Detection of Inflection Point of Waveform Using Wavelet Thresholding and Natural Observation Filter

웨이브릿 임계치와 자연관측필터를 이용한 파형의 변곡점 검출

  • Kim, Tae-Soo (Division of Information and Communication Engineering, Uiduk University)
  • 김태수 (위덕대학교 정보통신공학부)
  • Published : 2005.07.01

Abstract

The curve of motion indicated to waveform of the fast movement of human extracted using virtual reality or the quantity of time fluctuation of the electromagnetic signal as the quantity of electric fluctuation of the atmosphere is complex. It is important to decide exactly the signal property as the inflection point for the observation signal. When the signal is mixed by noise signal, the traditional method is difficult to detect the inflection point. In this paper the noisy signal is eliminated by wavelet thresholding method and the filter using natural observation theorem is applied. It shows that the inflection point of the signal waveform can be detected exactly.

가상현실을 이용한 사람의 빠른 움직임을 추출하여 파형으로 나타내는 동작곡선이나 자연계에서 실제로 관측되는 대기 전기변동량과 같은 전자계 신호의 시간 변동량은 매우 복잡하다. 이러한 신호의 관측파형에 대하여 변곡점과 같은 신호의 특징을 정확히 결정하는 것이 중요하다. 잡음이 신호에 중첩될 때 종래의 방법으로는 변곡점의 검출이 어렵다. 본 논문에서는 신호에 잡음이 첨가될 때 웨이브릿 임계치 잡음제거와 정규형 자연관측필터를 적용하여 정확한 변곡점 추출이 가능함을 보인다.

Keywords

References

  1. Bindiganvavale R. and Badler N. I., 'Motion abstraction and mapping with spatial constraints. In Modeling and Motion capture Techniques for Virtual Environments', International Workshop, CAPTECH'98, pages 70-82, Nov. 1998
  2. Taizo IIJIMA and Manoru IWAKI,' Fundamental Theory of Natural Observation Method with Complete Reconstruction Property by Finite Sum -Natural Observation Theory of Normal type- ', Journal of ICICE (A), Vol. J79-A, no. 1, pp. 77-87, Jan. 1996
  3. Manoru IWAKI and Taizo IIJIMA, 'Natural Observation Method Discrete-Time Waveforms', Journal of MCE (A), Vol. J79-A, no. 3, pp. 728-735, March. 1996
  4. Kan OKUBO and Nobunao TAKEUCHI, 'Detection of Inflection Point of Time Series Data by Natural Observation Filter', Journal of ICICE (A), Vol. J86-A, no. 11, pp. 1170-1178, November. 2003
  5. A. V. Oppenheim and R. W. Schafer, 'Digital Signal Processing', Englewood Cliffs, NJ: Prentice-Hall, 1975
  6. R. M. Rao, A.S. Bopardikar, 'Wavelet Transforms Introduction to Theory and Applications' Addison-Wesley, 1998
  7. D.L. Donoho, and I. Johnstone,' Ideal adaptation via wavelet shrinkage', Biometrika, vol. 81, pp. 425-455, 1994 https://doi.org/10.1093/biomet/81.3.425