1,720,974 research outputs found
A Roman Carved Tale Modelled in 3D and Interpreted with AI
Full text linkThis study proposes an innovative methodology for documenting and semantically analysing cultural heritage by integrating artificial intelligence (AI) with a photogrammetric 3D model. The case study is the Trajan’s Column in Rome, a monumental structure adorned with a continuous helical relief depicting Emperor Trajan’s Dacian campaigns. AI-driven semantic segmentation is used to identify key elements (such as human figures, battle scenes and natural motifs) within the digitised sculptural narrative. Starting from a high-resolution photogrammetric 3D model, the column’s texture is divided into multiple segments and a multimodal large language model (MLLM) is applied to produce context-aware segmentation masks via natural language prompts. Results are then projected onto the 3D geometry and visualised through a web-based 3D viewer
Integrative AI for the Understanding of Ancient Javanese Architectures
Full text linkThe use of digital techniques has seen an increasing amount of use in recent years for heritage documentation. The development of artificial intelligence (AI) also contributed to this rise, with many different applications to help facilitate the heritage recording process. A by-product of these developments is the increasing amount of available data, in tandem with the ever-increasing need for training data for AI purposes. This paper aims to re-use old datasets and repurpose them using modern methods. The objective is therefore to see if older datasets may be used to improve the quality of AI-based methods, while also investigating the use of new technologies such as Visual Language Models (VLM) to perform semantic queries and Gaussian splatting on these datasets. For this purpose, datasets from a previous documentation project involving Javanese “candi” architecture is used in this paper since this particular subject has not seen too many AI-based documentation research in the literature and is thus an interesting example to evaluate the generalisation of AI methods. Results show that old datasets can very well be used with modern techniques with promising results. In terms of semantic segmentation, machine learning yielded an overall accuracy of 0.89 while deep learning yielded 0.79. Several interesting inferences were also observed in the VLM query results, while Gaussian splatting showed very strong potential for visualisation-based applications to further enhance the reusability of these old datasets
LA COMPLICANZA ARTICOLARE DELLA MALATTIA CELIACA. STUDIO SU 62 CASI: RISULTATI PRELIMINARI
No full text
Labelling point clouds in VR
Full text linkRecent advancements in Virtual Reality (VR) technology have extended its applications beyond entertainment, offering promising tools for professional fields such as 3D data annotation. This paper explores the use of VR for labelling 3D point clouds in forestry and cultural heritage datasets. We employ Labelling Flora, an open-source VR annotation tool, to re-label three existing cultural heritage and one forestry datasets and assess the effectiveness of VR-based annotations in training machine learning models. By comparing the accuracy, precision, and F1-score of inference models trained with VR-generated labels to those trained with traditional desktop labelling methods, we evaluate the potential of VR to streamline labour-intensive annotation tasks. Our results indicate that VR enables intuitive 3D segmentation, even for individuals without technical expertise, particularly for very complex scenes, improving labelling efficiency and contributing to the overall automation of complex datasets. This study highlights therefore the potential of VR to enhance other workflows and make complex tasks more accessible to domain experts who may not be familiar with 3D data thus refining data accuracy and reliability
Perception of electrocutaneous stimuli in irritable bowel syndrome.
No full textBACKGROUND Irritable bowel syndrome (IBS) and fibromyalgia syndrome (FMS) are common conditions with some
AND AIM: similarities, but different perceptual responses to somatic and visceral stimuli. The purpose of this
study was to assess in a large group of IBS patients the somatic perception by transcutaneous
electrical nerve stimulation (TENS) and its relation to the level of severity and presence of FMS.
METHODS: In 99 patients grouped by the validated functional bowel disorder severity index (FBDSI) in mild,
moderate, and severe IBS and in 33 healthy controls (HC), we studied discomfort thresholds and
perception of somatic stimuli at control (hands and elbows) and active (trapezius) sites by TENS and
by using a specific questionnaire.
RESULTS: The use of TENS showed that IBS showed significant higher thresholds and lower perception
cumulative score compared to HC. The severity of IBS is significantly associated with age and mean
control site values for discomfort and borderline associated with gender in the ordinal model
constructed for the ascending series protocol. The severity of IBS is also significantly associated with
the active cumulative perception score in the long stimulus protocol. Due to limited sample size of
IBS men with FMS, analyses of discomfort thresholds and cumulative perception score by FMS were
done only for women. IBS women without FMS had significantly higher mean control site values for
discomfort and significantly lower active cumulative perception score than HC. IBS women with FMS
had significantly lower mean active site values for discomfort thresholds than IBS women without
FMS (Dunn’s test p < 0.05).
CONCLUSIONS: IBS patients showed somatic hypoalgesia to electrical stimuli. The severity of IBS and the presence
of FMS influence the perception of somatic stimuli induced by TENS
Seeing among foliage with LiDAR and Machine Learning: towards a transferable archaeological pipeline
Full text linkAirborne LiDAR technology has become an essential tool in archaeology during the last two decades since it allows archaeologists to measure and map items or structures that would otherwise be hidden under vegetation. In order to detect and characterise the archaeological evidence, it is a common practice to extract accurate digital terrain models (DTM) by filtering out the vegetation from Airborne Laser Scanning (ALS) datasets. Although previous approaches have performed well in ALS filtration, they are still subject to several variables (flight height, forest cover, type of sensors utilised, etc.) and are frequently integrated into expensive commercial software or customised for specific locations. This study presents a workflow for treating ALS archaeological datasets using machine learning algorithms for both filtering the vegetation and detecting hidden structures. The workflow is applied to two different archaeological environments (in terms of structures, vegetation, landscape, point density), and results demonstrate that the pipeline is rapid and accurate, and the prediction model is transferable
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