A New Hand Shape Recognition Algorithm Based on Delaunay Triangulation

2018 ◽  
pp. 22-28
Author(s):  
Fu Liu ◽  
Shoukun Jiang ◽  
Bing Kang ◽  
Tao Hou
Keyword(s):  
Delaunay Triangulation ◽  
Shape Recognition ◽  
Recognition Algorithm ◽  
Hand Shape

Clustered Dynamic Graph CNN for Biometric 3D Hand Shape Recognition

2020 ◽  
Author(s):  
Jan Svoboda ◽  
Pietro Astolfi ◽  
Davide Boscaini ◽  
Jonathan Masci ◽  
Michael Bronstein
Keyword(s):  
Shape Recognition ◽  
Dynamic Graph ◽  
Hand Shape

Hand shape recognition

2000 ◽  
Vol 32 (1) ◽  
pp. 1-5 ◽  
Author(s):  
Yun Long Lay
Keyword(s):  
Shape Recognition ◽  
Hand Shape

Real-time hand shape recognition by orientation invariant data learning for smart TV

2013 ◽  
Author(s):  
Jae-Joon Han ◽  
Changkyu Choi ◽  
ByungIn Yoo ◽  
Dusik Park ◽  
Changyeong Kim
Keyword(s):  
Real Time ◽  
Shape Recognition ◽  
Hand Shape ◽  
Smart Tv

Fusion of Image Contour Moments and Fourier Descriptors for the Hand Gesture Recognition

2012 ◽  
Vol 433-440 ◽  
pp. 5188-5192
Author(s):  
Hai Long Lei ◽  
Sheng Yang
Keyword(s):  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Fourier Descriptor ◽  
Bp Network ◽  
Moment Invariant ◽  
Hand Shape ◽  
Image Contour

Hand is a highly variable organ and hand features are easily affected by environmental factors. Considering the characteristics of hand gesture, a novel hand gesture recognition algorithm based on hybrid moments is presented. First, According to the color cue, the hand shape is available to extract from the complicated background, then the contour moment invariant and Fourier Descriptor are extracted and fused into a hybrid feature, finally the hybrid feature are put into the BP network to identity. The experimental results show that the method has better robustness and higher recognition rate.

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