基于像素统计特性及细胞神经网络的运动目标分割

针对动态图像序列中背景成像过程因各种因素而变化存在复杂性，提出了一种基于像素统计特性及细胞神经网络（CNN）的目标分割方法。首先建立图像每一像素点的高斯分布模型，并根据图像序列中的当前帧及历史帧信息自适应地调整模型的参数。然后结合图像的帧间信息将图像从空间域映射到统计域。最后在统计域中用细胞神经网络方法对其进行目标分割。由于CNN是由局部互连的细胞组成，因此易于用VLSI实现。通过对图像像素建立细胞近邻模型，可以获得较强的鲁棒运动目标分割。实验的结果反映了该方法的有效性。

Motion Object Segmentation Based on Statistical Feature and Cellular Neural Networks

Proposes a novel approach using cellular neural networks 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 Cellular Neural Networks(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 CNN,sparse nosy pixel can be erased and robust segmenting results of moving objects can be achieved.Experimental results from a real monocular image sequence demonstrate the feasibility of the proposed approach.