Multi-modal user interaction method based on gaze tracking and gesture recognition

2013 ◽  
Vol 28 (2) ◽  
pp. 114-126 ◽  
Author(s):  
Heekyung Lee ◽  
Seong Yong Lim ◽  
Injae Lee ◽  
Jihun Cha ◽  
Dong-Chan Cho ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
User Interaction ◽  
Interaction Method ◽  
Gaze Tracking

An intuitive user interaction method using multi-sensors for pencil drawing filter of NPR rendering in mobile devices

2015 ◽  
Vol 74 (7) ◽  
pp. 2371-2389
Author(s):  
Jong-Jin Jung ◽  
Ji-Yeon Kim ◽  
Hyun-Sook Chung ◽  
Pan-Seop Shin
Keyword(s):  
Mobile Devices ◽  
User Interaction ◽  
Interaction Method ◽  
Pencil Drawing

Mandos: A User Interaction Method in Embedded Applications for Mobile Telephony

2008 ◽  
Author(s):  
Mauro Teofilo ◽  
Lucas Cordeiro ◽  
Raimundo Barreto ◽  
Jose Raimundo Pereira ◽  
Ayres Mardem ◽  
...  
Keyword(s):  
User Interaction ◽  
Mobile Telephony ◽  
Interaction Method ◽  
Embedded Applications

An Automatic Interaction Method Using Part Recognition Based on Deep Network for Augmented Reality Assembly Guidance

2018 ◽  
Author(s):  
Xuyue Yin ◽  
Xiumin Fan ◽  
Jiajie Wang ◽  
Rui Liu ◽  
Qiang Wang
Keyword(s):  
Neural Network ◽  
Augmented Reality ◽  
Real Time ◽  
Deep Neural Network ◽  
Back Propagation ◽  
User Interaction ◽  
Guidance System ◽  
Assembly Process ◽  
Interaction Method ◽  
Assembly Guidance

Assembly process of complex electromechanical products can be quite complicated and time consuming because of high quality demands. Aiming at improving the efficiency of the manual assembly process, this paper proposes an automatic interaction method using part recognition for augmented reality (AR) assembly guidance, which improves both the accuracy of part picking and the interaction efficiency of AR guidance system. Taking sample images of similar parts as input and part types as output, a deep neural network model Part R-CNN for part recognition is build based on Faster R-CNN and is further fine-tuned by back propagation. By recognizing the assembly part, the augmented assembly guidance information of the corresponding parts assembly process is triggered in real-time without direct user interaction. Experimental results show that the deep neural network based part recognition method reaches 94% on mean average precision and the average recognition speed is 200ms per image frame. The average speed of AR guidance content triggering is about 20fps. All system performance satisfies the accuracy and real-time requirements of the AR-aided assembly system.

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