KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Multi-Parameter Operation Method for Robust Disparity Plane

강건한 시차 평면을 위한 다중 파라미터 연산 기법

  • 김현정 (건국대학교 컴퓨터공학과) ;
  • 원일용 (서울호서전문학교 사이버해킹보안과) ;
  • 이창훈 (건국대학교 컴퓨터공학과)
  • Received : 2015.03.04
  • Accepted : 2015.04.16
  • Published : 2015.05.31
Download PDF
⟨ Previous Next ⟩

Abstract

Although many different methods have been used to solve stereo correspondent problems, the deviation of accuracy is too big. Among those many methods, the one that uses segmentation information of input image has received high attention in academic field since it is very close to vision recognition. In this thesis, the existing method of acquiring a single value by using the segment information and initial disparity value was viewed in NP-hard problem to propose a new method. In order to verify the validity of the proposed method, well-known data were used for experiment and the resulted data was analyzed. Although there were some disadvantages in the time aspect, it showed somewhat useful results in the accuracy aspect.

스테레오 대응 문제(Stereo Correspondent Problem)를 해결하기 위해 다양한 방법들이 시도되고 있지만 정확도의 편차가 심하다. 이 중 입력영상의 세그먼테이션 정보를 이용하여 접근하는 방법은 인간의 인식과 유사하여 많은 연구가 진행되고 있다. 세그먼트 정보와 초기 시차(disparity)값을 이용하여 단일한 해만을 구하는 기존 방법을 본 논문에서는 NP-hard 문제로 시각을 전환하여 해결하는 새로운 방법으로 제안하였다. 제안한 방법의 유용성 검증을 위해 잘 알려진 실험 데이터로 실험하고 그에 따른 결과를 분석하였다. 기존 방식에 비해 제안된 방법은 시간에서는 불이익이 있지만 정확도에서는 어느 정도 유용한 결과를 보여주었다.

Keywords

References

  1. D. Scharstein, R. Szeliski, "A taxonomy and evaluation of dense two-frame stereo correspondence algorithms," IJCV, Vol.47, No.1-3, pp.7-42, Apr., 2002. https://doi.org/10.1023/A:1014573219977
  2. http://vision.middlebury.edu/stereo
  3. L. Shafarenko, M. Petrou, and J. Kittler, "Automatic watershed segmentation of randomly textured color images," IEEE Trans. on Image Processing, Vol. 6, No.11, pp.1530-44, 1997. https://doi.org/10.1109/83.641413
  4. Heng-Da Cheng, Ying Sun, "A hierarchical approach to color image segmentation using homogeneity, Image Processing," IEEE Transactions on, Vol.9, Issue.12, pp.2071-2082, 2000.
  5. Yun-Suk Kang, Yo-Sung H, "Foreground Segmentation and High-Resolution Depth Map Generation Using a Timeof- Flight Depth Camera," The Journal of Korea information and communications society, Vol.37C, No.09, 2012.
  6. Daolei Wang, Kah Bin Lim, "A New Segment-based Stereo Matching using Graph Cuts," Computer Science and Information Technology (ICCSIT), 3rd IEEE International Conference on, Vol.5, 2010.
  7. Z. Wang, Z. Zheng, "A region based stereo matching algorithm using cooperative optimization," CVPR, 2008.
  8. Arti Khaparde, Apurva Naik, Manini Deshpande, Sakshi Khar, Kshitija Pandhari, and Mayura Shewale, "Performance Analysis of Stereo Matching Using Segmentation Based Disparity Map," ICDT, 2013.
  9. Li Hong, Chen, George, "Segment-based stereo matching using graph cuts," CVRP, IEEE, Vol.1, 2004.
  10. A. Klaus, M. Sormann, and K. Karner, "Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure," ICPR, 2006.
  11. Uyeol Park, Sunghoon An, "A study on Optimization Model of Time-Cost Trade-off Analysis suing Particle Swarm Optimization," Journal of The Korea Institute of Building Construction, Vol.8, No.6, 2008.
  12. Youngho Lee, "Design of Multiplier-less 2D State Space Digital Filters Based on Particle Swam Optimization," The Korean Institute of Communications and Information Sciences, Vol.17, No.4, 2013.
  13. A. Khaparde, A Naik, M. Deshpande, S. Khar, K. Pandhari, and M. Shewale, "Performance Analysis of Stereo Matching Using Segmentation Based Disparity Map," ICDT 2013: The Eighth International Conference on Digital Telecommunications, 2013.
  14. R. Szeliski, R. Zabih, D. Scharsein, O.Veksler, V. Komogorov, A. Agarwala, M. Tappen, and C. rother, "A comparative Study of Endergy Minimzation Methods for Markov Random Fields whtih Smoothness-Based Priors," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.30, No.6, pp.1068-1080, Jun., 2008. https://doi.org/10.1109/TPAMI.2007.70844
  15. Xiaofei Huang, "Cooperative Optimization for Energy Minimization: A Case Study of Stereo Matching," [Internet] http://arxiv.org/abs/cs/0701057v1, 2007.
  16. D. Comaniciu, P. Meer, "Mean shift: a robust approach toward feature space analysis," PAM IEEE Transaction, Vol. 24, pp. 603-619, 2002. https://doi.org/10.1109/34.1000236
  17. H. Tao, H. S. Sawhney, and R. Kumar, "A Global Matching Framework for Stereo Computation," Proc. Int'l Conf. Computer Vision, 2001.
  18. Kah Bin Lim, Daolei Wang, "A New Segment-based Stereo Matching using Graph Cuts," Computer Science and Information Technology(ICCSIT), IEEE International Conference, Vol.5, pp.410-416, 2010.