Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
권호 표지

Object Analysis on Outdoor Environment Using Multiple Features for Autonomous Navigation Robot

자율주행 로봇을 위한 다중 특징을 이용하여 외부환경에서 물체 분석

  • 김대년 (건동대학교 신재생에너지공학과) ;
  • 조강현 (울산대학교 전기전자정보시스템공학부)
  • Received : 2009.10.27
  • Accepted : 2010.02.02
  • Published : 2010.05.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper describes a method to identify objects for autonomous navigation of an outdoor mobile robot. To identify objects, the robot recognizes the object from an image taken by moving robot on outdoor environment. As a beginning, this paper presents the candidates for a segment of region to building of artificial object, sky and trees of natural objects. Then we define their characteristics individually. In the process, we segment the regions of the objects included by preprocessing using multiple features. Multiple features are HSI, line segments, context information, hue co-occurrence matrix, principal components and vanishing point. An analysis of building identifies the geometrical properties of building facet such as wall region, windows and entrance. The building as intersection in vertical and horizontal line segment of vanishing point extracts the mesh. The wall region of building detect by merging the mesh of the neighbor parallelograms that have similar colors. The property estimates the number of story and rooms in the same floors by merging skewed parallelograms of the same color. We accomplish the result of image segmentation using multiple features and the geometrical properties analysis of object through experiments.

본 연구는 외부환경에서 자율주행 로봇을 위해 중요한 물체를 찾기 위한 방법을 설명한다. 외부환경의 물체를 찾기 위해서 먼저 로봇은 외부환경에서 주행할 때 획득한 영상으로부터 물체를 검출하고 분할한다. 로봇은 물체의 후보를 자연물의 하늘과 나무로, 인공물의 빌딩으로 나눈다. 후보 물체를 분할하기 위해서 다중 특징을 이용한다. 다중 특징은 색상, 선분, 상황정보, 동시발생 행렬, 소실점 및 주요한 요소성분을 이용한다. 후보 특징은 물체의 특성에 맞게 혼합하여 물체를 분할한다. 이런 다중 특징은 물체에 대한 공간정보, 인간의 선험적인 지식을 이용한 물체의 기하학 정보, 공간적인 주파수 등으로 다양한 특징 추출 방법을 이용하여 물체의 영역분할의 결과를 얻는다. 물체의 분석은 분할된 영역을 이용하여 벽 영역, 창문, 정문과 같은 빌딩면의 기하학적인 속성을 찾는다. 빌딩은 소실점의 수직선분과 수평선분을 교차함으로써 그물을 얻는다. 빌딩의 벽 영역은 유사한 색상을 가지는 이웃해 있는 평행사변형의 그물을 합병해서 검출한다. 창문은 층의 수와 동일한 층에 있는 방의 수를 추정하여 빌딩의 높이와 크기를 추정한다. 실험에서 다중 특징을 이용하여 물체의 영역을 분할하고 빌딩의 기하학적인 속성을 이용하여 물체를 분석한다.

Keywords

Acknowledgement

Supported by : 한국산업기술진흥원

References

  1. M. De Martinao, F. Causa, and S. B. Serpico, "Classification of Optical High Resolution Images in Urban Environment Using Spectral and Textural Information," Proc. of Int'l Conf. on Geoscience and Remote Sensing Symposium, Vol.1, pp. 467-469, 2003.
  2. A. Bosch, X. Munoz, and J. Freixenet, "Segmentation and Description of atural Outdoor cenes," J. of Image and Vision Computing, Vol.25, pp.727-740, 2007. https://doi.org/10.1016/j.imavis.2006.05.015
  3. C. Zhang and P. Wang, "A ew Method of Color image Segmentation Based on Intensity and Hue Clustering ," Proc. of Int'l Conf. on Pattern Recognition, Vol.3, pp. 613-616, 2000.
  4. P. H. S. Torr and A. Zizzerman, "MLESAC: A new robust estimator with application to estimating image geometry," J. of Computer Vision and Image Understanding, Vol.78, pp.138-156, 2000. https://doi.org/10.1006/cviu.1999.0832
  5. Q. Ye, W. Gao, and W. Zeng, "Color Image Segmentation Using Density-based Clustering," Int'l Conf. on Acoustics, Speech and Signal Processing, Vol.3, pp. 345-348, 2003.
  6. M. Lievin and F. Luthon, "Nonlinear color space and spatio temporal MRF for hierarchical segmentation of face features in video," IEEE Trans. on Image Processing, Vol.13, pp. 63-71, 2004. https://doi.org/10.1109/TIP.2003.818013
  7. D. Mohr and G. Zachmann, "Segmentation of Distinct Homogeneous Color Regions in Images," Proc. of Int'l Conf. on Computer Analysis of Images and Patterns, LNCS 4673, pp. 432-440, 2007.
  8. J. G. Lim, J. H. Park, and G. G. Medioni, "Text segmentation in color images using tensor voting," J. of Image and Vision Computing, Vol.25, pp. 671-685, 2007. https://doi.org/10.1016/j.imavis.2006.05.011
  9. R. M. Haralick, K. Shanmugam, and I. Dinstein, "Texture Features for Image Classification," IEEE Trans. Syst. ManCybern, Vol. SMC-3, No.6, pp. 610-621, 1973. https://doi.org/10.1109/TSMC.1973.4309314
  10. A. Baraldi and F. Parmiggiani, "An investigation of the Textural Characteristics Associated with Gray Level Co-occurrence Matrix Statistical Parameters," IEEE Trans. on Geosciences and Remote Sensing, pp. 293-304, 1995.
  11. L. K. Soh and C. Tsatsoulis, "Texture Analysis of SAR Sea Ice Imagery Using Gray Level Co-occurrence Matrices," IEEE Trans. Geo-science and Remote Sensing, Vol.37, pp.780-795, 1999. https://doi.org/10.1109/36.752194
  12. J. Li, J. Z. Wang, and G. Wiederhold, "Classification of Textured and on-textured Images Using Region Segmentation." Proc. of Int'l Conf. on Image Processing, pp.754-757, 2000.
  13. J. Chen and T. N. Pappa, A. Mojsilovic and B. Rogowitz, "Adaptive Image Segmentation Based on Color and Texture," Proc. of Int'l Conf. on image Processing, pp. 777-780, 2002.
  14. M. Partio, B. Cramariuc, M. Gabbouj, and A. Visa, "Rock Texture Retrieval Using Gray Level Co-occurrence Matrix," Proc. of 5th. Nordic Signal Processing Symposium, 2002.
  15. S. Muhammad, M. Wachowicz, and L. M. T. de Carvalho, "Evaluation of Wavelet Transfonn Algorithms for Multi-resolution image Fusion," Proc. of Int'l Conf. on lnformation Fusion, Vol. 2, pp. 1573-1580, 2002.
  16. L. Lepisto, I. Kunttu, J. Autio, and A. Visa, "Rock Image Classification Using Non-homogenous Textures and Spectral Imaging," WSCG' 2003, pp. 82-86, 2003.
  17. Y. Qian and W. Si, "A semi-supervised color image segmentation method," IEEE Int'l Conf. on Image Processing, Vol.2, pp. 1194-1197, 2005.
  18. D.-N. Kim, H.-H. Trinh, and K.-H. Jo, "Objects Segmentation Using Multiple Features for Robot Navigation on Outdoor Environment," Int'l J. of Information Acquisition, Vol.6, No.2, pp. 99-108, 2009. https://doi.org/10.1142/S0219878909001862