Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Performance Improvement of Stereo Matching by Image Segmentation based on Color and Multi-threshold

컬러와 다중 임계값 기반 영상 분할 기법을 통한 스테레오 매칭의 성능 향상

  • Kim, Eun Kyeong (Department of Electrical and Computer Engineering, Pusan National University) ;
  • Cho, Hyunhak (Department of Interdisciplinary Cooperative Course: Robot, Pusan National University) ;
  • Jang, Eunseok (Department of Electrical and Computer Engineering, Pusan National University) ;
  • Kim, Sungshin (School of Electrical and Computer Engineering, Pusan National University)
  • 김은경 (부산대학교 전자전기컴퓨터공학과) ;
  • 조현학 (부산대학교 로봇관련협동과정) ;
  • 장은석 (부산대학교 전자전기컴퓨터공학과) ;
  • 김성신 (부산대학교 전기컴퓨터공학부)
  • Received : 2015.11.13
  • Accepted : 2015.12.23
  • Published : 2016.02.25
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Abstract

This paper proposed the method to improve performance of a pixel, which has low accuracy, by applying image segmentation methods based on color and multi-threshold of brightness. Stereo matching is the process to find the corresponding point on the right image with the point on the left image. For this process, distance(depth) information in stereo images is calculated. However, in the case of a region which has textureless, stereo matching has low accuracy and bad pixels occur on the disparity map. In the proposed method, the relationship between adjacent pixels is considered for compensating bad pixels. Generally, the object has similar color and brightness. Therefore, by considering the relationship between regions based on segmented regions by means of color and multi-threshold of brightness respectively, the region which is considered as parts of same object is re-segmented. According to relationship information of segmented sets of pixels, bad pixels in the disparity map are compensated efficiently. By applying the proposed method, the results show a decrease of nearly 28% in the number of bad pixels of the image applied the method which is established.

본 논문에서는 스테레오 매칭 시 발생하는 신뢰도가 낮은 부분을 컬러와 명도의 다중 임계값에 기반한 영상 분할 기법을 통해 보정하는 방법을 제안한다. 스테레오 매칭은 좌측 영상 위의 한 점과 대응하는 점을 우측 영상에서 찾는 과정이며, 이를 통해 스테레오 영상 내에서 거리 정보를 복원할 수 있다. 하지만 영상 내 특징이 불분명한 부분의 경우, 스테레오 매칭의 신뢰도가 낮기 때문에 Bad Pixel이 발생하게 된다. 제안하는 방법에서는 Bad Pixel을 보정하기 위해서 각 픽셀의 연관성을 고려하고자 한다. 일반적으로 동일한 물체는 비슷한 색상과 명도를 가진다. 따라서 컬러와 명도의 다중 임계값에 의해 각각 분할된 영역을 통해 영역 간의 연관성을 고려하여, 동일한 물체로 판단되는 부분을 재분할한다. 이후 분할된 픽셀들의 관계 정보에 따라 디스패리티 맵의 Bad Pixel을 보정하였다. 실험 결과, 제안하는 방법을 통해 기존 방법의 결과에서 Bad Pixel이 28% 감소함을 확인하였다.

Keywords

References

  1. G. N. DeSouza, and A. C. Kak, "Vision for mobile robot navigation: A survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 2, pp. 237-267, 2002. https://doi.org/10.1109/34.982903
  2. C. Hane, et al., "Stereo depth map fusion for robot navigation," IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1618-1625, 2011.
  3. H. H. Min, D. S. Yoo, and Y. T. Kim, "Fuzzy Tracking Control Based on Stereo Images for Tracking of Moving Robot," Journal of Korean Institute of Intelligent Systems, vol. 22, no. 2, pp. 198-204, 2012. https://doi.org/10.5391/JKIIS.2012.22.2.198
  4. D. Scharstein, and R. Szeliski, "A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms," International Journal of Computer Vision, vol. 47, no. 7, pp. 7-42, 2002. https://doi.org/10.1023/A:1014573219977
  5. F. Tombari, et al., "Classification and evaluation of cost aggregation methods for stereo correspondence," IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008
  6. X. Sun, et al., "Stereo matching with reliable disparity propagation," International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, pp. 132-139, 2011.
  7. I. Oh, Computer Vision, Hanbit Academy Inc., 2014.
  8. T. Sag, and M. Cunkas, "Color image segmentation based on multiobjective artificial bee colony optimization," Applied Soft Computing, vol. 34, pp. 389-401, 2015 https://doi.org/10.1016/j.asoc.2015.05.016
  9. W. Skarbek, et al., "Colour image segmentation-a survey," 1994.
  10. B. I. Choi, and C. H. Rhee, "Fuzzy Kernel K-Nearest Neighbor Algorithm for Image Segmentation," Journal of Korean Institute of Intelligent Systems, vol. 15, no. 7, pp. 828-833, 2005. https://doi.org/10.5391/JKIIS.2005.15.7.828
  11. P. Corke, Robotics, Vision and Control, Springer, 2011.
  12. H. Li, H. He, and Y. Wen, "Dynamic particle swarm optimization and K-means clustering algorithm for image segmentation," Optik-International Journal for Light and Electron Optics, pp. 4817-4822, 2015.
  13. D. Scharstein, and R. Szeliski, "Middlebury Stereo Vision Page", Available: http://vision.middlebury.edu/stereo/, 2002, [Accessed: November 03, 2015]
  14. Y. H. Cho, "Shape Image Recognition by Using Histogram-based Correlation," Journal of Korean Institute of Intelligent Systems, vol. 20, no. 4, pp. 548-553, 2010. https://doi.org/10.5391/JKIIS.2010.20.4.548
  15. N. Duan, et al., "Multi-thresholds Selection Based on Plane Curves," Journal of Korean Institute of Intelligent Systems, vol. 20, no. 2, pp. 279-284, 2010. https://doi.org/10.5391/JKIIS.2010.20.2.279
  16. S. B. Roh, et al., "Space Partition using Context Fuzzy c-Means Algorithm for Image Segmentation," Journal of Korean Institute of Intelligent Systems, vol. 20, no. 3, pp. 368-374, 2010. https://doi.org/10.5391/JKIIS.2010.20.3.368