9789819530588"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3058-8_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,11,18]]},"assertion":[{"value":"18 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ksem2025.scimeeting.cn\/en\/web\/index\/27434","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}