A complex network-based approach for boundary shape analysis
In practice of image contour recognition, the precision of shape contour extraction is affected by lots of factors, such as noise, shelter and parameters. That will affect the shape contour quality and reduce the recognition effect. To solve these problems, a Shape Contour Recognition Method Based on Complex Network is discussed in this study. The main idea of the approach is to use complex network methodology to extract a feature vector for shape contour recognition under rotation, noise and shelter. An approximation method for Distance Threshold Determining (DTD) is presented to help modeling the complex networks. Experiments show that the proposed method and the DTD method have efficient power in shape recognition. It is also proved to be scale invariant, rotation invariant and partially overcome noise-sensitive and shelter.