DOI QR코드

DOI QR Code

질감 특징을 이용한 시각장애인용 보행유도 시스템

Walking assistance system using texture for visually impaired person

  • 투고 : 2011.04.23
  • 심사 : 2011.05.17
  • 발행 : 2011.09.30

초록

본 논문은 보행중인 시각장애인에 장착된 카메라로부터 획득한 영상에서 보도와 차도 영역을 구분하기 위한 영역분할 기법과 질감 특징추출 기법에 대해 제안한다. 허프 변환 알고리즘을 이용한 라인검출을 통해 도로 경계선을 검출하고, 분할된 영역을 원근에 따라 3단계의 레벨로 구분한다. 그리고 분할된 영역들의 질감 특징성분을 추출함으로써 보도와 차도영역으로 분리한다. 보도블록이 가지는 복잡하고 다양한 특성의 패턴과 차도의 균일한 질감을 가진 영역의 특성을 비교하기 위하여 회전에 강건한 LBP, GLCM 질감 특징성분들을 이용함으로써 두 영역을 구분하였다. 제안된 방법은 주간과 야간 영상에 대해 실험한 결과 조도의 변화에 강건하게 영역을 분리할 수 있었고, 또한 보행자와 장애물이 많은 영상에서도 회전이나 폐색에 관계없이 영역 분리가 가능함을 확인하였다.

In this paper, we propose an region segmentation and texture based feature extraction method which split the pavement and roadway from the camera which equipped to the visually impaired person during a walk. We perform the hough transformation method for detect the boundary between pavement and roadway, and devide the segmented region into 3-level according to perspective. Next step, split into pavement and roadway according to the extracted texture feature of segmented regions. Our walking assistance system use rotation-invariant LBP and GLCM texture features for compare the characteristic of pavement block with various pattern and uniformity roadway. Our proposed method show that can segment two regions with illumination invariant in day and night image, and split there regions rotation and occlution invariant in complexed outdoor image.

키워드

참고문헌

  1. Ki-son Lee, So-Hee Jeon, and Byung-Doo Kwon, "Implemen tation of GlCM/GLDV-based Texture Algorithm and Its Application to High Resolution Imagery Analysis ", Korean Journal of Remote Sensing, Vol. 21, No. 2, pp.121-133, 2005.
  2. Ojala, T., Pietikäinen, M., and Mäenpää, T., "Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns", IEEE Transactions on Pattern Analysis and Machine Intelligence 24, pp.971-987, 2002. https://doi.org/10.1109/TPAMI.2002.1017623
  3. Turtinen, M., and Pietikäinen, M., "Visual Training and Classification of Textured Scene Images", The 3rd International Workshop on Texture Analysis and Synthesis, pp.101-106, 2003.
  4. Arvis V., Debain C., Berducat M., and Benassi A., "Generali zation of the cooccurrence matrix for colour images: application to colour texture classification", Image Anal Stereol Vol. 23, pp.63-72, 2004. https://doi.org/10.5566/ias.v23.p63-72
  5. Vadivel A., Shamik Sural, andMajumdar A.K., "An Integrated Color and Intensity Co-occurrence Matrix", Pattern Recognition Letters, Vol. 28, No. 8, pp.974-983, 2007. https://doi.org/10.1016/j.patrec.2007.01.004
  6. Palm C., "Color texture classification by integrative co-occ rrence matrices", Pattern Recognition, Vol. 37, pp.965-976, 2004. https://doi.org/10.1016/j.patcog.2003.09.010
  7. Romuald A., Roland C., and Frederic C., "A model-driven approach for real-time road recognition", Machine Vision and Applications, Vol. 13, No. 2, pp.95-107, 2001. https://doi.org/10.1007/PL00013275
  8. Wang Y., Teoh E.K., and Shen D., "Lane detection and tracking using B-Snake", Image and Vision Computing Vol. 22, No. 4, pp.269-280, 2004. https://doi.org/10.1016/j.imavis.2003.10.003
  9. Wang Y., Shen D., and Teoh E.K., "Lane detection using spline model", Pattern Recognition Letters Vol. 21, No. 8, pp.677-689, 2000. https://doi.org/10.1016/S0167-8655(00)00021-0
  10. Paetzold F., and Franke U., "Road recognition in urban environment", Image and Vision Computing, Vol. 18, No. 5, pp.377-387, 2000. https://doi.org/10.1016/S0262-8856(99)00033-5
  11. Hanif S.M., and Prevost L., "Texture based text detection in natural scene images : a help to blind and visually impaired persons", CVHI 2007.
  12. Puig D., and Angel Garcia M., "Automatic texture feature selection for image pixel classification", Pattern Recognition Vol. 39, No. 11, pp.1996-2009, 2006. https://doi.org/10.1016/j.patcog.2006.05.016
  13. Seong-Whan Lee and Seong-Hoon Kang, "OpenEyes: Wea rable Computer for the Blind", Journal of KIISE, Vol. 18, No. 9, pp.31-36, Sept. 2000.
  14. Young-Soo Chae, Hyun-Cheol Kim, and Whoi-Yul Kim, "Multiple Background Modeling using Local Binary Pattern", Conference of The Institute of Electronics Engineers of Korea, Vol. 31권, No. 1, pp.1001-1002, 2008.