AbstractÐContour representations of binary images of handwritten words afford considerable reduction in storage requirements while providing lossless representation. On the other hand, the one-dimensional nature of contours presents interesting challenges for processing images for handwritten word recognition. Our experiments indicate that significant gains are to be realized in both speed and recognition accuracy by using a contour representation in handwriting applications.

A fog level estimation method using intensity curves arranged with geometrical information is proposed. The curves, extracted from pixels in the road region and reflecting the tendency of intensity convergence under foggy conditions, are provided to the stacked autoencoders and the encoded features are classified into four fog levels by a neural network. Experimental results with clear and foggy road images of various places show that the intensity curve feature of the proposed method is effectively working not only in detecting the presence of fog, but also in estimating the fog level accurately.

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