Multiple Object Scene Description for the Visually Impaired Using Pre-trained Convolutional Neural Networks

2016 ◽  
pp. 290-295
Author(s):  
Haikel Alhichri ◽  
Bilel Bin Jdira ◽  
Yacoub bazi ◽  
Naif Alajlan
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Visually Impaired ◽  
Multiple Object ◽  
Scene Description

scholarly journals INFERRING THE SCALE AND CONTENT OF A MAP USING DEEP LEARNING

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 17-24
Author(s):  
G. Touya ◽  
F. Brisebard ◽  
F. Quinton ◽  
A. Courtial
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Visually Impaired ◽  
The Other ◽  
Visually Impaired People ◽  
Impaired People ◽  
Tactile Maps

Abstract. Visually impaired people cannot use classical maps but can learn to use tactile relief maps. These tactile maps are crucial at school to learn geography and history as well as the other students. They are produced manually by professional transcriptors in a very long and costly process. A platform able to generate tactile maps from maps scanned from geography textbooks could be extremely useful to these transcriptors, to fasten their production. As a first step towards such a platform, this paper proposes a method to infer the scale and the content of the map from its image. We used convolutional neural networks trained with a few hundred maps from French geography textbooks, and the results show promising results to infer labels about the content of the map (e.g. ”there are roads, cities and administrative boundaries”), and to infer the extent of the map (e.g. a map of France or of Europe).

Multiple Object Extraction from Aerial Imagery with Convolutional Neural Networks

2016 ◽  
Vol 2016 (10) ◽  
pp. 1-9 ◽  
Author(s):  
Shunta Saito ◽  
Takayoshi Yamashita ◽  
Yoshimitsu Aoki
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Aerial Imagery ◽  
Object Extraction ◽  
Multiple Object

Traffic Lights Detection in adverse conditions using Convolutional Neural Networks

2018 ◽  
Author(s):  
George Symeonidis ◽  
Peter P. Groumpos ◽  
Evangelos Dermatas
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Lights ◽  
Adverse Conditions
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