Low-complexity fusion of intensity, motion, texture, and edge for image sequence segmentation: a neural network approach

We develop an image sequence segmentation scheme which uses intensity, motion, edge, and texture features. The proposed scheme is simple and inherently parallel in nature. Motion confidence values are employed for a feature weighting scheme in order to suppress unreliable feature components. These feature vectors are quantized by training self-organizing feature maps (SOFM). In order to generate more meaningful boundaries of the segmentation, we also develop an edge fusion algorithm in which an edge-linked map extracted from a real-time edge linking algorithm is incorporated for the segmentation. Experimental results show the validity of our approach.