Abstract
Devising a pattern recognition methodology for traffic sign images captured in noisy outdoor environments poses a challenging problem. Such a method has to cope with occlusion noise as when part of the region of interest (in this case, the traffic sign) is blocked by an interfering object, with centroid noise as when an interfering object is present in the vicinity of the region of interest, etc. In this research, a noise-tolerant method is designed such that the recognition rate will not be adversely affected by noisy conditions that can be expected in realistic environments. The performance of various color coordinate systems (RGB, ISH, XYZ, YIQ, uvY) is evaluated via several separability measures in order to obtain the color coordinate system giving the best separation between traffic signs and other objects in the scene. Color segmentation is performed by using a self-organizing neural network, which generates a segmented binary image of the traffic sign. A signature of the traffic sign is extracted that is invariant to changes in scale, location, and orientation based on the log polar exponential grid and Fourier transformations. Finally, a backpropagation neural network trained by using signatures of various traffic sign images is used to classify the traffic signs.
Ahmad, Akram (1995). A noise-tolerant traffic sign recognition method based on color images. Master's thesis, Texas A&M University. Available electronically from
https : / /hdl .handle .net /1969 .1 /ETD -TAMU -1995 -THESIS -A364.