DocumentCode
1710429
Title
Real-time hand postures recognition using low computational complexity Artificial Neural Networks and Support Vector Machines
Author
Bragatto, Ticiano A C ; Ruas, Gabriel S I ; Lamar, Marcus V.
Author_Institution
Dept. of Electr. Eng., Brasilia Univ., Brasilia
fYear
2008
Firstpage
1530
Lastpage
1535
Abstract
This paper proposes two main techniques for reduce computational complexity on artificial neural networks, using piecewise linear activation function, and support vector machines built on a probability based binary tree. These methods are compared with well-known classifiers based on the computational complexity, correct rate and time taken to process the required information. The results show that probability based binary tree SVM has an equivalent recognition rate and is faster than ANNs.
Keywords
computational complexity; neural nets; pose estimation; probability; support vector machines; trees (mathematics); artificial neural network; binary tree; low computational complexity; piecewise linear activation function; probability; real-time hand posture recognition; support vector machine; Artificial neural networks; Binary trees; Computational complexity; Feature extraction; Fingers; Handicapped aids; Humans; Real time systems; Support vector machine classification; Support vector machines; Artificial Neural Networks; Hand Posture Recognition; Support Vector Machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Communications, Control and Signal Processing, 2008. ISCCSP 2008. 3rd International Symposium on
Conference_Location
St Julians
Print_ISBN
978-1-4244-1687-5
Electronic_ISBN
978-1-4244-1688-2
Type
conf
DOI
10.1109/ISCCSP.2008.4537470
Filename
4537470
Link To Document