Artificial Intelligence at the Interface between Cultural Heritage and Photography: A Systematic Literature Review

被引:5
作者
Silva, Carmen [1 ,2 ,3 ]
Oliveira, Lidia [1 ]
机构
[1] Univ Aveiro, DigiMedia Digital Media & Interact Res Ctr, P-3810193 Aveiro, Portugal
[2] Fed Univ Para, Inst Art Sci Museol, BR-66075110 Belem, Brazil
[3] Fed Univ Para, Master Programs Cultural Heritage Sci, BR-66075110 Belem, Brazil
关键词
artificial intelligence; cultural heritage; photography; machine learning; deep learning; neural network;
D O I
10.3390/heritage7070180
中图分类号
C [社会科学总论];
学科分类号
03 ; 0303 ;
摘要
Artificial intelligence has inspired a significant number of studies on the interface between cultural heritage and photography. The aims of these studies are, among others, to streamline damage monitoring or diagnoses for heritage preservation, enhance the production of high-fidelity 3D models of cultural assets, or improve the analysis of heritage images using computer vision. This article presents the results of a systematic literature review to highlight the recent state of these studies, published in the last five years and available in the Scopus, Web of Science, and JSTOR databases. The aim is to identify the potential and challenges of artificial intelligence through the connection between cultural heritage and photography, the latter of which represents a relevant methodological aspect in these investigations. In addition to the advances exemplified, the vast majority of studies indicate that there are also many obstacles to overcome. In particular, there is a need to improve artificial intelligence methods that still have significant flaws. These include inaccuracy in the automatic classification of images and limitations in the applications of the results. This article also aims to reflect on the meaning of these innovations when considering the direction of the relationship between cultural heritage and photography.
引用
收藏
页码:3799 / 3820
页数:22
相关论文
共 55 条
[21]  
DAVALLON Jean, 2016, Sciences de la societe, V99, P15
[22]  
de Spinoza B., 2009, tica
[23]  
Desvalles Andre., 2013, Conceitos-chave de Museologia
[24]  
Dodebei V., 2015, Memria e Novos Patrimnios, P21
[25]   MO.SE.: MOSAIC IMAGE SEGMENTATION BASED ON DEEP CASCADING LEARNING [J].
Felicetti, Andrea ;
Paolanti, Marina ;
Zingaretti, Primo ;
Pierdicca, Roberto ;
Malinverni, Eva Savina .
VIRTUAL ARCHAEOLOGY REVIEW, 2021, 12 (24) :25-38
[26]  
Florncio S., 2014, Educao Patrimonial: Histrico, Conceitos e Processos
[27]   Knowledge-based generative adversarial networks for scene understanding in Cultural Heritage [J].
Garozzo, Raissa ;
Santagati, Cettina ;
Spampinato, Concetto ;
Vecchio, Giuseppe .
JOURNAL OF ARCHAEOLOGICAL SCIENCE-REPORTS, 2021, 35
[28]  
Grabois T., 2020, Arquitetura, Materialidade e Tecnologias Digitais: Aplicaes na Construo e Conservao do Ambiente Construdo, P102
[29]   MACHINE LEARNING CLUSTERING FOR POINT CLOUDS OPTIMISATION VIA FEATURE ANALYSIS IN CULTURAL HERITAGE [J].
Gujski, L. M. ;
Di Filippo, A. ;
Limongiello, M. .
9TH INTERNATIONAL WORKSHOP 3D-ARCH 3D VIRTUAL RECONSTRUCTION AND VISUALIZATION OF COMPLEX ARCHITECTURES, VOL. 46-2, 2022, :245-251
[30]   Deep learning-based weathering type recognition in historical stone monuments [J].
Hatir, Mehmet Ergun ;
Barstugan, Mucahit ;
Ince, Ismail .
JOURNAL OF CULTURAL HERITAGE, 2020, 45 :193-203