A Real Time Vision System Based on Deep Learning for Gesture Based Human Machine Interaction

2020 ◽  
pp. 561-572
Author(s):  
Alberto Tellaeche Iglesias ◽  
Iker Pastor-López ◽  
Borja Sanz Urquijo ◽  
Pablo García-Bringas
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Vision System ◽  
Human Machine Interaction ◽  
Machine Interaction

Human-machine interaction for real-time linear optimization

2012 ◽  
Author(s):  
Simon Hamel ◽  
Jonathan Gaudreault ◽  
Claude-Guy Quimper ◽  
Mathieu Bouchard ◽  
Philippe Marier
Keyword(s):  
Real Time ◽  
Linear Optimization ◽  
Human Machine Interaction ◽  
Machine Interaction ◽  
Time Linear

Deep Learning for EMG-based Human-Machine Interaction: A Review

2021 ◽  
Vol 8 (3) ◽  
pp. 512-533
Author(s):  
Dezhen Xiong ◽  
Daohui Zhang ◽  
Xingang Zhao ◽  
Yiwen Zhao
Keyword(s):  
Deep Learning ◽  
Human Machine Interaction ◽  
Machine Interaction

scholarly journals Hand Gesture Recognition for Real Time Human Machine Interaction System

2015 ◽  
Vol 19 (5) ◽  
pp. 262-264 ◽  
Author(s):  
Poonam Sonwalkar ◽  
◽  
Tanuja Sakhare ◽  
Ashwini Patil ◽  
Sonal Kale
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Human Machine Interaction ◽  
Interaction System ◽  
Machine Interaction

scholarly journals Strawberry Fungal Leaf Scorch Disease Identification in Real-Time Strawberry Field Using Deep Learning Architectures

Plants ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 2643
Author(s):  
Irfan Abbas ◽  
Jizhan Liu ◽  
Muhammad Amin ◽  
Aqil Tariq ◽  
Mazhar Hussain Tunio
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Agricultural Development ◽  
Vision System ◽  
Severe Disease ◽  
Disease Diagnosis ◽  
Plant Diseases ◽  
Leaf Scorch ◽  
Disease Identification ◽  
Severe Stage

Plant health is the basis of agricultural development. Plant diseases are a major factor for crop losses in agriculture. Plant diseases are difficult to diagnose correctly, and the manual disease diagnosis process is time consuming. For this reason, it is highly desirable to automatically identify the diseases in strawberry plants to prevent loss of crop quality. Deep learning (DL) has recently gained popularity in image classification and identification due to its high accuracy and fast learning. In this research, deep learning models were used to identify the leaf scorch disease in strawberry plants. Four convolutional neural networks (SqueezeNet, EfficientNet-B3, VGG-16 and AlexNet) CNN models were trained and tested for the classification of healthy and leaf scorch disease infected plants. The performance accuracy of EfficientNet-B3 and VGG-16 was higher for the initial and severe stage of leaf scorch disease identification as compared to AlexNet and SqueezeNet. It was also observed that the severe disease (leaf scorch) stage was correctly classified more often than the initial stage of the disease. All the trained CNN models were integrated with a machine vision system for real-time image acquisition under two different lighting situations (natural and controlled) and identification of leaf scorch disease in strawberry plants. The field experiment results with controlled lightening arrangements, showed that the model EfficientNet-B3 achieved the highest classification accuracy, with 0.80 and 0.86 for initial and severe disease stages, respectively, in real-time. AlexNet achieved slightly lower validation accuracy (0.72, 0.79) in comparison with VGGNet and EfficientNet-B3. Experimental results stated that trained CNN models could be used in conjunction with variable rate agrochemical spraying systems, which will help farmers to reduce agrochemical use, crop input costs and environmental contamination.

Towards Web 2.0 Applications

2010 ◽  
pp. 75-95
Author(s):  
Alessandro Bozzon ◽  
Sara Comai ◽  
Piero Fraternali ◽  
Giovanni Toffetti Carughi
Keyword(s):  
Web 2.0 ◽  
Conceptual Model ◽  
Real Time ◽  
Web Application ◽  
Multimedia Content ◽  
Human Machine Interaction ◽  
Rich Internet Application ◽  
Internet Application ◽  
Machine Interaction ◽  
Independent Model

This chapter introduces a conceptual model for the design of Web 2.0 applications relying on rich Internet application (RIA) technologies. RIAs extend Web application features by allowing computation to be partitioned between the client and the server and support core Web 2.0 requirements, like real-time collaboration among users, sophisticated presentation and manipulation of multimedia content, and flexible human-machine interaction (synchronous and asynchronous, connected and disconnected). The proposed approach for the design of Web 2.0 applications extends a conceptual platform-independent model conceived for Web 1.0 applications with novel primitives capturing RIA features; the conceptual model can be automatically converted into implementations in all the most popular RIA technologies and frameworks like AJAX, OpenLaszlo, FLEX, AIR, Google Gears, Google Web toolkit, and Silverlight.

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