TY  - JOUR
T1  - Cellular Neural Networks for Object Segmentation of Image Sequence
AU - , Yaming Wang AU - , Weida Zhou AU - , Xiongjie Wang 
JO  - Asian Journal of Information Technology
VL  - 4
IS  - 11
SP  - 1098
EP  - 1101
PY  - 2005
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2005.1098.1101
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2005.1098.1101
KW  - Cellular neural networks
KW  -object segmentation
KW  -monocular image sequence
KW  -statistical domain
AB  - 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.
ER  -