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픽셀 연결성 추적을 이용한 의사 특징점 제거

Pseudo Feature Point Removal using Pixel Connectivity Tracing

  • 김강 (강원관광대학 관광정보처리과) ;
  • 이건익 (강원대학교 컴퓨터공학과)
  • Kim, Kang (Dept. of Tourism Information Processing, Kangwon Tourism University) ;
  • Lee, Keon-Ik (Dept. of Computer Science, Kangwon University)
  • 투고 : 2011.04.20
  • 심사 : 2011.06.01
  • 발행 : 2011.08.31

초록

본 논문에서는 픽셀 연결성 추적을 이용한 의사 특징점 제거에 관하여 연구하였다. 특징점을 추출하는 방법에는 교차수를 이용한 방법이 있다. 그러나 교차수를 이용한 방법에서는 의사 특징점이 많이 추출된다. 교차수를 이용한 방법에서 잘못 추출된 특징점들을 제거하기 위하여 단점과 분기점 주위에 있는 8개 픽셀을 추적하여 조건을 만족하는 경우 실제 특징점으로 추출하고 조건을 만족하지 않는 경우 의사 특징점이므로 제거하였다. 성능 평가를 위하여 교차수를 이용한 방법과 픽셀 연결성 추적을 이용하여 추출된 실제 특징점을 비교하였으며, 실험결과 픽셀 연결성 추적을 이용하여 궁상문형, 와상문형, 제상문형에 대하여 의사특징점이 각각 47%, 40%, 30% 제거되었음을 알 수 있었다.

In this paper, using pixel connectivity tracking feature to remove a doctor has been studied. Feature extraction method is a method using the crossing. However, by crossing a lot of feature extraction method sis a doctor. Extracted using the method of crossing the wrong feature to remove them from the downside and the eight pixels around the fork to trace if it satisfies the conditions in the actual feature extraction and feature conditions are not satisfied because the doctor was removed. To evaluate the performance using crossing methods and extracted using pixel connectivity trace was compared to the actual feature, the experimental results using pixel connectivity trace arcuate sentence, croissants sentence, sentence the defrost feature on your doctor about47%, respectively, 40%, 30%were found to remove.

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참고문헌

  1. Seong young-jin, kimkyung-Hwan, "Quality estima tion and classification of minutiae-based fingerprint matching algorithm using a Delaunary Triangulation", Institute of Multimedia Chapter 13 No. 4, 2010.
  2. W. Chen and Y. Gao, "A minutiae-based fingerprint mat hching alogorithm using phase correlation", in 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications, pp. 233-238, 2007.
  3. Lee Eun-jung, "the fingerprint image and fingerprint classifi cation through a combination of directional features", Hanshin University Graduate School Master's Thesis, 2010.
  4. Kaohsiung, "can be changed for user authentication of fingerprint generation techniques". soonchun hyang University Graduate School Master's Thesis, 2010.
  5. Lee Keon-Ik, "Fingerprint recognition features for improved detection RPAOC Study", Kanto Univer sity doctoral dissertation, 2005.
  6. Marius Tico and Pauli Kuosmanen, "An Algorithm for Fingerprint Image Postprocessing", Proceedings of the Conference record of The Thrity-Fourth Asilomar Conference on Signals, Systems & Computers - Volume 2, pp. 1735-1739, 2000.
  7. Son gmyeong-cheol, "Fingerprint Reference Point De tection Using orientation information and fingerp int authentication system", Korea Univer sity Master Thesis, 2002.
  8. jang dong-hyeok,"Implementation of Digital Image Processing", Information Gate, 2002
  9. Marius Tico and Eero Immonen and Pauli Ramo and pauli Kuosmanen and Jukka Saarinen, "Fingerprint Recognition Using Wavelet Features", Proceedings of the IEEE International Symposiumon Circuits and Systems, Vol. 2, pp. 21-24, 2001.
  10. Marius Tico and Pauli Kuosmanen, "An Algorithm for Fingerprint Image Postprocessing", Proceedings of the Conference record of The Thrity-Fourth Asilomar Conference on Signals, Systems & Computers - Volume 2, pp. 1735-1739, 2000
  11. YuKo Mizuhara. AKira Hayashi, Nobuo Suematsu, "Embedd ing of time series data by using dynamic time warping distances," Systems and Computers in Japan. Vol 37, No 3 pp. 1-9, 2006. https://doi.org/10.1002/scj.20486
  12. Zhu Hao, Qianwei Lei, "Vision-Based Interface; Using Face and Eye Blinking Tracking with Camera", Second International Symposium on Intelligent Information Technology Application, 2008.
  13. Engin Avci, Derya Avci, "An expert systembased on fuzzy entropy for automatic threshold selection in image processing", Expert Systems with Applications, Vol, 36, pp. 3077-3085, 2009. https://doi.org/10.1016/j.eswa.2008.01.027
  14. Geng Zhang, Nanning Zheng, Chao Cui, Yuzhen Ya and Zejian yuan "An Efficient Road Detection Method in Noisy Urban Environment" IEEE Intellgent Vehicles Symposium 03 June, 2009.
  15. Keon-LK Lee, Kang Kim, "Pseudo feature point re oval using 8-neighbors connection sum", Korea institute of computer and Information Winter Conference, Vol 18-1, pp. 117-120, January, 2010.