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1.西北大学 文化遗产数字化国家地方联合工程研究中心,陕西 西安 710127
2.西北大学 信息科学与技术学院,陕西 西安 710127
3.伯恩茅斯大学 创新技术系,英国 普尔 BH12 5BB
[ "耿国华,西北大学二级教授,博士生导师,西北大学文化遗产数字化国家地方联合工程研究中心主任。国家教学名师,“万人计划”领军人才,国务院政府特殊津贴专家,全国优秀科技工作者,现任全国高等院校计算机基础教育研究会副会长,教育部大学计算机教学指导委员会委员,陕西省计算机教育学会理事长,获CCF杰出教育奖。长期从事智能信息处理与模式识别领域的创新性研究。主持“973”计划前期预研、国家自然科学基金重点及面上项目、国家科技支撑计划子课题、省部级重点项目等20余项。在文化遗产数字化保护、智能信息处理方面取得多项成果,出版专著5部、发表学术论文200余篇、发明专利51项。获国家科技进步奖、省部级科技奖18项,主持获国家教学成果奖4项。" ]
周明全,男,教授,博士生导师,从事虚拟现实与可视化、智能信息处理研究,mqzhou@nwu.edu.cn。
纸质出版日期:2025-02-25,
收稿日期:2024-08-20,
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耿国华, 高健, 汤汶, 等. 文化遗产数字化保护与应用研究综述[J]. 西北大学学报(自然科学版), 2025,55(1):1-22.
GENG GUOHUA, GAO JIAN, TANG WEN, et al. Review of research on digital protection and application of cultural heritage. [J]. Journal of northwest university (natural science edition), 2025, 55(1): 1-22.
耿国华, 高健, 汤汶, 等. 文化遗产数字化保护与应用研究综述[J]. 西北大学学报(自然科学版), 2025,55(1):1-22. DOI: 10.16152/j.cnki.xdxbzr.2025-01-001.
GENG GUOHUA, GAO JIAN, TANG WEN, et al. Review of research on digital protection and application of cultural heritage. [J]. Journal of northwest university (natural science edition), 2025, 55(1): 1-22. DOI: 10.16152/j.cnki.xdxbzr.2025-01-001.
中国拥有丰富多样的物质与非物质文化遗产,利用数字化技术进行文化遗产的建模、保护与展示,已成为文化遗产保护领域以及计算机图形学、计算机视觉等相关领域的研究热点。西北大学文化遗产数字化国家地方联合工程研究中心主要在三代文物采集建模设备、智慧博物馆建设、陶瓷器文物虚拟复原、古代人物面貌复原以及秦腔的智能媒体融合全息展演5个方面展开研究。然而,由于物质文化遗产与非物质文化遗产的本质不同,在建模方法、修复保护技术以及展示形式方面遇到诸多挑战:①现有文物数字化建模设备效率不高,且需要大量人工干预;②文物种类繁多、特征复杂、形态各异、语义丰富,需要开发适合中国文物的知识抽取和知识图谱构建方法,以实现高效的文物组织与展示;③对破损文物碎片的形状表示、描述方法以及自动重组的研究;④古代人物面貌的虚拟复原及性别和种族的识别;⑤全息展演技术面临高计算性能需求、艺术与技术融合的精准度、硬件兼容性、实时性、沉浸感和互动性等挑战。针对这5个方面的需求和挑战,首先,对近些年的相关领域的研究进行综述;然后,总结西北大学文化遗产数字化国家地方联合工程研究中心的系列成果;最后,对文化遗产数字化领域的未来研究方向进行展望。
China boasts a rich and diverse array of both tangible and intangible cultural heritage. Today
the use of digital technologies for the modeling
preservation
and presentation of cultural heritage has become a research hotspot in the fields of cultural heritage conservation
as well as in related areas such as computer graphics and computer vision. The Cultural Heritage Digitization National-Local Joint Engineering Research Center at Northwest University conducts research in five key areas: 3D modeling equipment for cultural relics from the three dynasties
smart museum construction
virtual restoration of ceramic artifacts
the restoration of ancient human faces
and the intelligent media fusion for holographic performances of Qin Opera. However
due to the fundamental differences between tangible and intangible cultural heritage
many challenges arise in areas such as modeling methods
restoration and preservation technologies
and presentation forms. These specific challenges include: ① Existing digital modeling equipment for cultural relics is inefficient and requires significant human intervention. ② The wide variety of cultural relics
with their complex features
diverse shapes
and rich semantics
necessitates the development of knowledge extraction and knowledge graph construction methods tailored to Chinese cultural relics for efficient organization and presentation. ③ Research on the shape representation
description methods
and automatic recombination of damaged relic fragments. ④ Virtual restoration of ancient human faces
including gender and racial recognition. ⑤ Holographic performance technology faces challenges such as high computational power demands
the accuracy of the fusion of art and technology
hardware compatibility
real-time processing
immersion
and interactivity
while also needing to address cultural differences and audience acceptance issues. In response to these needs and technical challenges
this paper first reviews the relevant literature from recent years
then summarizes a series of achievements by the Cultural Heritage Digitization National-Local Joint Engineering Research Center at Northwest University
and finally discusses future research directions in the field of cultural heritage digitization.
文化遗产数字化智慧博物馆文物虚拟复原知识图谱全息展演技术
cultural heritage digitizationsmart museumsartifact virtual restorationknowledge graphsholographic performance technology
LUTZKE P, KÜHMSTEDT P, NOTNI G. Measuring error compensation on three-dimensional scans of translucent objects[J]. Optical Engineering, 2011, 50(6): 063601.
O'TOOLE M, ACHAR S, NARASIMHAN S G, et al. Homogeneous codes for energy-efficient illumination and imaging[J]. ACM Transactions on Graphics, 2015, 34(4): 1-13.
KOBAYASHI T, HIGO T, YAMASAKI M, et al. Accurate and practical 3D measurement for translucent objects by dashed lines and complementary gray code projection[C]//2015 International Conference on 3D Vision. Lyon: IEEE, 2015: 189-197.
CHIBA N, HASHIMOTO K. Ultra-fast multi-scale shape estimation of light transport matrix for complex light reflection objects[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). Brisbane: IEEE, 2018: 6147-6152.
ZHAO H J, XU Y, JIANG H Z, et al. 3D shape measurement in the presence of strong interreflections by epipolar imaging and regional fringe projection[J]. Optics Express, 2018, 26(6): 7117-7131.
XU Y, ZHAO H J, JIANG H Z, et al. High-accuracy 3D shape measurement of translucent objects by fringe projection profilometry[J]. Optics Express, 2019, 27(13): 18421-18434.
QI Z S, WANG Z, HUANG J H, et al. Micro-frequency shifting projection technique for inter-reflection removal[J]. Optics Express, 2019, 27(20): 28293-28312.
JIANG H Z, ZHAI H J, XU Y, et al. 3D shape measurement of translucent objects based on Fourier single-pixel imaging in projector-camera system[J]. Optics Express, 2019, 27(23): 33564-33574.
JIANG H Z, YANG Q Y, LI X D, et al. 3D shape measurement in the presence of strong interreflections by using single-pixel imaging in a camera-projector system[J]. Optics Express, 2021, 29(3): 3609-3620.
JIANG H Z, LI Y X, ZHAO H J, et al. Parallel single-pixel imaging: A general method for direct-global separation and 3D shape reconstruction under strong global illumination[J]. International Journal of Computer Vision, 2021, 129(4): 1060-1086.
LI Y X, ZHAO H J, JIANG H Z, et al. Projective parallel single-pixel imaging to overcome global illumination in 3D structure light scanning[M]//Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, 2022: 489-504.
DIZEU F B D, BOISVERT J, DROUIN M A, et al. Frequency shift method: A technique for 3-D shape acquisition in the presence of strong interreflections[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 7004919.
DING D L, SUN J H. 3-D shape measurement of translucent objects based on fringe projection[J]. IEEE Sensors Journal, 2024, 24(3): 3172-3179.
MINKOV E, KAHANOV K, KUFLIK T. Graph-based recommendation integrating rating history and domain knowledge: Application to on-site guidance of museum visitors[J]. Journal of the Association for Information Science and Technology, 2017, 68(8): 1911-1924.
刘绍南, 杨鸿波, 侯霞. 文物知识图谱的构建与应用探讨[J]. 中国博物馆, 2019, 36(4): 118-125.
YOON S A, ELINICH K, WANG J, et al. Using augmented reality and knowledge-building scaffolds to improve learning in a science museum[J]. International Journal of Computer-Supported Collaborative Learning, 2012, 7(4): 519-541.
BORDES A, USUNIER N, GARCIA-DURÁN A, et al. Translating embeddings for modeling multi-relational data[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada: Curran Associates Inc, 2013: 2787-2795.
WANG Z, ZHANG J W, FENG J L, et al. Knowledge graph embedding by translating on hyperplanes[C]//Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. Québec: AAAI, 2014: 1112-1119.
JI G L, HE S Z, XU L H, et al. Knowledge graph embedding via dynamic mapping matrix[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Beijing: Association for Computational Linguistics, 2015: 687-696.
XIE R B, LIU Z Y, JIA J, et al. Representation learning of knowledge graphs with entity descriptions[C]//Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. Phoenix: ACM, 2016: 2659-2665.
LENAT D B. CYC: A large-scale investment in knowledge infrastructure[J]. Communications of the ACM, 1995, 38(11): 33-38.
MILLER G A. WordNet: A Lexical Database for English[J]. Communications of the ACM, 1995, 38(11): 39-41.
BIZER C, LEHMANN J, KOBILAROV G, et al. DBpedia-A crystallization point for the Web of Data[J]. Journal of Web Semantics, 2009, 7(3): 154-165.
REBELE T, SUCHANEK F, HOFFART J, et al. YAGO: A multilingual knowledge base from wikipedia, wordnet, and geonames[M]//Lecture Notes in Computer Science. Cham: Springer International Publishing, 2016: 177-185.
NAVIGLI R, PONZETTO S P. Babel Net: Building a very large multilingual semantic network[C]//ACL 2010: In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Uppsala: ACL, 2010: 216-225.
XU B, XU Y, LIANG J Q, et al. CN-DBpedia: A never-ending Chinese knowledge extraction system[C]//International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Cham: Springer, 2017: 428-438.
XU B, XU Y, LIANG J, et al. Advances in artificial intelligence: From theory to practice[M]. Arras: Spinger, 2017.
WANG Q, MAO Z D, WANG B, et al. Knowledge graph embedding: A survey of approaches and applications[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(12): 2724-2743.
JI S X, PAN S R, CAMBRIA E, et al. A survey on knowledge graphs: Representation, acquisition, and applications[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(2): 494-514.
HOFFART J, SUCHANEK F M, BERBERICH K, et al. YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia[J]. Artificial Intelligence, 2013, 194: 28-61.
DONG X, GABRILOVICH E, HEITZ G, et al. Knowledge vault: A web-scale approach to probabilistic knowledge fusion[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2014: 601-610.
WU W T, LI H S, WANG H X, et al. Probase: a probabilistic taxonomy for text understanding[C]//Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data. Scottsdale: ACM, 2012: 481-492.
LIANG J Q, XIAO Y H, WANG H X, et al. Probase+: Inferring missing links in conceptual taxonomies[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(6): 1281-1295.
LIN J J, ZHAO Y Z, HUANG W Y, et al. Domain knowledge graph-based research progress of knowledge representation[J]. Neural Computing and Applications, 2021, 33(2): 681-690.
DWORSCHAK F, KÜGLER P, SCHLEICH B, et al. Integrating the mechanical domain into seed approach[J]. Proceedings of the Design Society: International Conference on Engineering Design, 2019, 1(1): 2587-2596.
徐增林, 盛泳潘, 贺丽荣, 等. 知识图谱技术综述[J]. 电子科技大学学报, 2016, 45(4): 589-606.
XU Z L, SHENG Y P, HE L R, et al. Review on knowledge graph techniques[J]. Journal of University of Electronic Science and Technology of China, 2016, 45(4): 589-606.
DE BOER V, WIELEMAKER J, VAN GENT J, et al. Amsterdam museum linked open data[J]. Semantic Web, 2013, 4(3): 237-243.
闫晓创. 欧洲文化遗产资源的在线整合实践研究[J]. 中国档案, 2017(4): 74-75.
张加万. 敦煌文物数字化保护传承技术[J]. 敦煌研究, 2017(1): 7-8.
张娜. 文物知识图谱构建关键技术研究与应用[D]. 杭州: 浙江大学, 2019.
刘峤, 李杨, 段宏, 等. 知识图谱构建技术综述[J]. 计算机研究与发展, 2016, 53(3): 582-600.
LIU Q, LI Y, DUAN H, et al. Knowledge graph construction techniques[J]. Journal of Computer Research and Development, 2016, 53(3): 582-600.
SUNDHEIM B M. Named entity task definition, version 2.1[C]//The Sixth Message Understanding Conference (MUC-6). Maryland: ACM, 1995: 317-332.
CHINCHOR N, ROBINSON P. MUC-7 named entity task definition[C]//Proceedings of the 7th Conference on Message Understanding. Virginia: ACM, 1997: 1-21.
曾平. 基于文本特征学习的知识图谱构建技术研究[D]. 长沙: 国防科技大学, 2018.
张敏. 面向文物领域的知识图谱构建技术研究[D]. 西安: 西北大学, 2021.
ZHANG M, GENG G H, CHEN J. Semi-supervised bidirectional long short-term memoryand conditional random fields model for named-entity recognition using embeddings from language models representations[J]. Entropy, 2020, 22(2): 252.
PETERS M E, NEUMANN M, IYYER M, et al. Deep contextualized word representations[EB/OL]. (2018-02-15) [2024-05-24]. http://arxiv.org/abs/1802.05365http://arxiv.org/abs/1802.05365.
HUANG Z H, XU W, YU K. Bidirectional LSTM-CRF models for sequence tagging[EB/OL]. (2015-08-09) [2024-05-24]. http://arxiv.org/abs/1508.01991http://arxiv.org/abs/1508.01991.
LAFFERTY J, MCCALLUM A, PEREIRA F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the Eighteenth International Conference on Machine Learning. San Francisco: Morgan Kaufmann Publishers Inc. 2001: 282-289.
ZHANG M, GENG G H. Capsule networks with word-attention dynamic routing for cultural relics relation extraction[J]. IEEE Access, 2020, 8: 94236-94244.
ZHANG M, GENG G H, ZENG S, et al. Knowledge graph completion for the Chinese text of cultural relics based on bidirectional encoder representations from transformers with entity-type information[J]. Entropy, 2020, 22(10): 1168.
ZENG S, GENG G H, GAO H J, et al. A novel geometry image to accurately represent a surface by preserving mesh topology[J]. Scientific Reports, 2021, 11: 22573.
ZENG S, GENG G H, ZHOU M Q. Automatic representative view selection of a 3D cultural relic using depth variation entropy and depth distribution entropy[J]. Entropy, 2021, 23(12): 1561.
MARK B, OTFRIED C, MARC K, et al. Computational geometry algorithms and applications[M]. Berlin: Spinger, 2008.
曾升. 三维模型知识抽取与表示方法研究[D]. 西安: 西北大学.2022.
耿国华, 冯龙, 李康, 等. 秦陵文物数字化及虚拟复原研究综述[J]. 西北大学学报(自然科学版), 2021, 51(5): 709-721.
GENG G H, FENG L, LI K, et al. A literature review on the digitization and virtual restoration of cultural relics in the Mausoleum of Emperor Qinshihuang[J]. Journal of Northwest University (Natural Science Edition), 2021, 51(5): 710-721.
王飘, 耿国华, 杨稳, 等. 结合表面纹理与断裂轮廓的碎片拼接方法[J]. 计算机工程, 2019, 45(2): 315-320.
WANG P, GENG G H, YANG W, et al. Fragment splicing method combined with surface texture and fracture contour[J]. Computer Engineering. 2019, 45(2): 315-320.
袁洁, 周明全, 耿国华, 等. 基于轮廓线双向距离场的文物碎片拼接算法[J]. 计算机工程, 2018, 44(6): 207-212.
YUAN J, ZHOU M Q, GENG G H, et al. Heritage debris splicing algorithm based on contour line two-way distance field[J]. Computer Engineering. 2018, 44(6): 207-212.
周蓬勃, 李姬俊男, 税午阳. 基于断裂面匹配的破碎文物的虚拟修复方法[J]. 系统仿真学报, 2014, 26(9): 2176-2179.
ZHOU P B, LIJI J N, SHUI W Y. Virtual restoration of broken artifacts based on fracture surface[J]. Journal of System Simulation, 2014, 26(9): 2176-2179.
高宏娟. 文物碎块精细分类与多碎块拼接方法研究[D]. 西安: 西北大学, 2021.
HUANG R, HONG D F, XU Y S, et al. Multi-scale local context embedding for LiDAR point cloud classification[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(4): 721-725.
LIU J, CAO X, ZHANG P C, et al. AMS-net: An attention-based multi-scale network for classification of 3D terracotta warrior fragments[J]. Remote Sensing, 2021, 13(18): 3713.
WANG H F, ZHANG Y M, LIU W Q, et al. A novel GCN-based point cloud classification model robust to pose variances[J]. Pattern Recognition, 2022, 121: 108251.
YANG M M, CHEN J J, VELIPASALAR S. Cross-modality feature fusion network for few-shot 3D point cloud classification[C]//2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). Waikoloa: IEEE, 2023: 653-662.
周明全, 褚彤, 耿国华, 等. 结构与纹理融合的三维文物孔洞修复方法[J]. 光学精密工程, 2022, 30(8): 894-907.
ZHOU M Q, CHU T, GENG G H, et al. Three-dimensional cultural relic hole repair method combining structure and texture[J]. Optics and Precision Engineering, 2022, 30(8): 894-907.
魏明强, 陈红华, 孙杨杏, 等. 破损文物数字化修复:以中国出土青铜器为例[J]. 计算机辅助设计与图形学学报, 2021, 33(5): 789-797.
WEI M Q, CHEN H H, SUN Y X, et al. Digital restoration of damaged cultural relics: A case study on Chinese unearthed bronzes[J]. Journal of Computer-Aided Design & Computer Graphics, 2021, 33(5): 789-797.
王乐乐. 虚拟修复技术在石造像文物保护中的应用[J]. 北方文物, 2021(4): 64-68.
WANG L L. Digitalized restoration technique applied in protection of the stone statues[J]. Northern Cultural Relics, 2021(4): 64-68.
张豪远, 徐丹, 罗海妮, 等. 基于边缘重建的多尺度壁画修复方法[J]. 图学学报, 2021, 42(4): 590-598.
ZHANG H Y, XU D, LUO H N, et al. Multi-scale mural restoration method based on edge reconstruction[J]. Journal of Graphics, 2021, 42(4): 590-598.
HOU M L, YANG S, HU Y G, et al. A novel method for the virtual restoration of cultural relics based on a 3D fine model[J]. Dyna, 2015, 90(3): 307-313.
GAO H J, GENG G H, ZENG S. Approach for 3D cultural relic classification based on a low-dimensional descriptor and unsupervised learning[J]. Entropy, 2020, 22(11): 1290.
高宏娟, 耿国华, 王飘. 基于关键点特征描述子的三维文物碎片重组[J]. 计算机辅助设计与图形学学报, 2019, 31(3): 393-399.
GAO H J, GENG G H, WANG P. 3D archaeological fragment reassembly based on feature descriptors of key points[J]. Journal of Computer-Aided Design & Computer Graphics, 2019, 31(3): 393-399.
王毅, 李晓梦, 耿国华, 等. 基于直觉模糊熵的混合粒子群优化算法[J]. 电子学报, 2021, 49(12): 2381-2389.
WANG Y, LI X M, GENG G H, et al. Hybrid particle swarm optimization algorithm based on intuitionistic fuzzy entropy[J]. Acta Electronica Sinica, 2021, 49(12): 2381-2389.
王毅. 基于混沌反向学习的鲸鱼优化的破损佣体碎片配准方法:中国,202110220089.2[P]. 2024-03-29.
耿国华. 基于多尺度和折叠结构的兵马俑点云的形状补全方法及系统:中国,202110259051.6[P]. 2021-03-09.
吉晓瑶. 基于深度学习的兵马俑点云降采样及形状补全方法研究[D]. 西安: 西北大学, 2021.
耿国华, 薛米妍, 周蓬勃, 等. 基于对比学习与多尺度结合的陶瓷显微图像分类方法[J]. 西北大学学报(自然科学版), 2021, 51(5): 734-741.
GENG G H, XUE M Y, ZHOU P B, et al. Ceramic microscopic image classification based on the combination of contrastive learning and multi-scale methods[J]. Journal of Northwest University (Natural Science Edition), 2021, 51(5): 734-741.
HU Y, GENG G H, LI K, et al. Self-supervised segmentation for terracotta warrior point cloud (EGG-net)[J]. IEEE Access, 2022, 10: 12374-12384.
ISCAN , MEHMET Y, RICHARD P, et al. Forensic analysis of the skull[M]. New York: Wiley, 1993.
WILKINSON C. Forensic facial reconstruction[M]. Cambridge: Cambridge University Press, 2004.
周明全, 耿国华, 李康, 等. 颅面形态信息学[M]. 北京: 科学出版社, 2016.
CLAES P, VANDERMEULEN D, DE GREEF S, et al. Computerized craniofacial reconstruction: Conceptual framework and review[J]. Forensic Science International, 2010, 201(1/2/3): 138-145.
SHUI W Y, ZHANG Y M, WU X J, et al. A computerized facial approximation method for archaic humans based on dense facial soft tissue thickness depths[J]. Archaeological and Anthropological Sciences, 2021, 13(11): 186.
KÄHLER K, HABER J, SEIDEL H P. Reanimating the dead: Reconstruction of expressive faces from skull data[J]. ACM Transactions on Graphics, 2003, 22(3): 554-561.
VANEZIS P, VANEZIS M, MCCOMBE G, et al. Facial reconstruction using 3-D computer graphics[J]. Forensic Science International, 2000, 108(2): 81-95.
QUATREHOMME G, COTIN S, SUBSOL G, et al. A fully three-dimensional method for facial reconstruction based on deformable models[J]. Journal of Forensic Sciences, 1997, 42(4): 649-652.
VANDERMEULEN D, CLAES P, LOECKX D, et al. Computerized craniofacial reconstruction using CT-derived implicit surface representations[J]. Forensic Science International, 2006, 159(Suppl 1): S164-S174.
PEI Y R, ZHA H B, YUAN Z B. The craniofacial reconstruction from the local structural diversity of skulls[J]. Computer Graphics Forum, 2008, 27(7): 1711-1718.
DENG Q Q, ZHOU M Q, SHUI W Y, et al. A novel skull registration based on global and local deformations for craniofacial reconstruction[J]. Forensic Science International, 2011, 208(1/2/3): 95-102.
CLAES P, VANDERMEULEN D, DE GREEF S, et al. Bayesian estimation of optimal craniofacial reconstructions[J]. Forensic Science International, 2010, 201(1/2/3): 146-152.
BERAR M, DESVIGNES M, BAILLY G, et al. 3D statistical facial reconstruction[C]//Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis. Zagreb: IEEE, 2005: 365-370.
王琳, 赵俊莉, 黄瑞坤, 等. 颅面的径向曲线统计复原模型[J]. 光学精密工程, 2020, 28(12): 2729-2736.
WANG L, ZHAO J L, HUANG R K, et al. Craniofacial statistical reconstructionby radial curves[J]. Optics and Precision Engineering, 2020, 28(12): 2729-2736.
BERAR M, TILOTTA F M, GLAUNÈS J A, et al. Craniofacial reconstruction as a prediction problem using a Latent Root Regression model[J]. Forensic Science International, 2011, 210(1/2/3): 228-236.
PAYSAN P, LÜTHI M, ALBRECHT T, et al. Face reconstruction from skull shapes and physical attributes[M]//Lecture Notes in Computer Science. Berlin: Springer Berlin Heidelberg, 2009: 232-241.
HUANG D H, DUAN F Q, DENG Q Q, et al. Face reconstruction from skull based on partial least squares regression[C]//2011 Seventh International Conference on Computational Intelligence and Security. Sanya: IEEE, 2011: 1118-1121.
LI Y, CHANG L, QIAO X J, et al. Craniofacial reconstruction based on least square support vector regression[C]//2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC). San Diego: IEEE, 2014: 1147-1151.
DENG Q Q, ZHOU M Q, WU Z K, et al. A regional method for craniofacial reconstruction based on coordinate adjustments and a new fusion strategy[J]. Forensic Science International, 2016, 259: 19-31.
MADSEN D, LÜTHI M, SCHNEIDER A, et al. Probabilistic joint face-skull modelling for facial reconstruction[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 5295-5303.
陈仲晗, 赵俊莉, 于晗, 等. 基于测地回归模型的颅面复原[J]. 计算机辅助设计与图形学学报, 2021, 33(3): 395-404.
CHEN Z H, ZHAO J L, YU H, et al. Craniofacial reconstruction based on geodesic regression model[J]. Journal of Computer-Aided Design & Computer Graphics, 2021, 33(3): 395-404.
杨稳, 周明全, 耿国华, 等. 基于视图特征和形状特征融合的颅骨身份识别方法[J]. 激光与光电子学进展, 2023, 60(10): 138-146.
YANG W, ZHOU M Q, GENG G H.et al. Skull identification method based on fusion of view and shape features[J]. Laser & Optoelectronics Progress, 2023, 60(10): 138-146.
杨稳, 周明全, 张向葵, 等. 基于分层优化策略的颅骨点云配准算法[J]. 光学学报, 2020, 40(6): 121-133.
YANG W, ZHOU M Q, ZHANG X K.et al. Skull point cloud registration algorithm based on hierarchical optimization strategy[J]. Acta Optica Sinica, 2020, 40(6): 121-133.
CHAKRABORTY S, DAS S. K-Means clustering with a new divergence-based distance metric: Convergence and performance analysis[J]. Pattern Recognition Letters, 2017, 100: 67-73.
潘章明. 一种基于KD树子样的自动聚类方法[J]. 计算机工程与科学, 2011, 33(1): 166-170.
PAN Z M. An automatic clustering method using sub-sampling for the KD-tree[J]. Computer Engineering & Science, 2011, 33(1): 166-170.
YANG W, ZHOU M Q, LIN P Y, et al. Ancestry estimation of skull in Chinese population based on improved convolutional neural network[C]//2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Seoul: IEEE, 2020: 2861-2867.
LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
LIN P Y, YANG W, XIA S Y, et al. CFR-GAN: A generative model for craniofacial reconstruction[C]//2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Houston: IEEE, 2021: 462-469.
周明全, 杨稳, 林芃樾, 等. 基于最小二乘正则相关性分析的颅骨身份识别[J]. 光学精密工程, 2021, 29(1): 201-210.
ZHOU M Q, YANG W, LIN P Y, et al. Skull identification based on least square canonical correlation analysis[J]. Optics and Precision Engineering, 2021, 29(1): 201-210.
李昆杰. 秦腔的艺术特色研究[J]. 戏剧之家, 2019(21): 44.
KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.
SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2014-09-04) [2024-05-24]. http://arxiv.org/abs/1409.1556http://arxiv.org/abs/1409.1556.
CHEN Y, LAI Y K, LIU Y J. CartoonGAN: Generative adversarial networks for photo cartoonization[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 9465-9474.
王浩童. 基于隐向量控制的人脸卡通漫画风格迁移研究[D]. 成都: 电子科技大学, 2021.
FANG H S, XU Y L, WANG W G, et al. Learning pose grammar to encode human body configuration for 3D pose estimation[C]//Thirty-Second AAAI Conference on Artificial Intelligence. Palo Alto: AAAI, 2018: 6821-6828.
ZHAO L, PENG X, TIAN Y, et al. Semantic graph convolutional networks for 3D human pose regression[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach: IEEE, 2019: 3420-3430.
LI C, ZHONG Q Y, XIE D, et al. Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation[EB/OL]. (2018-04-17) [2024-05-24]. http://arxiv.org/abs/1804.06055http://arxiv.org/abs/1804.06055.
白铂, 刘玉婷, 马驰骋, 等. 图神经网络[J]. 中国科学:数学, 2020, 50(3): 367-384.
BAI B, LIU Y T, MA C P, et al. Graph neural network[J]. Scientia Sinica (Mathematica). 2020, 50(3): 367-384.
徐冰冰, 岑科廷, 黄俊杰, 等. 图卷积神经网络综述[J]. 计算机学报, 2020, 43(5): 755-780.
YAN S J, XIONG Y J, LIN D H. Spatial temporal graph convolutional networks for skeleton-based action recognition[C]//Proceedings of the Thirty-second AAAI Conference on Artificial Intelligence. New Orleans: AAAI, 2018: 7444-7452.
SI C Y, CHEN W T, WANG W, et al. An attention enhanced graph convolutional LSTM network for skeleton-based action recognition[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach: IEEE, 2019: 1227-1236.
SHI L, ZHANG Y F, CHENG J, et al. Two-stream adaptive graph convolutional networks for skeleton-based action recognition[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach: IEEE, 2019: 12018-12027.
ZHANG P F, LAN C L, ZENG W J, et al. Semantics-guided neural networks for efficient skeleton-based human action recognition[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE, 2020: 1109-1118.
GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial networks[EB/OL]. (2014-06-10) [2024-05-24]. http://arxiv.org/abs/1406.2661http://arxiv.org/abs/1406.2661.
ISOLA P, ZHU J Y, ZHOU T H, et al. Image-to-image translation with conditional adversarial networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 5967-5976.
ZHU J Y, PARK T, ISOLA P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//2017 IEEE International Conference on Computer Vision (ICCV). Venice: IEEE, 2017: 2242-2251.
LIU M Y, BREUEL T, KAUTZ J. Unsupervised image-to-image translation networks[EB/OL]. (2017-03-02) [2024-05-24]. http://arxiv.org/abs/1703.00848http://arxiv.org/abs/1703.00848.
HUANG X, LIU M Y, BELONGIE S, et al. Multimodal unsupervised image-to-image translation[M]//Lecture Notes in Computer Science. Cham: Springer International Publishing, 2018: 179-196.
MEJJATI Y A, RICHARDT C, TOMPKIN J, et al. Unsupervised attention-guided image to image translation[EB/OL]. (2018-06-06) [2024-05-24]. http://arxiv.org/abs/1806.02311http://arxiv.org/abs/1806.02311.
CHEN Y, LAI Y K, LIU Y J. CartoonGAN: generative adversarial networks for photo cartoonization[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 9465-9474.
KIM J, KIM M, KANG H, et al. U-GAT-IT: Unsupervised generative attentional networks with adaptive layer-instance normalization for image-to-image translation[EB/OL]. (2019-07-25) [2024-05-24]. http://arxiv.org/abs/1907.10830http://arxiv.org/abs/1907.10830.
CHEN R F, HUANG W B, HUANG B H, et al. Reusing discriminators for encoding: Towards unsupervised image-to-image translation[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE, 2020: 8165-8174.
米祺. 基于时空图卷积网络动作识别的研究及其在戏曲人物表演中的应用[D]. 西安: 西北大学, 2022.
石兴月. 可交互增强现实关键技术研究及其在秦腔虚拟展演中的应用[D]. 西安: 西北大学, 2022.
刘喆. 基于两阶段的三维姿态估计技术的研究与应用[D]. 西安: 西北大学, 2022.
CAO Z, SIMON T, WEI S H, et al. Realtime multi-person 2D pose estimation using part affinity fields[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 1302-1310.
BAI S J, KOLTER J Z, KOLTUN V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling[EB/OL]. (2018-03-04) [2024-05-24]. http://arxiv.org/abs/1803.01271http://arxiv.org/abs/1803.01271.
范力. 基于深度学习的秦腔戏曲情感分析方法研究与实现[D]. 西安: 西北大学, 2021.
KOONCE B. ResNet 50[M]//Convolutional Neural Networks with Swift for Tensorflow. Berkeley: Apress, 2021: 63-72.
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