This paper describes the algorithm that lowers the dimension, maintains the gesture recognition and significantly reduces the eigenspace configuration time by combining the higher correlation feature information and Principle Component Analysis. Since the suggested method doesn't require a lot of computation than the method using existing geometric information or stereo image, the fact that it is very suitable for building the real-time system has been proved through the experiment. In addition, since the existing point to point method which is a simple distance calculation has many errors, in this paper to improve recognition rate the recognition error could be reduced by using several successive input images as a unit of recognition with K-Nearest Neighbor which is the improved Class to Class method.
목차
Abstract 1. Introduction 2. Preprocessing 2.1. Background Removal 2.1. Higher Order Local Auto-correlation Features Generation 3. Space Generation Using PCA 4. Distance Evaluation and Gesture Recognition Using Improved K-NN 5. Experiment Results 6. Conclusion References
키워드
Eigen SpacePCA(Principal Component Analysis)HLAF(Higher order Local Auto Correlation Features)
저자
Jong-Min Kim [ Computer Science and Statistic Graduate School, Chosun University, Gwangju, 501-709, Korea ]
Corresponding author
Kee-Jun Lee [ Department of Health Education & Information, Gwangju Health College, Gwangju, 506-701, Korea ]
조선대학교 기초과학연구원 [The Natural Science Research Institute of Chosun]
설립연도
2008
분야
자연과학>자연과학일반
소개
본 연구원은 기초과학을 진흥하기 위한 연구·교육 및 그 보급을 목적으로 한다. 이 목적을 달성하기 위하여 다음 각 호의 사업을 수행한다.
1. 기초과학 제 분야에 관한 조사와 연구
2. 기초과학에 관한 학술행사(학술대회, 학술세미나, 심포지엄, 초청강연회 등) 개최
3. 학문후속세대 및 일반인을 위한 기초과학 교육
4. 기관지『조선자연과학논문지』 발간
5. 『자연과학연구총서』, 『자연과학번역총서』 등 단행본 발간
6. 기타 본 연구원의 목적과 관련된 사업
간행물
간행물명
통합자연과학논문집(구 조선자연과학논문집) [Journal of Integrative Natural Science]