The first step is to obtain the images of a runway on a clear day and compute the smoothness coefficient followed by edge detection, using the SUSAN edge detection algorithm and then finally develop a database of the smoothness coefficients and edge detected images. Now, for the foggy images we compute the smoothness coefficient. Typically, foggy images have low contrast. Hence, before we perform edge detection, we enhance the image using Multi-Scale Retinex ({\sc msr}). {\sc msr} provides the low contrast enhancement and color constancy, required to enhance foggy images, by performing non-linear spatial/spectral transforms. After enhancement, the next step is to run the same edge detection algorithm with appropriate thresholds. Finally we determine a hazard by comparing the edge detected images of images taken under clear and foggy conditions. The paper also compares the results of the SUSAN edge detection algorithm with the state of art edge detection techniques.
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