Vision-Based Human–Machine Interactive Operation Technology for Gesture Recognition, Motion Capture and Multi-Touch
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".
Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 668
Special Issue Editors
Interests: artificial intelligence; intelligent image analytics; embedded systems; intelligent vehicles; smart manufacturing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Human–machine interactive operation technology plays an important role in many application fields. Nowadays, friendly human–machine interactive operation technology allows higher effectiveness and productivity in many different application areas, such as smart manufacturing, intelligent robots, autonomous vehicles, smart home, etc. Among several approaches of human–machine interactive operation technology, the vision-based technique is one of the most popular ones in recent years. The vision-based technique is performed based on the vision information from 2D image sensors or 3D RGB-depth sensors. Through machine learning techniques applied to the visual information, gesture recognition, motion capture, and multi-touch techniques have been implemented and widely applied on the human–machine interface (HMI) to allow users to interact and communicate with machines, such as manufacturing machines, robots, or autonomous vehicles. Furthermore, the advent of deep learning techniques has been successfully applied to such scenarios. As such, the Special Issue “Vision-Based Human–Machine Interactive Operation Technology for Gesture Recognition, Motion Capture and Multi-Touch” aims to bring together leading academic research results of vision-based gesture recognition, motion capture, and multi-touch techniques that are emerging into human–machine interaction operations.
The topics of interest for this Special Issue include, but are not limited to, the following:
- Vision-based human–machine interactive techniques for smart manufacturing, intelligent robots, autonomous vehicles, smart home, etc.
- Gesture recognition, motion capture, and multi-touch for human–machine interactive interfaces.
- Deep learning and machine learning techniques for human–machine interaction applications.
- Lightweight techniques for vision-based human–machine interactive techniques on embedded or wearable devices.
- Computer vision and image processing for human–machine interaction applications.
- Novel ideas and frameworks for developing intelligent human–machine interaction systems.
Prof. Dr. Yen-Lin Chen
Prof. Dr. Hsin-Han Chiang
Guest Editors
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Keywords
- vision-based human–machine interactive techniques
- gesture recognition
- motion capture
- multi-touch
- deep learning
- machine learning
- lightweight techniques
- embedded and wearable devices
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