Authors : Yaming Wang , Weida Zhou and Xiongjie Wang
Abstract: This study proposes a novel approach based on Cellular Neural Networks (CNN) is proposed for segmenting moving objects from monocular image sequence regardless of complex, changing background. First, a Gaussian distribution model for image pixel is proposed. The parameters contained in the model are adaptively updated based on the information from the current and historical frames. Based on this, every image frame is mapped from spatial domain to statistical domain. Then, a CNN framework is proposed for segmenting moving objects in statistical domain. The desirable feature of CNNs is that the processors arranged in the two dimensional grid only have local connections, which lend themselves easily to VLSI implementations. By modeling pixel interactions through using a spatial-temporal neighborhood of the CNN, sparse nosy pixel can be erased and robust segmenting results of moving objects can be achieved. Experimental results from two real monocular image sequences demonstrate the feasibility of the proposed approach.
Yaming Wang , Weida Zhou and Xiongjie Wang , 2005. Cellular Neural Networks for Object Segmentation of Image Sequence . Asian Journal of Information Technology, 4: 1098-1101.