ARCHITECTURE RECOGNITION BY MEANS OF CONVOLUTIONAL NEURAL NETWORKS
| Autor: | Valerio Palma, R. D'Autilia, L. N. Andrianaivo |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
lcsh:Applied optics. Photonics
Architectural Heritage Computer science Mobile computing Context (language use) lcsh:Technology Convolutional neural network Mobile Computing Machine Learning 03 medical and health sciences Deep Learning 0302 clinical medicine Artificial Intelligence Human–computer interaction Architecture 030304 developmental biology 0303 health sciences Image Classification Contextual image classification Artificial neural network lcsh:T business.industry Deep learning Convolutional Neural Networks lcsh:TA1501-1820 lcsh:TA1-2040 Artificial intelligence lcsh:Engineering (General). Civil engineering (General) business Mobile device 030217 neurology & neurosurgery |
| Zdroj: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-2-W15, Pp 77-84 (2019) |
| ISSN: | 2194-9034 |
| DOI: | 10.5194/isprs-archives-xlii-2-w15-77-2019 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|