Local motion estimation based on cellular neural network technology for image stabilization processing

This paper presents a novel robust image stabilization (IS) technique to find out local motion vectors in the image sequences captured. Our technique is based on a cellular neural network (CNN) algorithm, which tracks a small set of features to estimate the motion of the camera. Real-time and parallel analog computing elements are contained in the architecture of CNN. It is a regular two-dimensional array and connects with its neighborhood locally. To implement this algorithm on VLSI CNN, the adaptive-minimized threshold method is proposed to find quickly extract reliable motion vectors in plain images which are lack of features or contain large low-contrast area. Each size of CNN is set to 1/120 of an image. A background evaluation model is also developed to deal with irregular images which contain large moving objects. The experimental results are on-line available to demonstrate the remarkable performance of the proposed CNN-based motion technique.