Strategies for detection of distorted road signs in background noise

Design of an on-board processor that enables recognition of a given road sign affected by different distortions is presented. The road sign recognition system is based on a nonlinear processor. Analysis of different filtering methods allows us to select the best techniques to overcome a variety of distortions. The proposed recognition system has been tested in real still images as well as in video sequences. Scenes were captured in real environments, with cluttered backgrounds and contain many distortions simultaneously. Recognition results for various images show that the processor is able to properly detect a given road sign even if it is varying in scale, slightly tilted or viewed under different angles. Recognition is also achieved when dealing with partially occluded road signs. In addition, the system is robust to illumination fluctuations.

[1]  J García,et al.  Extended scale-invariant pattern recognition with white-light illumination. , 2000, Applied optics.

[2]  Bahram Javidi,et al.  Distortion-invariant pattern recognition with Fourier-plane nonlinear filters. , 1996, Applied optics.

[3]  Bertrand Noharet,et al.  Comparison of the performance of correlation filters on images with real-world nonoverlapping noise: influence of the target size, illumination, and in-plane rotation distortion , 1998, Defense + Commercial Sensing.

[4]  D Casasent,et al.  Multivariant technique for multiclass pattern recognition. , 1980, Applied optics.

[5]  Katsunori Matsuoka,et al.  Detection of road signs by using a multiple correlator and partial invariant filters , 1998, Other Conferences.

[6]  B Javidi,et al.  Nonlinear joint-transform correlation: an optimal solution for adaptive image discrimination and input noise robustness. , 1994, Optics letters.

[7]  Ezra Silvera,et al.  Adaptive pattern recognition with rotation, scale, and shift invariance. , 1995, Applied optics.

[8]  J L Horner,et al.  Metrics for assessing pattern-recognition performance. , 1992, Applied optics.

[9]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[10]  H J Caulfield,et al.  Improved discrimination in optical character recognition. , 1969, Applied optics.

[11]  H J Caulfield.  Linear combination of filters for character recognition: a unified treatment. , 1980, Applied optics.

[12]  Bahram Javidi,et al.  Image Processing for Intelligent Transportation Systems: Application to Road Sign Recognition , 2001 .

[13]  Bahram Javidi,et al.  Composite Fourier-plane nonlinear filter for distortion-invariant pattern recognition , 1997 .

[14]  Katsunori Matsuoka,et al.  Correlation filter design for classification of road sign by multiple optical correlators , 1999, Optics + Photonics.

[15]  Jean-Louis de Bougrenet de la Tocnaye,et al.  On-board optical joint transform correlator for real-time road sign recognition , 1995 .

[16]  李幼升,et al.  Ph , 1989 .

[17]  Bahram Javidi,et al.  Optimum distortion-invariant filter for detecting a noisy distorted target in nonoverlapping background noise , 1995 .

[18]  Bahram Javidi,et al.  Illumination-invariant pattern recognition with a binary nonlinear joint transform correlator using spatial frequency dependent threshold function , 1993, Optics & Photonics.

[19]  B. Javidi.  Nonlinear joint power spectrum based optical correlation. , 1989, Applied optics.

[20]  D Casasent,et al.  Unified synthetic discriminant function computational formulation. , 1984, Applied optics.

[21]  Y. Pétillot,et al.  Scale-invariant optical correlators using ferroelectric liquid-crystal spatial light modulators. , 1995, Applied optics.

[22]  Bahram Javidi,et al.  Minimum mean square error filter for pattern recognition with spatially disjoint signal and scene noise , 1993, Optics & Photonics.

[23]  D. Casasent,et al.  Minimum average correlation energy filters. , 1987, Applied optics.