A Graph based Geometric Approach to Contour Extraction from Noisy Binary Images

We present a method to extract the contour of geometric objects embedded in binary digital images using techniques in computational geometry. Rather than directly dealing with pixels as in traditional contour extraction methods, we process on object point set extracted from the image. The proposed algorithm works in four phases: point extraction, Euclidean graph construction, point linking and contour simplification. In point extraction phase, all pixels that represent the object pattern are extracted as a point set from the input image. We use the color segmentation to distinguish the object pixels from the background pixels. In the second phase, a geometric graph G=(V,E) is constructed, where V consists of the extracted object point set and E consists of all possible edges whose Euclidean distance is less than a threshold parameter, l ; which can be derived from the available information from the point set. In point linking phase, all border points are connected to generate the contour using the orientation information inferred from the clockwise turn angle at each border point. Finally, the extracted contour is simplified using collinearity check. Experiments on various standard binary images show that the algorithm is capable of constructing contours with high accuracy and achieves high compression ratio in noisy and non-noisy binary images.