Contour detection based on self-organizing feature clustering

The real vision system has a well-developed ability to detect multiple contours and recognize various objects in images. Previous simulation models to perform this process often employ image segmentation or contour integration. algorithms. In this paper a new model is proposed to separate individual object contours from the background by the feature clustering. The model is inspired by the contrast mechanism and the self-organizing characteristic of the vision system. It can group edge elements with similar local features together automatically. The Self-Organizing Map (SOM) is used in the model to classify the edge elements in the-image. Experimental results show that the object contours can be separated effectively by this model. The model can be used to supply useful information to higher-level visual mechanism for better object recognition.