scholarly journals Investigating cultural aspects in the fundamental diagram using convolutional neural networks and virtual agent simulation

2019 ◽  
Vol 30 (3-4) ◽  
Author(s):  
Rodolfo Migon Favaretto ◽  
Roberto Rosa dos Santos ◽  
Soraia Raupp Musse ◽  
Felipe Vilanova ◽  
Angelo Brandelli Costa
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Virtual Agent ◽  
Fundamental Diagram ◽  
Cultural Aspects ◽  
Agent Simulation

scholarly journals Investigating cultural aspects in the fundamental diagram using convolutional neural networks

2018 ◽  
Author(s):  
Rodolfo Migon Favaretto ◽  
Roberto Rosa dos Santos ◽  
Soraia Raupp Musse ◽  
Felipe Vilanova ◽  
Ângelo Brandelli Costa
Keyword(s):  
Neural Networks ◽  
Cultural Differences ◽  
Convolutional Neural Networks ◽  
Personal Space ◽  
Group Behavior ◽  
Video Sequences ◽  
Relevant Feature ◽  
Fundamental Diagram ◽  
Cultural Aspects ◽  
Neighbor Relation

AbstractThis paper presents a study regarding group behavior in a controlled experiment focused on differences in an important attribute that vary across cultures - the personal spaces - in two Countries: Brazil and Germany. In order to coherently compare Germany and Brazil evolutions with same population applying same task, we performed the pedestrian Fundamental Diagram experiment in Brazil, as performed in Germany. We use convolutional neural networks to detect and track people in video sequences. With this data, we use Voronoi Diagrams to find out the neighbor relation among people and then compute the walking distances to find out the personal spaces. Based on personal spaces analyses, we found out that people behavior is more similar in high dense populations. So, we focused our study on cultural differences between the two Countries in low and medium densities. Results indicate that personal space analyses can be a relevant feature in order to understand cultural aspects in video sequences even when compared with data from self-reported questionnaires.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Convolutional Neural Networks for Underwater Pipeline Segmentation using Imperfect Datasets

2021 ◽  
Author(s):  
Edgar Medina ◽  
Roberto Campos ◽  
Jose Gabriel R. C. Gomes ◽  
Mariane R. Petraglia ◽  
Antonio Petraglia
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Underwater Pipeline
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