Performance Evaluation of a Hand Gesture Recognition System Using Fuzzy Algorithm and Neural Network for Post PC Platform
Identifieur interne : 005C39 ( Main/Exploration ); précédent : 005C38; suivant : 005C40Performance Evaluation of a Hand Gesture Recognition System Using Fuzzy Algorithm and Neural Network for Post PC Platform
Auteurs : Jung-Hyun Kim [Corée du Sud, États-Unis] ; Yong-Wan Roh [Corée du Sud, États-Unis] ; Jeong-Hoon Shin [Corée du Sud] ; Kwang-Seok Hong [Corée du Sud]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2006.
Abstract
Abstract: In this paper, we implement hand gesture recognition system using fuzzy algorithm and neural network for Post PC (the embedded-ubiquitous environment using blue-tooth module, embedded i.MX21 board and smart gate-notebook computer). Also, we propose most efficient and reasonable hand gesture recognition interface for Post PC through evaluation and analysis of performance about each gesture recognition system. The proposed gesture recognition system consists of three modules: 1) gesture input module that processes motion of dynamic hand to input data, 2) Relational Database Management System (hereafter, RDBMS) module to segment significant gestures from input data and 3) 2 each different recognition module: fuzzy max-min and neural network function recognition module to recognize significant gesture of continuous / dynamic gestures. Experimental result shows the average recognition rate of 98.8% in fuzzy max-min module and 96.7% in neural network recognition module about significantly dynamic gestures.
Url:
DOI: 10.1007/11676935_16
Affiliations:
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Also, we propose most efficient and reasonable hand gesture recognition interface for Post PC through evaluation and analysis of performance about each gesture recognition system. The proposed gesture recognition system consists of three modules: 1) gesture input module that processes motion of dynamic hand to input data, 2) Relational Database Management System (hereafter, RDBMS) module to segment significant gestures from input data and 3) 2 each different recognition module: fuzzy max-min and neural network function recognition module to recognize significant gesture of continuous / dynamic gestures. 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