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논문검색

Gesture Recognition Using Higher Correlation Feature Information and PCA

원문정보

초록

영어

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

저자정보

  • Jong-Min Kim Computer Science and Statistic Graduate School, Chosun University, Gwangju, 501-709, Korea
  • Kee-Jun Lee Department of Health Education & Information, Gwangju Health College, Gwangju, 506-701, Korea

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