ARCHITECTURE RECOGNITION BY MEANS OF CONVOLUTIONAL NEURAL NETWORKS

被引：2

作者：

Andrianaivo, Louis N. ^{[1
,2
]}

D'Autilia, Roberto ^{[2
]}

Palma, Valerio ^{[1
]}

机构：

[1] Politecn Torino, FULL, Via Agostino da Montefeltro 2, I-10134 Turin, Italy

[2] Univ Roma Tre, Dipartimento Matemat & Fis, Largo San Leonardo Murialdo 1, I-00146 Rome, Italy

来源：

27TH CIPA INTERNATIONAL SYMPOSIUM: DOCUMENTING THE PAST FOR A BETTER FUTURE | 2019年 / 42-2卷 / W15期

关键词：

Artificial Intelligence; Machine Learning; Deep Learning; Convolutional Neural Networks; Image Classification; Architectural Heritage; Mobile Computing; DEEP;

D O I：

10.5194/isprs-archives-XLII-2-W15-77-2019

中图分类号：

K85 [文物考古];

学科分类号：

0601 ;

摘要：

The use of mobile computing technologies can change the experience of visiting cultural sites by making vast digital heritage collections accessible on site. The spread of machine learning technologies on mobile devices is encouraging the interaction of artificial intelligence with the shape of the built environment. However, while some research already applies deep learning image recognition in an urban context, the literature on how to develop effective neural networks to detect architectural features is still limited, as well as the availability of architecture-related datasets. This work presents the steps and results of the prototype development of a mobile app to perform monument recognition using convolutional neural networks. The tool allows users to interact with the physical space and access a digital archive of texts, models, images and other data.

引用

页码：77 / 84

页数：8

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