Learning Representations for Animated Motion Sequence and Implied Motion Recognition

2012 ◽  
pp. 288-295 ◽  
Author(s):  
Georg Layher ◽  
Martin A. Giese ◽  
Heiko Neumann
Keyword(s):  
Motion Recognition ◽  
Implied Motion ◽  
Motion Sequence

The interaction of global motion and global form processing on the perception of implied motion: An equivalent noise approach

2021 ◽  
Vol 186 ◽  
pp. 34-40
Author(s):  
Mahesh R. Joshi ◽  
Anita J. Simmers ◽  
Seong T. Jeon
Keyword(s):  
Global Motion ◽  
Form Processing ◽  
Implied Motion ◽  
Global Form

Comparison of machine learning methods in sEMG signal processing for shoulder motion recognition

2021 ◽  
Vol 68 ◽  
pp. 102577
Author(s):  
Yang Zhou ◽  
Chaoyang Chen ◽  
Mark Cheng ◽  
Yousef Alshahrani ◽  
Sreten Franovic ◽  
...  
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Learning Methods ◽  
Semg Signal ◽  
Motion Recognition ◽  
Machine Learning Methods ◽  
Shoulder Motion

scholarly journals Application of human motion recognition technology in extreme learning machine

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142098321
Author(s):  
Anzhu Miao ◽  
Feiping Liu
Keyword(s):  
Extreme Learning Machine ◽  
Research Work ◽  
Human Motion ◽  
Feature Descriptor ◽  
Background Variable ◽  
Motion Recognition ◽  
Network Training ◽  
Other Information ◽  
Human Motion Recognition ◽  
Learning Machine

Human motion recognition is a branch of computer vision research and is widely used in fields like interactive entertainment. Most research work focuses on human motion recognition methods based on traditional video streams. Traditional RGB video contains rich colors, edges, and other information, but due to complex background, variable illumination, occlusion, viewing angle changes, and other factors, the accuracy of motion recognition algorithms is not high. For the problems, this article puts forward human motion recognition based on extreme learning machine (ELM). ELM uses the randomly calculated implicit network layer parameters for network training, which greatly reduces the time spent on network training and reduces computational complexity. In this article, the interframe difference method is used to detect the motion region, and then, the HOG3D feature descriptor is used for feature extraction. Finally, ELM is used for classification and recognition. The results imply that the method proposed here has achieved good results in human motion recognition.

A Projection-based Human Motion Recognition Algorithm based on Depth Sensors

2021 ◽  
pp. 1-1
Author(s):  
Mu-Chun Su ◽  
Pang-Ti Tai ◽  
Jieh-Haur Chen ◽  
Yi-Zeng Hsieh ◽  
Shu-Fang Lee ◽  
...  
Keyword(s):  
Recognition Algorithm ◽  
Human Motion ◽  
Motion Recognition ◽  
Depth Sensors ◽  
Human Motion Recognition

scholarly journals Intelligent Human Interface by means of Motion Recognition

1995 ◽  
Vol 7 (4) ◽  
pp. 871-882 ◽  
Author(s):  
Michihiro YOSHIHARA ◽  
Toru YAMAGUCHI
Keyword(s):  
Motion Recognition ◽  
Human Interface

sEMG-Based Motion Recognition

2018 ◽  
pp. 67-104 ◽  
Author(s):  
Qingsong Ai ◽  
Quan Liu ◽  
Wei Meng ◽  
Sheng Quan Xie
Keyword(s):  
Motion Recognition
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