한국게임학회 논문지 (Journal of Korea Game Society)

  • 제17권6호
  • /
  • Pages.139-152
  • /
  • 2017
  • /
  • 1598-4540(pISSN)
  • /
  • 2287-8211(eISSN)
한국게임학회 (Korea Game Society)
권호 표지
DOI QR코드

DOI QR Code

영역 분할에 기반한 구면 영상에서의 바닥 검출 기법

A Ground Detection Technique based on Region Segmentation in Spherical Image

  • 김종윤 (인천대학교 컴퓨터공학부) ;
  • 박종승 (인천대학교 컴퓨터공학부)
  • Kim, Jong-Yoon (Dept. of Computer Science & Engineering, Incheon National University) ;
  • Park, Jong-Seung (Dept. of Computer Science & Engineering, Incheon National University)
  • 투고 : 2017.11.11
  • 심사 : 2017.12.15
  • 발행 : 2017.12.20
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 구면 영상에서 영역 분할 정보를 사용하여 바닥 영역을 검출하는 방법을 제시한다. 평면 영상에서의 Watershed 영역 분할 방법을 수정하여 구면 영상의 영역 분할에 적용할 수 있도록 하였다. 영역들을 분할한 뒤 가정된 바닥 영역 픽셀의 색상과 질감을 그 외의 영역들과 비교하여 바닥 영역을 검출한다. 구면 파노라마 영상에서는 구면 왜곡으로 인하여 평면에서의 바닥 검출 방법을 그대로 적용할 수 없다. 구면 왜곡을 고려한 바닥 영역 검출을 위하여 바닥 영역의 외곽선을 검출하는 알고리즘을 설계하였다. 실험에서 지상물이 없는 경우와 있는 경우의 모두에서 적절하게 바닥 영역을 검출할 수 있는 결과를 보였다.

In this paper, we propose a ground area detection technique based on region segmentation in the spherical image. We modified the Watershed planar image segmentation method to segment spherical images. After regions are segmented, the ground area is detected by comparing colors and textures of pixels of the assumed ground region with the pixels of other regions. The ground detection technique for planar images cannot be used for spherical images due to the spherical distortion. Considering the spherical distortion, we designed the ground shape detection algorithm to detect the ground area in the spherical images. Our experimental results show that the proposed technique properly detects ground areas both for the flat ground and the obstacle-filled ground environments.

키워드

참고문헌

  1. Cherian, A., Morellas, V., Papanikolopoulos, N., "Accurate 3D ground plane estimation from a single image", IEEE International Conference on Robotics and Automation, pp. 2243-2249, 2009.
  2. Lin, C. H., Jiang, S. Y., Pu, Y. J., Song, K. T., "Robust ground plane detection for obstacle avoidance of mobile robots using a monocular camera", IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3706-3711, 2010.
  3. Haines, O., Calway, A., "Detecting planes and estimating their orientation from a single image", British Machine Vision Conference, pp. 1-11, 2012.
  4. Rahimi, A., Moradi, H., Zoroofi, R. A., "Single image ground plane estimation", 20th IEEE International Conference on Image Processing, pp. 2149-2153. IEEE, 2013.
  5. Zhao, Y., Liu, J., Li, H., Li, G., "Improved watershed algorithm for dowels image segmentation", 7th World Congress on Intelligent Control and Automation, pp. 7644-7648, 2008.
  6. Yen, S. H., Tai, A. C., Wang, C. J., "Segmentation on color images based on watershed algorithm", 10th International Multimedia Modelling Conference, pp. 227-232, 2004.
  7. Belaid, L. J., Mourou, W., "Image segmentation: a watershed transformation algorithm", Image Analysis & Stereology, Vol. 28, No. 2, pp. 93-102, 2011. https://doi.org/10.5566/ias.v28.p93-102
  8. Tian, X., Yu, W., "Color image segmentation based on watershed transform and feature clustering", IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, pp. 1830-1833, 2016.
  9. Karvelis, P. S., Fotiadis, D. I., Georgiou, I., Syrrou, M., "A watershed based segmentation method for multispectral chromosome images classification", International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3009-3012, 2006.
  10. Sergyan, S., Csink, L., "Automatic parametrization of region finding algorithms in gray images", 4th International Symposium on Applied Computational Intelligence and Informatics, pp. 199-202, 2007.
  11. Yuan, L., Yu, Q., Shen, C., Hu, W., Yang, Z., "New watershed segmentation algorithm based on hybrid gradient and self-adaptive marker extraction", 2nd IEEE International Conference on Computer and Communications, pp. 624-628, 2016.
  12. Zhanpeng, H., Qi, Z., Shizhong, J., Guohua, C., "Medical Image Segmentation Based on the Watersheds and Regions Merging", 3rd International Conference on Information Science and Control Engineering, pp. 1011-1014, 2016.
  13. Emambakhsh, M., Sedaaghi, M. H., "Automatic MRI brain segmentation using local features, self-organizing maps, and watershed", IEEE International Conference on Signal and Image Processing Applications, pp. 123-128, 2009.
  14. Elyounsi, A., Tlijani, H., Bouhlel, M. S., "Combining top-hat, Thresholding and watershed transformation for 3D Inverse Synthetic Aperture Radar Images segmentation", International Conference on Information and Digital Technologies, pp. 466-471, 2017.
  15. Kim, B.S., Park, J.S., "Estimating Geometric Transformation of Planar Pattern in Spherical Panoramic Image", Journal of KIISE, Vol. 42, No. 10, 1185-1194, 2015. https://doi.org/10.5626/JOK.2015.42.10.1185
  16. Lim, K.T., Won C., "Face Image Analysis using Adaboost Learning and Non-Square Differential LBP", Journal of Korea Multimedia Society, Vol. 19, No. 6, pp. 1014-1023, 2016. https://doi.org/10.9717/kmms.2016.19.6.1014
  17. Nazari, A., Shouraki, S. B., "A constructive genetic algorithm for LBP in face recognition", 3rd International Conference on Pattern Recognition and Image Analysis, pp. 182-188, 2017.