Video Image Segmentation Based on Bayesian Learning
Video Image Segmentation Based on Bayesian Learning
复制标题
基于贝叶斯学习的视频图像分割
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
赵杰煜
中科院分区：
文献类型：
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作者：
王林波;赵杰煜
当背景光照发生变化时，图像分割就成为一个困难的问题。本文将贝叶斯学习方法应用于视频分割中，在像素级上对不断变化的背景进行建模，每个像素的特征向量用离散的概率分布函数表示，直方图颜色和颜色的变化对视频分割效果有很大影响。实验结果表明，基于贝叶斯学习的视频图像分类算法，所提出的方法能够学习照明逐渐或突然改变的复杂背景。
Segmentation becomes a difficult task when the background illumination changes.In this paper,we apply a Bayesian learning method into video segmentation.The constantly changing background has been modeled at the pixel level.The feature vector for each pixel is represented with a discrete probability distribution function.The histogram colors and co-occurrence vectors have been calculated.Bayesian learning has been used to obtain these probability distribution functions from the video image inputs.The experimental results indicate that the proposed approach is able to learn a complex background of which the illumination changes either gradually or suddenly.