FROM 2D TO 3D SUPERVISED SEGMENTATION AND CLASSIFICATION FOR CULTURAL HERITAGE APPLICATIONS
| Autor: | E. Grilli, D. Dininno, G. Petrucci, F. Remondino |
|---|---|
| Přispěvatelé: | E. Grilli, D. Dininno, G. Petrucci, F. Remondino |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
lcsh:Applied optics. Photonics
010504 meteorology & atmospheric sciences Computer science Classification Segmentation Cultural Heritage Machine Learning Random Forest 0211 other engineering and technologies Point cloud 02 engineering and technology Machine learning computer.software_genre lcsh:Technology 01 natural sciences Segmentation 021101 geological & geomatics engineering 0105 earth and related environmental sciences lcsh:T business.industry Representation (systemics) lcsh:TA1501-1820 2D to 3D conversion Object (computer science) Random forest Cultural heritage lcsh:TA1-2040 Artificial intelligence lcsh:Engineering (General). Civil engineering (General) business computer |
| Zdroj: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-2, Pp 399-406 (2018) |
| ISSN: | 2194-9034 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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