scholarly journals Smart Doll: Emotion Recognition Using Embedded Deep Learning

Symmetry ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 387 ◽  
Author(s):  
Jose Espinosa-Aranda ◽  
Noelia Vallez ◽  
Jose Rico-Saavedra ◽  
Javier Parra-Patino ◽  
Gloria Bueno ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Ethical Issues ◽  
Real Life ◽  
Graphic Processing Units ◽  
Cloud Processing ◽  
Embedded Computer Vision ◽  
Embedded Computer ◽  
Learning Techniques ◽  
Learning Capabilities

Computer vision and deep learning are clearly demonstrating a capability to create engaging cognitive applications and services. However, these applications have been mostly confined to powerful Graphic Processing Units (GPUs) or the cloud due to their demanding computational requirements. Cloud processing has obvious bandwidth, energy consumption and privacy issues. The Eyes of Things (EoT) is a powerful and versatile embedded computer vision platform which allows the user to develop artificial vision and deep learning applications that analyse images locally. In this article, we use the deep learning capabilities of an EoT device for a real-life facial informatics application: a doll capable of recognizing emotions, using deep learning techniques, and acting accordingly. The main impact and significance of the presented application is in showing that a toy can now do advanced processing locally, without the need of further computation in the cloud, thus reducing latency and removing most of the ethical issues involved. Finally, the performance of the convolutional neural network developed for that purpose is studied and a pilot was conducted on a panel of 12 children aged between four and ten years old to test the doll.

scholarly journals DeepOSN: Bringing deep learning as malicious detection scheme in online social network

2020 ◽  
Vol 9 (1) ◽  
pp. 146
Author(s):  
Putra Wanda ◽  
Marselina Endah Hiswati ◽  
Huang J. Jie
Keyword(s):  
Social Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Online Social Networks ◽  
Link Prediction ◽  
Online Social Network ◽  
Detection Scheme ◽  
Malicious Activity ◽  
Manual Analysis ◽  
Learning Techniques

Manual analysis for malicious prediction in Online Social Networks (OSN) is time-consuming and costly. With growing users within the environment, it becomes one of the main obstacles. Deep learning is growing algorithm that gains a big success in computer vision problem. Currently, many research communities have proposed deep learning techniques to automate security tasks, including anomalous detection, malicious link prediction, and intrusion detection in OSN. Notably, this article describes how deep learning makes the OSN security technique more intelligent for detecting malicious activity by establishing a classifier model.

Tailoring design for embedded computer vision applications

Computer ◽  
2015 ◽  
Vol 48 (5) ◽  
pp. 58-62 ◽  
Author(s):  
Jason Schlessman ◽  
Marilyn Wolf
Keyword(s):  
Computer Vision ◽  
Embedded Computer Vision ◽  
Embedded Computer ◽  
Computer Vision Applications

Interleaved pixel lookup for embedded computer vision

2008 ◽  
Author(s):  
Kota Yamaguchi ◽  
Yoshihiro Watanabe ◽  
Takashi Komuro ◽  
Masatoshi Ishikawa
Keyword(s):  
Computer Vision ◽  
Embedded Computer Vision ◽  
Embedded Computer

scholarly journals Underwater Gesture Recognition Using Classical Computer Vision and Deep Learning Techniques

2020 ◽  
Vol 8 (1) ◽  
pp. 9-14
Author(s):  
Mygel Andrei M. Martija ◽  
Jakov Ivan S. Dumbrique ◽  
Prospero C., Jr Naval
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Gesture Recognition ◽  
Learning Techniques ◽  
Classical Computer

scholarly journals Deep Learning-Based Embedded System for Carabao Mango (Mangifera Indica L.) Sorting

2019 ◽  
Vol 8 (2) ◽  
pp. 5456-5462
Keyword(s):  
Deep Learning ◽  
Embedded System ◽  
Mangifera Indica ◽  
Raspberry Pi ◽  
Single Shot ◽  
Embedded Computer ◽  
Quality Classification ◽  
Learning Techniques ◽  
Multiple Classification ◽  
Basic Quality

This paper presents the design and development of an embedded system for ‘Carabao’ or Philippine mango sorting utilizing deep learning techniques. In particular, the proposed system initially takes as input a top view image of the mango, which is consequently rolled over to evaluate every sides. The input images were processed by Single Shot MultiBox Detector (SSD) MobileNet for mango detection and Multi-Task Learning Convolutional Neural Network (MTL-CNN) for classification/sorting ripeness and basic quality, running on an embedded computer, i.e. Raspberry Pi 3. Our dataset consisting of 2800 mango images derived from about 270 distinct mango fruits were annotated for multiple classification tasks, namely, basic quality (defective or good) and ripeness (green, semi-ripe, and ripe). The mango detection results achieved a total precision score of 0.92 and a mean average precision (mAP) of over 0.8 in the final checkpoint. The basic quality classification accuracy results were 0.98 and 0.92, respectively, for defective and good quality, while the ripeness classification for green, ripe, and semi-ripe were 1.0, 1.0, and 0.91, respectively. Overall, the results demonstrated the feasibility of our proposed embedded system for image-based Carabao mango sorting using deep learning techniques.

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