The KIPS Transactions:PartB (정보처리학회논문지B)

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Segmentation and Recognition of Traffic Signs using Shape Information and Edge Image in Real Image

실영상에서 형태 정보와 에지 영상을 이용한 교통 표지판 영역 추출과 인식

  • 곽현욱 (영남대학교 대학원 컴퓨터공학과) ;
  • 오준택 (영남대학교 대학원 컴퓨터공학과) ;
  • 김욱현 (영남대학교 전자정보공학부)
  • Published : 2004.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

This study proposes a method for segmentation and recognition of traffic signs using shape information and edge image in real image. It first segments traffic sign candidate regions by connected component algorithm from binary images, obtained by utilizing the RGB color ratio of each pixel in the image, and then extracts actual traffic signs based on their symmetries on X- and Y-axes. Histogram equalization is performed for unsegmented candidate regions caused by low contrast in the image. In the recognition stage, it utilizes shape information including projection profiles on X- and Y-axes, moment, and the number of crossings and distance which concentric circular patterns and 8-directional rays from region center intersects with edges of traffic signs. It finally performs recognition by measuring similarity with the templates in the database. It will be shown from several experimental results that the system is robust to environmental factors, such as light and weather condition.

본 논문은 실영상에서 형태 정보와 에지 영상을 이용한 교통 표지판 영역 추출 및 인식 방법을 제안한다. 화소의 RGB 색상비를 이용하여 생성한 이진 영상에서 connected component 알고리듬에 의해 분할된 후보 영역들을 대상으로 형태 정보인 XY축 대칭성을 기반으로 교통 표지판 영역을 추출한다. 만약 후보 영역이 검출되지 않을 경우, 히스토그램 평활화에 의해 대비를 향상함으로써 영역 추출이 가능하다. 그리고 교통 표지판 영역의 에지 영상에서 추출한 수평-수직 투영(XY projection). 모멘트(moment), 동심원형 패턴 및 8 방향 광선과 에지와의 거리 및 교차점의 개수 등의 형태 정보를 기반으로, 사전에 구축한 데이터베이스와의 유사도 측정에 의해 인식을 수행한다. 다양한 실영상을 대상으로 실험한 결과, 본 방법이 빛이나 날씨 조건 등의 외부 환경에 강건하게 추출 및 인식함을 보인다.

Keywords

References

  1. Randy Crane, A Simplified Approach to Image Processing, Prentice Hall PTR Prentice-Hall Inc., 1997
  2. R .C. Gonzalez, R. E. Woods, Digital Image Processing, Addison Wesley, 1998
  3. Kenneth R. Castleman, Digital Image Processing, Prentice-Hall Inc. A Simon and Schuster Company, 1996
  4. G. Foresti, V. Murino, C. S. Regazzoni and G. Vernazza, 'A Distributed Approach to 3D Road Scene Recognition,' IEEE Trans, on Vehicular Technology, Vol.43, No.2, pp.389-406, May, 1994 https://doi.org/10.1109/25.293657
  5. M. Gay, 'Segmentation Using Region Merging With Edges,' In Proc. Alvey Vision Conf., pp.115-119, Sept., 1989
  6. H. Akatsuka and S. Imai, 'Road signposts recognition system,' In Proc. SAE vehicle highway infrastructure : safety compatibility, pp.189-196, 1987
  7. R. Ghica, S. W. Lu and X. Yuan, 'Recognition of traffic signs usin a multilayer neural network,' InProc, Canadian Conf, on Electrical and Computer Engineering, 1994
  8. N. Kehtarmavaz, N. C. Griswold and D. S. Kang, 'Stopsign recognition based on color-shape processing,' Machine Vision and Applications, Vol.6, pp.206-208, 1993 https://doi.org/10.1007/BF01212298
  9. L. Priese and V. Rehrmann, 'On hierarchical color segmentation and applications,' InProc. CVPR, pp.633-634, 1993 https://doi.org/10.1109/CVPR.1993.341052
  10. G. Nicchiotti, E. Ottaviani, P. Castello and G. Piccioli, 'Automatic road sign detection and classification from color image sequences,' In S. Impedove, editor, Proc. 7th Int. Conference on Image Analysis and Processing, World Scientific, pp.623-626, 1994
  11. S. Estable, J. Schick, F. Stein, R. Janssen, R. Ott, W. Ritter and Y. J. Zheng, 'A real-time traffic sign recognition system,' In Proc, Intelligent Vehicles, pp.213-218, 1994 https://doi.org/10.1109/IVS.1994.639506
  12. Y. Aoyagi and T. Asakura, 'A study on traffic sign recognition in scene image using genetic algorithm and neural networks,' In Proc. IEEE Conf, on Industrial Electronics, Control and Instrumentation, Taipei, Taiwan, pp.1838-1843, 1996 https://doi.org/10.1109/IECON.1996.570749
  13. J. H. An and T. Y. Choi, 'Dominant Color Transform and Circulr Pattern vector for Traffic Sign Detection and Recognition,' IEICE Trans, on Fundamentals of Electronics Communications and Computer Science, Vol.E81, No.6, pp.1128-1135, 1998
  14. D. G. Shaposhnikov, L. N. Podladchikova, A. V. Golovan and N. A. Shevtsova, 'Road Sign Recognition by Single Positioning of Space-Variant Sensor Window,' In Proc. Vision Interface, pp.213-217, 2002

Cited by

  1. Traffic Light and Speed Sign Recognition by using Hierarchical Application of Color Segmentation and Object Feature Information vol.17B, pp.3, 2010, https://doi.org/10.3745/KIPSTB.2010.17B.3.207