An Autonomous and User-Independent Hand Posture Recognition System for Vision-Based Interface Tasks

2006 ◽  
pp. 113-122
Author(s):  
Elena Sánchez-Nielsen ◽  
Luis Antón-Canalís ◽  
Cayetano Guerra-Artal
Keyword(s):  
Recognition System ◽  
Hand Posture ◽  
Hand Posture Recognition ◽  
Posture Recognition

Intelligent Hand Posture Recognition System Integrated to Process Control

2017 ◽  
Vol 15 (6) ◽  
pp. 1144-1153 ◽  
Author(s):  
Y.L. Coelho ◽  
J.M. Salomao ◽  
H.R. Kulitz
Keyword(s):  
Process Control ◽  
Recognition System ◽  
Hand Posture ◽  
Hand Posture Recognition ◽  
Posture Recognition

Development of hand posture recognition system for finger gaming robot

2013 ◽  
Author(s):  
Chyi-Yeu Lin ◽  
Li-Wen Chuang ◽  
Chun-Chia Huang ◽  
Ke-Jeng Lin ◽  
Chin-Shyurng Fahn
Keyword(s):  
Recognition System ◽  
Hand Posture ◽  
Hand Posture Recognition ◽  
Posture Recognition

A Real-Time Hand Posture Recognition System Using Deep Neural Networks

2015 ◽  
Vol 6 (2) ◽  
pp. 1-23 ◽  
Author(s):  
Ao Tang ◽  
Ke Lu ◽  
Yufei Wang ◽  
Jie Huang ◽  
Houqiang Li
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Deep Neural Networks ◽  
Recognition System ◽  
Hand Posture ◽  
Hand Posture Recognition ◽  
Posture Recognition

Hand posture Recognition for shape variant hands

2011 ◽  
Vol 6 (4) ◽  
pp. 1-6
Author(s):  
Ayesha Butalia ◽  
◽  
A.K. Ramani ◽  
Parag Kulkarni ◽  
Swapnil Patil ◽  
...  
Keyword(s):  
Hand Posture ◽  
Hand Posture Recognition ◽  
Posture Recognition

scholarly journals Approach to hand posture recognition based on hand shape features for human–robot interaction

2021 ◽  
Author(s):  
Jing Qi ◽  
Kun Xu ◽  
Xilun Ding
Keyword(s):  
Gaussian Mixture ◽  
Human Robot Interaction ◽  
Polar Coordinates ◽  
Support Vector ◽  
Hand Posture ◽  
Data Set ◽  
Hand Shape ◽  
Hand Posture Recognition ◽  
Hand Segmentation ◽  
Posture Recognition

AbstractHand segmentation is the initial step for hand posture recognition. To reduce the effect of variable illumination in hand segmentation step, a new CbCr-I component Gaussian mixture model (GMM) is proposed to detect the skin region. The hand region is selected as a region of interest from the image using the skin detection technique based on the presented CbCr-I component GMM and a new adaptive threshold. A new hand shape distribution feature described in polar coordinates is proposed to extract hand contour features to solve the false recognition problem in some shape-based methods and effectively recognize the hand posture in cases when different hand postures have the same number of outstretched fingers. A multiclass support vector machine classifier is utilized to recognize the hand posture. Experiments were carried out on our data set to verify the feasibility of the proposed method. The results showed the effectiveness of the proposed approach compared with other methods.

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