FROM 2D TO 3D SUPERVISED SEGMENTATION AND CLASSIFICATION FOR CULTURAL HERITAGE APPLICATIONS

The digital management of architectural heritage information is still a complex problem, as a heritage object requires an integrated representation of various types of information in order to develop appropriate restoration or conservation strategies. Currently, there is extensive research focused on automatic procedures of segmentation and classification of 3D point clouds or meshes, which can accelerate the study of a monument and integrate it with heterogeneous information and attributes, useful to characterize and describe the surveyed object. The aim of this study is to propose an optimal, repeatable and reliable procedure to manage various types of 3D surveying data and associate them with heterogeneous information and attributes to characterize and describe the surveyed object. In particular, this paper presents an approach for classifying 3D heritage models, starting from the segmentation of their textures based on supervised machine learning methods. Experimental results run on three different case studies demonstrate that the proposed approach is effective and with many further potentials

From 2D to 3D supervised segmentation and classification for cultural heritage applications.

The digital management of architectural heritage information is still a complex problem, as a heritage object requires an integrated representation of various types of information in order to develop appropriate restoration or conservation strategies. Currently, there is extensive research focused on automatic procedures of segmentation and classification of 3D point clouds or meshes, which can accelerate the study of a monument and integrate it with heterogeneous information and attributes, useful to characterize and describe the surveyed object. The aim of this study is to propose an optimal, repeatable and reliable procedure to manage various types of 3D surveying data and associate them with heterogeneous information and attributes to characterize and describe the surveyed object. In particular, this paper presents an approach for classifying 3D heritage models, starting from the segmentation of their textures based on supervised machine learning methods. Experimental results run on three different case studies demonstrate that the proposed approach is effective and with many further potentials

D. Dininno, T. Sgrulloni, Le indagini nei fornici VI, X-XIII dell'emiciclo nord orientale del Circo Massimo

The excavation (recently 2013-2014 promoted by the Capitoline Superintendency to Cultural Heritage) in the north-eastern sector of the Circus maximus hemicycle has allowed deeper investigation into the structure and function of the fornices XIII, XII, XI, X and VI of the Palatine sector, further clarifying the tripartite spaztial arrangement, architecture and function. Significant to the study was the use of 3D photogrammetry. Given the wealth of ceramic materials found, a case study has been presented concerning the XII fornix, of wich the different phases of use have been reconstructed starting from the pottery found there. Thanks to this excavation it is possible to understand wich classes and types of ceramics were circulated in this area and point out chronologically, some crucial moments in the life of this fornix, especially those relating to its commercial vocation

FROM 2D TO 3D SUPERVISED SEGMENTATION AND CLASSIFICATION FOR CULTURAL HERITAGE APPLICATIONS

The digital management of architectural heritage information is still a complex problem, as a heritage object requires an integrated representation of various types of information in order to develop appropriate restoration or conservation strategies. Currently, there is extensive research focused on automatic procedures of segmentation and classification of 3D point clouds or meshes, which can accelerate the study of a monument and integrate it with heterogeneous information and attributes, useful to characterize and describe the surveyed object. The aim of this study is to propose an optimal, repeatable and reliable procedure to manage various types of 3D surveying data and associate them with heterogeneous information and attributes to characterize and describe the surveyed object. In particular, this paper presents an approach for classifying 3D heritage models, starting from the segmentation of their textures based on supervised machine learning methods. Experimental results run on three different case studies demonstrate that the proposed approach is effective and with many further potentials