Abstract
Contour extraction is a fundamental problem in computer vision, and widely applied to visual projects. However, for some scenes with complex background or objects to be extracted are similar to background, traditional methods seem powerless. In this paper, we present a new approach to tackle this problem. Given an image sequence, the approach is decomposed into two main phases: background modeling, contour extracting. In the first phase, Gaussian mixture distribution is applied to build the background model in dynamic environment. In the second phase, we map the segmented foreground pixels to a two-dimensional point set, through which we construct a geometric graph based on Euclidean distance, and then all border nodes are connected to generate the contour using the orientation information. Actual project results show that the approach is capable of generating contours with high accuracy and strong robustness.
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