Reconstruction of somatosensory interaction in Fuzhou Shadow Play based on depth image recognition

2021 ◽  
Author(s):  
Peng Shan ◽  
Wenzhi Wang ◽  
Wenxuan Zhao ◽  
Zishen Yang
Keyword(s):  
Image Recognition ◽  
Depth Image ◽  
Shadow Play

Body Recognition Based on Depth Image

2014 ◽  
Vol 631-632 ◽  
pp. 414-417
Author(s):  
Jian Zhang ◽  
Wan Juan Song
Keyword(s):  
Pattern Recognition ◽  
Image Recognition ◽  
The Body ◽  
Depth Image ◽  
Body Parts ◽  
Research Status ◽  
Kinect Camera ◽  
Local Gradient ◽  
Body Recognition

The text introduces the research status of depth image in the pattern recognition and the application in the body recognition. Aiming at the problem that the image recognition shot by common camera has declined performance under the factors of illumination, posture, shielding, and the like, the body parts are distinguished and judged by taking Kinect equipment promoted by Microsoft as the platform, analyzing the features of the depth picture obtained by the Kinect camera and putting forwards to the local gradient features of comprehensive point features and the gradient features; and the elbow is taken as the example to argue simply .

Automated Judgment for Chin-Up Exercises Based on Microsoft Kinect Image System

2014 ◽  
Vol 945-949 ◽  
pp. 1775-1779
Author(s):  
Su Fang Zhao ◽  
Bing Luo
Keyword(s):  
Image Segmentation ◽  
Image Recognition ◽  
The Other ◽  
Microsoft Kinect ◽  
Automated System ◽  
Depth Image ◽  
Image System ◽  
Physical Exercises ◽  
Key Points ◽  
Computer Based

Chin-up physical exercises need to be judged regular or foul and counted, the work can be automated finished by computer based on image recognition accurately and objectively. There are two difficulties in this automated system: one is to judge whether chin has been above the bar, the other is to determine whether chin-up arms have moment unbent state during sagging. This paper proposed judging chin above bar by Microsoft Kinect depth image and determining arm unbending by skeletal tracking. Depth image can help image segmentation, and skeletal tracking can position key points of arms. Experimental results showed that the approach could automate judge chin-up rapidly, accurately and practically.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

AN ALGORITHM FOR IMAGE RECOGNITION AND ANALYSIS USING NEURAL INFORMATION TECHNOLOGIES

2012 ◽  
Vol 71 (17) ◽  
pp. 1565-1574 ◽  
Author(s):  
O. M. Gafurov ◽  
V. I. Syryamkin ◽  
A. O. Gafurov ◽  
S. S. Stolyarova
Keyword(s):  
Image Recognition ◽  
Information Technologies ◽  
Neural Information

Formation geological depth image according to refraction and reflection marine seismic data

2017 ◽  
Vol 39 (6) ◽  
pp. 106-121
Author(s):  
A. O. Verpahovskaya ◽  
V. N. Pilipenko ◽  
Е. V. Pylypenko
Keyword(s):  
Seismic Data ◽  
Depth Image ◽  
Marine Seismic

New Techniques for Face Image Recognition Identification and Labeling

2007 ◽  
Vol 1 (4) ◽  
pp. 62-69
Author(s):  
Milhled Alfaouri ◽  
◽  
Nada N. Al-Ramahi ◽  
Keyword(s):  
Image Recognition ◽  
Face Image ◽  
New Techniques

Performance Evaluation Of Face Image Recognition Based Voila-Joins With SVM

2019 ◽  
pp. 161
Author(s):  
Jamal Mustafa Al-Tuwaijari ◽  
Suhad Ibrahim Mohammed
Keyword(s):  
Performance Evaluation ◽  
Image Recognition ◽  
Face Image
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