1. 中南大学信息科学与工程学院,湖南,长沙,410083
2. 怀化学院计算机科学与技术系,湖南,怀化,418008
纸质出版:2007
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陈 姝, 邹北骥, 彭小宁, 等. 应用粒子滤波及先验概率模型 进行图像分割的新算法[J]. 电子学报, 2007,35(8):1533-1537.
CHEN Shu, ZOU Bei-ji, PENG Xiao-ning, et al. A New Algorithm for Image Segmenting by Using SMC and Prior Probability Model[J]. Acta Electronica Sinica, 2007, 35(8): 1533-1537.
基于粒子滤波在非线性非高斯情况下具有较好的预测结果
本文提出了一种自适应背景图像分割新算法
该算法利用粒子滤波对下一帧的前景区域进行预测
进而计算出下一帧各像素点属于背景的概率以指导下一帧图像分割;在前景像素值与背景像素值相近的情况下利用先验知识进行图像分割是一种较好的方法
本文以粒子滤波预测结果与先验概率模型计算结果的均值作为当前像素点属于背景的概率来进行图像分割
实验结果表明
该方法在背景变化范围较大的情况下
可以减少前景点误分割为背景点的概率.
A new adaptive algorithm is proposed by taken advantage of SMC(Sequential Monte Carlo) which have better predictive results under the condition of nonlinear non-Gaussian.The algorithm uses particle filtering to predict an anticipated foreground district for a coming frame.Moreover
it calculates the probability of pixels to be part of background in the coming frame to guide image segmentation.It is a good method to segment image on the setting where the pixel values of foreground similar to the ones of the background by using prior knowledge.This paper uses the probability of pixels to be part of background which is calculated by the average of the predict results of particle filtering and the calculated results of prior probability model to segment image.Experimental results show that the proposed algorithm can reduce the error of the pixels of foreground to be segmented as pixels of background compared with 3 rule when changes in background occur quickly.
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