Classification of mineral inclusions in ancient ceramics: comparing different modal analysis strategies

Abstract

Digital image analysis has recently emerged as a powerful tool for the analysis of the ceramic thin sections. By producing quantitative data, it increases the usefulness of ceramic petrography to address archaeological questions.Despite several works considering digital image analysis to study archaeological ceramic materials, so far no work has been proposed to evaluate the possibilities of optical microscopy (OM) image analysis for classification and modal analysis, knowing its advantages and drawbacks, as compared to scanning electron microscopy (SEM) image processing. In this work we propose an algorithm to count the mineral inclusions correctly classified by OM image analysis with respect to SEM images. The proposed algorithm is based on a matching mechanism applied to the regions of interest (ROIs) detected in the OM images according to the corresponding ROIs as detected in the SEM images.Moreover, a comparison with point counting on the same OM images is also provided to evaluate the pros and cons of each method. Two Holocene potsherds (8900-4200 BP) are considered as case study for modal analysis to identify and classify quartz, feldspars and carbonate aggregates as mineral inclusions.

Ceramic Microscope Image Classification Based on Multi-Scale Fusion Bottleneck Structure and Chunking Attention Mechanism

“…Hogan et al [13] discovered the relationship between compression testing and microstructure changes by conducting uniaxial and biaxial compression experiments on ceramics and conducting stress analysis while observing changes in ceramic microscopic images. Aprile et al [14], [15] identified the composition of ceramics through microscopic image acquisition methods such as OM and conducted modal analysis. This method of detection can avoid complex component extraction processes.…”

Paesaggi urbani e rurali in trasformazione. Contesti e dinamiche dell’insediamento letti alla luce della fonte archeologica

“…Tra le criticità si possono sottolineare la necessità della preparazione del campione, quella di conoscenze specifiche in ambito geologico e di una dettagliata osservazione dei campioni per un'analisi esaustiva. Questi ultimi due aspetti possono essere in parte migliorati con l'applicazione di tecniche di image analysis (Puglisi et al, 2013;Aprile et al, 2019;Maritan, 2019;Reedy, 2006) per automatizzare e velocizzare il riconoscimento di alcune fasi minerali, anche se alcune caratteristiche di questa tecnica, come l'estinzione dei minerali a certe angolazioni, possono rappresentare un ostacolo (Livingood & Cordell, 2009).…”

“…Quando il sistema SEM è accoppiato ad un rivelatore per i raggi X emessi dal campione (SEM-EDS -Scanning Electron Microscope equipped with Energy Dispersion System) risulta possibile ottenere un'analisi chimica del materiale, rivelandone gli elementi che lo compongono. Anche questa tecnica, oltre ad essere utile a identificare la composizione della ceramica in esame (Weiss, 2016;Velraj, 2015), si presta particolarmente ad essere accoppiata all'analisi d'immagine (Maritan, 2019;Aprile, 2019;Reedy, 2006).…”

A Systematic Review on Virtual Reality Technology for Ancient Ceramic Restoration

“…Under the realm of VR, virtual restoration technologies include deep learning, 3D printing, and 3D laser scanning, which are widely applied to cultural heritages such as stone sculptures, pottery sculptures, and massive architectures [6,[21][22][23][24]. Since ancient ceramic restoration is a branch of cultural relics restoration, these VR technologies are also applied to ceramic restoration [25][26][27], which is still relatively rare compared to other archaeological categories. In most cases of VR ancient ceramic restoration, the technologies applied mainly include 3D scanning, 3D modeling and 3D printing [28].…”