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Sensor and Sensing Technologies in Multimedia Computing and Computer-Human-Interaction

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 3283

Special Issue Editors


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Guest Editor
Department of Mechanical Engineering, National Central University, Taoyuan 320317, Taiwan
Interests: computer vision; image processing; machine learning
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
College of EECS at the National Central University,Taiwan
Interests: Multimedia computing, computer-human-interaction, and distance learning

Special Issue Information

Dear Colleagues,

The advent of computer vision and image processing techniques has successfully applied to computer-human-interaction, reducing the gap between human and machines. Recently, the popularity of computer vision-based applications has significantly increased and plays an important role in the development of human-computer interaction. The human-computer interaction revolves not only to provide user-friendly input, but also to understand the meaning of human’s intention and then acts and respond in a more natural and intelligent way to interact with human. With the advancements of computer vision for human-computer interaction, we can enrich the more attractive human-computer interaction applications and develop potential influential research works. The special issue “Sensor and Sensing Technologies in Multimedia Computing and Computer-Human-Interaction” aims to bring together leading academic research results on all aspects of Multimedia and Human-Computer Interaction. It also provides a platform to discuss insightful concerns as well as practical challenges encountered and solutions adopted in the this special issue.

The topics of interest for the special issue include, but are not limited to the following:

  • Virtual reality, augmented reality, mixed reality for human-computer interaction applications
  • Deep learning neural networks for human-computer interaction applications
  • Machine learning for human-computer interaction applications
  • Sensor and Sensing Technologies for human-computer interaction applications
  • Computer vision and image processing for human-computer interaction applications
  • New mixed design of multimedia computing and computer-human-interaction
  • Novel ideas and frameworks for developing intelligence human-computer interaction systems

Dr. Chih-Yang Lin
Dr. Timothy K. Shih
Guest Editors

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Published Papers (1 paper)

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Research

16 pages, 4503 KiB  
Article
Egocentric-View Fingertip Detection for Air Writing Based on Convolutional Neural Networks
by Yung-Han Chen, Chi-Hsuan Huang, Sin-Wun Syu, Tien-Ying Kuo and Po-Chyi Su
Sensors 2021, 21(13), 4382; https://doi.org/10.3390/s21134382 - 26 Jun 2021
Cited by 6 | Viewed by 2765
Abstract
This research investigated real-time fingertip detection in frames captured from the increasingly popular wearable device, smart glasses. The egocentric-view fingertip detection and character recognition can be used to create a novel way of inputting texts. We first employed Unity3D to build a synthetic [...] Read more.
This research investigated real-time fingertip detection in frames captured from the increasingly popular wearable device, smart glasses. The egocentric-view fingertip detection and character recognition can be used to create a novel way of inputting texts. We first employed Unity3D to build a synthetic dataset with pointing gestures from the first-person perspective. The obvious benefits of using synthetic data are that they eliminate the need for time-consuming and error-prone manual labeling and they provide a large and high-quality dataset for a wide range of purposes. Following that, a modified Mask Regional Convolutional Neural Network (Mask R-CNN) is proposed, consisting of a region-based CNN for finger detection and a three-layer CNN for fingertip location. The process can be completed in 25 ms per frame for 640×480 RGB images, with an average error of 8.3 pixels. The speed is high enough to enable real-time “air-writing”, where users are able to write characters in the air to input texts or commands while wearing smart glasses. The characters can be recognized by a ResNet-based CNN from the fingertip trajectories. Experimental results demonstrate the feasibility of this novel methodology. Full article
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