Cultural Heritage Images Research Articles

Research on real-time 3D imaging technology based on CMOS sensors

Abstract. With the continuous progress of science and technology, 3D imaging technology has been widely used in many fields, such as medical imaging, virtual reality and protection of cultural heritage. It can provide more information than traditional 2D images, greatly improving the accuracy and reliability of various applications. CMOS (Complementary Metal Oxide Semiconductor) sensors, as efficient and low-cost image sensors, have been widely used in image imaging and processing technologies in recent years. However, how to realize real-time and high-precision 3D imaging with CMOS sensors is still a technical challenge to be solved. Based on this background, this paper will focus on real-time 3D imaging techniques based on CMOS sensors through three topics. This paper will first introduce how CMOS sensors work, followed by the introduction of real-time 3D imaging technology, and finally the real-time 3D imaging technology based on CMOS sensors. The research in this paper will be valuable for the research and application of real-time 3D imaging technology based on CMOS sensors.

A New Transmission Target for Cultural Heritage Imaging

A New Transmission Target for Cultural Heritage Imaging

Dynamics of Cultural Loyalty Among Keroncong Music Fans: Roles of Cultural Values, Heritage Image, and Community Involvement

Dynamics of Cultural Loyalty Among Keroncong Music Fans: Roles of Cultural Values, Heritage Image, and Community Involvement

Images of Cultural Heritage in the Tourism Brand of Spain

Images of Cultural Heritage in the Tourism Brand of Spain

Review on integration of ontology and deep learning in cultural heritage image retrieval

Image retrieval methods are currently developing towards big data processing. The literature review is focused on image big data extraction with cultural heritage domain as training and testing datasets. The development of image retrieval process starts from content-based using machine algorithms, deep learning to ontology-based. Image recognition research with cultural heritage domain is conducted because of the importance of preserving and appreciating cultural heritage, in this case, cultural heritage images such as Indonesian Batik are discussed. Batik motif images are Indonesian cultural heritage that has thousands of motifs that are grouped into many classes with a non-linear hyperplane. The problem is focused on processing big data that has many classes. Currently research is evolving into knowledge-based image retrieval using ontologies due to semantic gap constraints. The results of this literature study can be the basis for developing research on the application of appropriate deep learning algorithms so as to utilize the hierarchy of classes and subclasses of image ontologies with cultural heritage domains.

Colorimetric characterization of multispectral imaging systems for visualization of historical artifacts

Multispectral imaging has long been used in the study of cultural heritage artifacts, particularly manuscripts with low-contrast text. The goal of multispectral imaging systems has largely been to enhance the text by imaging outside the spectral response of the human visual system and by applying various image processing techniques. However, there is a desire to use multispectral images for high quality, true color rendering of the artifacts as well. We use two multispectral imaging systems in this work. One of them is the Multispectral Imaging System for the study of Historical Artifacts (MISHA) which has been demonstrated to have significant utility at a lower cost and better ease of use compared to commercially available systems. This system was specifically designed and built for the purpose of imaging artifacts, particularly manuscripts and works on paper with illegible text due to deterioration and other environmental causes. Given this goal of “discovering” low-contrast text (and other features) on planar objects, the system has not been optimized for color reproduction purposes. The second system is a commercially available MegaVision EVTM Spectral Imaging System. This system is also used for cultural heritage imaging. In this work, we develop an approach to colorimetrically characterize these cameras and illumination systems, adding an option for accurate color reproduction to their capabilities. To do this, we present two different approaches. The first approach we call the Color Transformation Method. Here we use a Macbeth ColorChecker to first linearize the camera photometric response, and then find a linear matrix transformation from the camera response to CIEXYZ tristimulus values. The second approach we call the Illuminant Method. Here we consider each output by the camera as a reflectance factor value at the known wavelength of a narrowband illuminator. We then convert these values to the color of the object in terms of CIEXYZ tristimulus values. We show that the Color Transformation Method using a Macbeth ColorChecker leads to a more accurate result. Meanwhile, the Illuminant Method is much simpler and produces acceptable results, particularly considering accurate color reproduction was not a goal in designing the MISHA camera system. The same methods are applied to both imaging systems to show the robustness and applicability of the approaches developed herein to other imaging systems. Results are shown for both quantitative calibration targets as well as artifacts of interest to the field of cultural heritage studies. It is worth noting that we are not comparing the two imaging systems overall or the color processing software available from MegaVision. Rather, we simply show that the two approaches developed here are applicable to different multispectral imaging systems, from a very cost-effective system, i.e., MISHA to a more expensive one, i.e., MegaVision.

Measuring intangible cultural heritage image: A scale development.

Although an increasing number of studies have examined issues relating to the preservation and development of intangible cultural heritage (ICH), there has been limited research on how tourists perceive ICH. Moreover, UNESCO asserts that the concept of "authenticity" is not applicable to ICH, and so far, no valid instrument for measuring tourists' subjective perceptions of ICH has been developed, even though their perceptions play a very important role in the preservation and development of ICH. Therefore, this paper aims to develop a measurement scale for ICH image, using both qualitative and quantitative research methods. Participant observation, semi-structured in-depth interviews, secondary data collection, and a literature review were conducted to generate the initial scale items, and then the main surveys were conducted to collect data for the model tests. Four dimensions were extracted by exploratory factor analysis: transmission, localization, vitality, and association. The reliability and validity of the measurement model were demonstrated through confirmatory factor analysis. We further determined that the transmission, vitality, and association of ICH image have a positive impact on tourists' revisit intention. The paper highlights the crucial role of ICH image in sustainable tourism development. The theoretical and managerial implications of the study are discussed, followed by suggestions for future research.

A Framework Study on the Application of AIGC Technology in the Digital Reconstruction of Cultural Heritage

Abstract AIGC is currently a hot field and a future trend in AI applications, and addressing the challenge of digitally reconstructing cultural heritage under the influence of AI technology is a pressing issue that requires immediate resolution. The article proposes an application framework for AIGC technology that is based on refining its meaning and designing a specific process for applying it to the digital reconstruction of cultural heritage. A high-definition camera is used to acquire relevant images of cultural heritage. The image features are extracted by the SIFT algorithm optimized by the PROSAC algorithm. The color features are acquired by combining the color histogram, color moment, and color correlation diagram. The 3D laser scanning technology is used to obtain the 3D point cloud data of the cultural heritage; the KD-tree improved ICP algorithm is introduced to improve the efficiency of point cloud alignment; the dense reconstruction of the 3D point cloud data of the cultural heritage is realized based on CMVS/PMVS; and the immersive 3D experience system of the cultural heritage is constructed by combining with platforms such as Unity3D. The average matching rate of the optimized SITF algorithm to the image features of cultural heritage is about 74.91%, and the maximum alignment time of the ICP algorithm to the cultural heritage point cloud data based on KD-tree is 9.241 s. The cultural heritage immersive 3D experience system has a satisfaction rate of 56.75%, and the density reconstructed model’s surface has an average deviation of only 0.34 mm from the real surface. The user satisfaction rating for the immersive 3D experience system for cultural heritage is 56.75%. Based on AIGC technology, it can revitalize cultural heritage and achieve digital reconstruction and inheritance innovation of cultural heritage.

Intangible cultural heritage image classification with multimodal attention and hierarchical fusion

Designing an efficient Intangible Cultural Heritage (ICH) image classification is beneficial for the public to recognize the ICH and fostering the preservation and spread of that. Currently, related ICH image classification researches mainly focus on the visual features of ICH images, ignoring attached textual descriptions. However, attached textual descriptions can provide crucial clues for ICH images classification. Therefore, in this study, we propose to combine attached textual descriptions to perform ICH image classification in a multimodal way. Additionally, to capture intra- and inter-interactions between ICH images and attached textual descriptions, we propose a novel model named MICMLF, mainly consisted of multimodal attention and hierarchical fusion. Multimodal attention is employed to make the model focus on “important regions” and “important words” in ICH image and attached textual descriptions respectively. Hierarchical fusion is utilized to capture inter-modal dynamics interactions. Extensive experiments are conducted on two Chinese nation-level ICH lists datasets, New Year Print (年画) and Clay Figurine (泥塑). Experimental results demonstrate the superiority of MICMLF, compared with several state-of-the-art methods. Also, the proposed model can handle the situation where ICH images and textual descriptions are incomplete. To the best of our knowledge, we are the first to propose to combine textual descriptions to perform ICH image classification in a multimodal way.

Application of VR technology based on interactive genetic algorithm in the protection of ethnic intangible cultural heritage images in Guangxi

Application of VR technology based on interactive genetic algorithm in the protection of ethnic intangible cultural heritage images in Guangxi

Cultural Heritage Image Analysis and Support Modeling Using X-ray Computed Tomography: Focused on the Byeongsan Mask ‘Eul’ of the Hahoe Masks and Byeongsan Masks of Andong

Before conservation treatment, the Byeongsan Mask ‘Eul’ was damaged, and the face was separated on both sides and attached with stiffeners. For conservation treatment, it was necessary to remove the existing stiffeners and adhese the faces together. At this time, a support was needed to fix both faces that were separated during conservation process and to structurally stabilize the adhesion area where pressure could be concentrated even after adhesion. In order to manufacture new support, the shape analysis was first performed using the X-ray computed tomography data, and then the re-adhesion position was set. Next, modeling was conducted considering the function of the support and aesthetic conditions using the digital haptic modeling system. The completed model was printed by 3D printing using transparent material. The support produced in this study is significant in that it played a major role in the conservation process, such as being used not only to hold and store the masks but also to fix them during the conservation process. In addition, by producing support using digital methods and data, support optimized for the shape of artifact could be produced with minimal contact with artifact. This study presented a method for conserving and displaying artifacts using digital technology, and is expected to be expanded and applied to various cultural heritages.

Toward Fine-grained Image Retrieval with Adaptive Deep Learning for Cultural Heritage Image

Fine-grained image classification is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a specific category caused by different poses and scales. A cultural heritage image is one of the fine-grained images because each image has the same similarity in most cases. Using the classification technique, distinguishing cultural heritage architecture may be difficult. This study proposes a cultural heritage content retrieval method using adaptive deep learning for fine-grained image retrieval. The key contribution of this research was the creation of a retrieval model that could handle incremental streams of new categories while maintaining its past performance in old categories and not losing the old categorization of a cultural heritage image. The goal of the proposed method is to perform a retrieval task for classes. Incremental learning for new classes was conducted to reduce the re-training process. In this step, the original class is not necessary for re-training which we call an adaptive deep learning technique. Cultural heritage in the case of Thai archaeological site architecture was retrieved through machine learning and image processing. We analyze the experimental results of incremental learning for fine-grained images with images of Thai archaeological site architecture from world heritage provinces in Thailand, which have a similar architecture. Using a fine-grained image retrieval technique for this group of cultural heritage images in a database can solve the problem of a high degree of similarity among categories and a high degree of dissimilarity for a specific category. The proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 percent. Adaptive deep learning for fine-grained image retrieval was used to retrieve cultural heritage content, and it outperformed state-of-the-art methods in fine-grained image retrieval.

Linked Open Images: Visual similarity for the Semantic Web

This paper presents ArtVision, a Semantic Web application that integrates computer vision APIs with the ResearchSpace platform, allowing for the matching of similar artworks and photographs across cultural heritage image collections. The field of Digital Art History stands to benefit a great deal from computer vision, as numerous projects have already made good progress in tackling issues of visual similarity, artwork classification, style detection, gesture analysis, among others. Pharos, the International Consortium of Photo Archives, is building its platform using the ResearchSpace knowledge system, an open-source semantic web platform that allows heritage institutions to publish and enrich collections as Linked Open Data through the CIDOC-CRM, and other ontologies. Using the images and artwork data of Pharos collections, this paper outlines the methodologies used to integrate visual similarity data from a number of computer vision APIs, allowing users to discover similar artworks and generate canonical URIs for each artwork.

Design method of intangible cultural creative products based on pharma Industries Ethics Education among Educators

In order to solve the problem of low image fusion caused by the high cost of clustering calculation in the design of intangible cultural creative products, a design method for intangible cultural creative products based on AI painting generation is proposed for pharma companies. The intangible cultural heritage image is extracted and segmented. The image is divided into different regions according to the gray level of pixels, and the edge detection algorithm of images in different regions of the product is designed to make the region near the picture more fit. The fusion image of intangible cultural heritage features is generated based on AI paintings, so as to reduce the cost of clustering calculation and complete the splicing of artistic style. Build the design model of intangible cultural creative products, combine the design creativity, and realize the mutual integration of intangible culture and products. Construct four image sample sets of scenic spots and historic sites, religious beliefs, festival folk customs and handmade, and test the fusion degree. Taking the festival folk custom image set as an example, the average fusion degree of this method is 0.773, which is 0.133 and 0.145 higher than the comparison method based on convolutional neural network and machine learning, respectively. Therefore, it has good application effect.

Artist Acrylic Paint Spectral, Colorimetric, and Image Dataset

Spectral datasets, and subsequent colorimetric and image data, have a variety of uses in cultural heritage imaging including sensor design, lighting design, synthetic target generation, spectral accuracy assessment of multispectral and hyperspectral cameras, color accuracy assessment of digital cameras, and encoding errors. Spectral data for 58 Golden Artist Colors Heavy Body Acrylics were used to calculate the spectra of 831 varnished tints, tones, and masstones, based on the two-constant opaque form of Kubelka Munk turbid-media theory. The data were used to calculate a synthetic target that was used to quantify encoding errors using AdobeRGB (1998), commonly used in cultural heritage imaging, and sRGB, commonly used in documents and consumer imaging. 22% and 31% of the target colors were out of gamut, respectively. Principal component analysis was performed and the first three eigenvectors used to extract spectra similar to cyan, magenta, and yellow. These PCA-based primaries poorly approximated the 58 pigments.

Beyond RGB: A Spectral Image Processing Software Application for Cultural Heritage Studio Photography

A software application for colorimetric and spectral processing of six-channel spectral images has recently been developed. The application, called Beyond RGB, takes as input two RAW RGB image sets (object/flat-field/dark current) captured under two different lighting or filtering conditions, and outputs 1.) a color managed RGB image with ancillary information about the accuracy of the color calibration and 2.) a spectral reflectance transform that enables the interactive estimation of reflectance curves from user-selected regions of interest in the image. Beyond RGB was designed with considerations for form, function, and user friendliness, and is intended for use by cultural heritage imaging professionals. It is cross-platform compatible and is operated through an interactive graphical user interface. Beyond RGB is a living, updatable, open-source project, and is freely available for download from the project’s public GitHub repository.

Images of architectural heritage in the cultural space

The subject of this article is the analysis of methodological aspects of the study of images of architectural heritage in the cultural space of the city. The object of the study is the architectural environment of the city as part of the architectural heritage. The author examines in detail such aspects of the topic as the influence of heritage on the positive image of the territory, the essence of the image and figurative characteristics of the city, factors of perception of urban space, directions of development of the architectural environment of the city, analysis of the figurative characteristics of the city, the state of architectural heritage. Special attention is paid to the methodology of analyzing images of cultural heritage, classification of figurative characteristics, substantiation of a new approach to the analysis of the architectural environment of the city. A special contribution of the authors to the study of the topic is the substantiation of a new methodological construct in the study of the subject field of the influence of figurative characteristics of the architectural environment of the city on the preservation of its cultural heritage. The novelty of the research is the new application of the figurative-symbolic approach to the study of cultural heritage in the images of architecture. The main conclusions of the study are: the need for the formation of an architectural environment, as well as significant activities to actualize and popularize cultural heritage, the active inclusion of images of architectural heritage in the semiotic field of the cultural space of the city, increasing awareness of the value of the architectural environment on the part of citizens.

Intelligent Splicing Method of Virtual Reality Lingnan Cultural Heritage Panorama Based on Automatic Machine Learning

With the increasing expansion of virtual reality application fields and the complexity of application content, the demand for real-time rendering of realistic graphics has increased sharply. This research mainly discusses the intelligent mosaic method of virtual reality Lingnan cultural heritage panorama based on automatic machine learning. In order to effectively make up for the impact of the insufficiency of the collection process on the quality of the final panoramic image of Lingnan cultural heritage, it is necessary to minimize the irregular rotation of the camera and collect images according to the overlapping area between adjacent images of appropriate size. In order to make Lingnan cultural heritage panoramic images have better visual effects, it is necessary to preprocess the images before image registration and fusion. Image preprocessing mainly includes image denoising and image projection transformation. In this study, cylindrical projection is used to construct the panorama of Lingnan cultural heritage. For each Lingnan cultural heritage training image, we first perform image segmentation to obtain multiple regions and extract the visual features of each region. We use automatic machine learning models to train the visual feature set and use the bagging method to generate different training subsets. In order to generate each component classifier, we determine the overlap area of the two images according to the matched SIFT feature points and determine the best stitching line during the implementation of stitching. In this paper, the number of pixels in the first row of the overlapping area is used to determine the candidate stitching line column, and the best stitching line position should be determined in consideration of the smallest color difference in the stitching area and the most similar texture on both sides. This article uses a Java Applet-based approach to realize virtual roaming of viewing panoramic images of Lingnan cultural heritage in IE browser. The highest accuracy of SIFT is 82.22%, and the lowest recognition time is 0.01 s. This research will promote the development of Lingnan cultural heritage.

A Methodology for Semantic Enrichment of Cultural Heritage Images Using Artificial Intelligence Technologies.

Cultural heritage images are among the primary media for communicating and preserving the cultural values of a society. The images represent concrete and abstract content and symbolise the social, economic, political, and cultural values of the society. However, an enormous amount of such values embedded in the images is left unexploited partly due to the absence of methodological and technical solutions to capture, represent, and exploit the latent information. With the emergence of new technologies and availability of cultural heritage images in digital formats, the methodology followed to semantically enrich and utilise such resources become a vital factor in supporting users need. This paper presents a methodology proposed to unearth the cultural information communicated via cultural digital images by applying Artificial Intelligence (AI) technologies (such as Computer Vision (CV) and semantic web technologies). To this end, the paper presents a methodology that enables efficient analysis and enrichment of a large collection of cultural images covering all the major phases and tasks. The proposed method is applied and tested using a case study on cultural image collections from the Europeana platform. The paper further presents the analysis of the case study, the challenges, the lessons learned, and promising future research areas on the topic.

Segmentation of Change in Surface Geometry Analysis for Cultural Heritage Applications.

This work proposes a change-based segmentation method for applications to cultural heritage (CH) imaging to perform monitoring and assess changes at each surface point. It can be used as a support or component of the 3D sensors to analyze surface geometry changes. In this research, we proposed a new method to identify surface changes employing segmentation based on 3D geometrical data acquired at different time intervals. The geometrical comparison was performed by calculating point-to-point Euclidean distances for each pair of surface points between the target and source geometry models. Four other methods for local distance measurement were proposed and tested. In the segmentation method, we analyze the local histograms of the distances between the measuring points of the source and target models. Then the parameters of these histograms are determined, and predefined classes are assigned to target surface points. The proposed methodology was evaluated by considering two different case studies of restoration issues on CH surfaces and monitoring them over time. The results were presented with a colormap visualization for each category of the detected change in the analysis. The proposed segmentation method will help in the field of conservation and restoration for the documentation and quantification of geometrical surface change information. This analysis can help in decision-making for the assessment of damage and potential prevention of further damage, and the interpretation of measurement results.