The paper presents a methodology for the documentation, preservation, and enhancement of street art through augmented reality technologies. The approach combines photogrammetric survey and image-based tracking techniques to generate high-resolution digital representations of artworks, which are then integrated into an AR application developed with Unity and Vuforia Engine. The system allows users to access contextual information, interpretative content, and multimedia data directly overlaid onto the physical artworks, enhancing their understanding and accessibility. The research is tested on selected street art works in Naples, demonstrating how AR can support both the conservation of ephemeral urban art and the creation of interactive cultural itineraries, while also highlighting the potential for scalable, georeferenced applications and WebAR developments.
Interactive Heritage Site Mobile Application on Artworks
In this work, we are introducing early technological prototypes of an interactive mobile application aimed at heritage sites and museums. This mobile application will comprise three main components as follows: (1) Image Captioning, (2) Indoor Navigation, and (3) Augmented Reality. To address the Image Captioning component, we aim to build a mobile application for the guidance of heritage site visitors with a caption with relevant information about each artwork. This application can be helpful for visually impaired visitors, and it can provide some extra informa-tion about the museum’s objects. We will build this project based on computer vision and AI techniques. The main methodology of this work is according to the Image Captioning algorithms which by giving any input image, the AI model can provide a relevant caption to describe the input image through the textual and voice outputs. For the Indoor Heritage Site Navigation, we propose a mobile application that will be connected to an indoor positioning system which will be able to locate each piece of artwork of the museum and indicate directions to the museum visitors. Furthermore, this application would be beneficial for visually impaired people to follow prop-erly all the artwork of the museum and find their locations. The Augmented Reality technology will provide the interactive aspect of an application for the heritage site visitors to obtain extra information about the artworks. The goal is to show animated visualizations of the artwork and/or to show detailed information about each part of the artwork by pointing out each area and adding some more textual or visual data around that. The proposed application will be a holistic interactive application which will be comprised of all the three above-mentioned packages in one. The key characteristic of this work stems from building a lightweight mobile application to include three platforms together.
Between Image and Text: Automatic Image Processing for Character Recognition in Historical Inscriptions
The research addresses the challenges in Optical Character Recognition (OCR) systems when applied to ancient inscriptions and graffiti. These artifacts, serving celebratory or commemorative purposes, often present legibility issues due to erosion and gaps in the text. Our study proposes an automated image processing pipeline supported by 3D data from photogrammetric surveys. The processing phase involves manipulating image parameters and utilizing spatial coordinates and writing system information. The goal is to enhance legibility by extracting images with neutral backgrounds and highlighted characters, resembling printed texts. This processed data aims to improve the performance of pre-trained Artificial Intelligence (AI) models dedicated to OCR. Ultimately, the research seeks to provide a compar-ative study between unprocessed and processed images, validating the significance of the pre-processing phase in enhancing text recognition systems. The proposed automated workflow aims to contribute to the field of computer vision, specifically in the context of preserving and interpreting historical inscriptions.
Real-Time Identification of Artifacts:Synthetic Data for AI Model
The collections represent the constitutive element and the raison d’être of each museum. Their man-agement, care and dissemination are therefore a task of primary importance for every museum. Applying new Artificial Intelligence technologies in this area could lead to new initiatives. However, the development of certain tools requires structured and labeled datasets for the training phases which are not always easily available. The proposed contribution is within the domain of the construction of specific datasets with low budget tools and explores the results of a first step in this direction by testing algorithms for the recognition and labeling of heritage objects. The developed workflow is part of a first prototype that could be used both in heritage dissemination or gamification applications, and for use in heritage research tools.
Supervised Classification Approach for the Estimation of Degradation
The study presents an innovative approach to classify geomaterials using supervised classification methods from orthophotos derived from UAV (Unmanned Aerial Vehicle) and photogrammetric processing. The case study examined is the Ponte Rotto, dating back to 20 BC, which in antiquity allowed the Appian Way to cross the Calore River – between the provinces of Avellino and Beneven-to – to continue towards the port of Brindisi. In previous studies, experts on geomaterial diagnosis estimated – from aerophotogrammetric orthophotos generated for both bridge elevations – the geo-materials and quantities used for the construction of the monument and an overview of the state of conservation of the monument studied. Orthophotos of facades were imported into CAD software and used as the basis for – according to a manual process – the mapping of the materials. The work presents the results according to automatic Machine Learning clustering from the same orthophotos to identify geomaterials.
Media Convergence and Museum Education in the EMODEM Project
The interpretation of the museums heritage as an active social element is the basis of the most cur-rent cultural institutions projects that envisage forms of documentation, use and dissemination of the cultural heritage increasingly dialogic (the museum community) and dynamics (mixing conversational, experiential, and participative modes).This scenario is the broad framework of the project conceived to design the EMODEM app based on the convergence of face detection, eye tracking and AR to interface the virtual and the physical space and make the museum experience more visitor-centered, interactive and personalized.This article integrates the EMODEM research already underway and updates the scientific roadmap according to the progress recently achieved in phases 3 and 4 of the project, presenting the technolog-ical innovation that has intervened in the meantime in the project and the advancement of research, currently reached at third field usability testing.
“Divina!” a Contemporary Statuary Installation
In 2021, the year of the 700th anniversary of Dante Alighieri’s death, the ASTRO Laboratory of the Pisa University Department of Civil Engineering have set up, in collaboration with the Tuscany Re-gional Council, the contemporary statuary installation “Divina!” based on the work of the great poet. This installation leads users to ponder, from a technological standpoint, the way in which the means of communication are used and the importance of preserving and conserving the roots of linguistic evolution.
Automatic Recognition Through Deep Learning of Standard Forms in Executive Projects
In this paper is presented a possible methodology for automation through the use of deep learning of BIM modeling starting from different types of formats, such as digital processing of paper documents and CAD formats. The work is configured as a proof of concept of a possible contribution that a technique currently scarcely used in the architectural field such as deep learning can bring to the design, in particular in the realization of the information model, which today represents one of the most consuming–time activities.
The Emotion Detection Tools in the Museum Education EmoDeM Project
Facial recognition technologies, already used nowadays in many applications, i.e. to support security systems in sensitive buildings, could in a short time achieve widespread use also in others sectors including culture institutions like museums or art galleries. The state of the art in the field of facial recognition allows discriminating factors not only related to the essential somatic characteristics of a person to recognize, with an ever–greater degree of precision, the emotional reactions that may occur on person’s face.The article intends to describe the research’s EmoDeM experimentation in the museum environment in order to provide a tool capable of interpreting the reaction of a user in front of an artwork and propose a responsive information content coherent what is manifested through facial expressions.
Photogrammetric Survey for a Fast Construction of Synthetic Dataset
In this work we show how Physically Based Rendering (PBR) tools can be used to extend the training image datasets of Machine Learning (ML) algorithms for the recognition of built heritage. In the field of heritage valorization, the combination of Artificial Intelligence (AI) and Augmented Reality (AR) has allowed to recognize built heritage elements with mobile devices, anchoring digital products to the physical environment in real time, thus making the access to information related to real space more intuitive and effective. However, the availability of training data required for these systems is extremely limited and a large–scale image dataset is required to achieve accurate results in image recognition. Manually collecting and annotating images can be very resource and time–consuming. In this contribution we explore the use of PBR tools as a viable alternative to supplement an otherwise inadequate dataset.