Segmentation and Free Space Detection Using Gabor Filters

  • Tadayoshi Shioyama
  • Haiyuan Wu
  • Masaya Takebe
  • Naoya Shimaoka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)

Abstract

This paper proposes a new segmentation method and applies it for detecting free space, which is defined as a free road area without any obstacle. The problem is important for an intelligent transportation control or collision avoidance in vehicles, and also important for assisting the blind pedestrian. In this paper, the texture analysis is carried out using Gabor filters. Input images are segmented with not only the outputs of Gabor filters, but also both edge and color information. A free space is extracted from the segmented image. Then, the three-dimensional (3-D) information such as a free path at arbitrary direction from the observer is computed by stereo method. In order to evaluate the effectiveness of this proposed method, experimental results are shown for real images consisting of outdoor and indoor scenes.

Keywords

Free Space Free Path Road Surface Gabor Filter Stereo Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. [1]
    Onoguchi, K., Takeda, N. and Watanabe, M., “Obstacle Location Estimation Using Planar Projection Stereopsis Method,” IEICE Trans., Vol.J81-D-II, No.8 (1998) pp. 1895–1903.Google Scholar
  2. [2]
    Adiv, G.,“Determining Three-Demensional Motion and Structure from Optical Flow Generated by Several Moving Objects,” IEEE Trans. on PAMI, Vol.7, No.4 (1985) pp. 384–401.Google Scholar
  3. [3]
    Thompson, W.B., Mutch, K.M. and Berzins, V.A., “Dynamic Occlusion Analysis in Optical Flow Fields,” IEEE Trans. on PAMI, Vol.7, No.4 (1985) pp. 374–383.Google Scholar
  4. [4]
    Murray, D.W. and Buxton, B.F., “Scene Segmentation from Visual Motion Using Global Optimization,” IEEE Trans. on PAMI, Vol.9, No.2 (1987) pp. 220–228.Google Scholar
  5. [5]
    Thompson, W.B., Lechleider, P. and Stuck, E.R., “Detecting Moving Objects Using the Rigidity Constraint,” IEEE Trans. on PAMI, Vol.15, No.2 (1993) pp. 162–166.Google Scholar
  6. [6]
    Ohta, N., “Structure from Motion with Confidence Measure and Its Application for Moving Object Detection,” IEICE Trans., Vol. J76-D-II, No.8 (1993) pp. 1562–1571.Google Scholar
  7. [7]
    Smith, S.M., “ASSET-2:Real-Time Motion Segmentation and Shape Tracking,” Proc. of ICCV’95 (1995) pp. 237–244.Google Scholar
  8. [8]
    Tian, T.W. and Shah, M., “Recovering 3D Motion of Multiple Objects Using Adaptive Hough Transform,” Proc. of ICCV’95 (1995) pp. 284–289.Google Scholar
  9. [9]
    Adorni, G. and Cagnoni, S., “A cellular automata-based tool as an aid for autonomous navigation,” Proc. of 11th Scandinavian Conf. on Image Analysis, (1999) pp. 609–614.Google Scholar
  10. [10]
    Jain, A.K., Ratha, N.K. and Lakshmanan, S., “Object Detection Using Gabor Filters,” Pattern Recognition, Vol.30, No.2 (1997) pp. 295–309.CrossRefGoogle Scholar
  11. [11]
    Mitani, S., Wu, H. and Shioyama, T., “Car Detection with Gabor Filters,” IEICE Trans., Vol.J83-D-II, No.12 (2000) pp. 2641–2651.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Tadayoshi Shioyama
    • 1
  • Haiyuan Wu
    • 2
  • Masaya Takebe
    • 1
  • Naoya Shimaoka
    • 1
  1. 1.Department of Mechanical and System EngineeringKyoto Institute of TechnologyKyotoJapan
  2. 2.Department of Computer and Commucation SciencesWakayama UniversityWakayama

Personalised recommendations