一种新的基于时空马尔可夫随机场的运动目标分割技术
A Novel Moving Object Segmentation Technology Based on Spatiotemporal Markov Random Field
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摘要: 在图像处理领域，视频图像序列中的运动目标分割技术是一个被广泛研究的热点课题。该文提出一种新的基于时空马尔可夫随机场的运动目标分割技术。首先，对视频序列的前后3帧图像进行处理，获得两帧初始标记场；随后，对两帧初始标记场进行与操作，获得共同标记场；最后，以原始图像的色彩聚类图像作为先验知识，重新定义Gibbs能量函数，并利用迭代条件模型（ICM）实现最大后验概率（MAP）的估算问题，获得优化标记场。实验结果表明：该模型克服了传统时空马尔可夫随机场模型因运动产生的显露遮挡现象，同时减弱了运动一致性造成的空洞现象并削弱了噪声的影响。
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关键词:
- 图像分割;马尔可夫随机场;迭代条件模型
Abstract: In the field of image processing, the segmentation of moving object in video sequences is a hot research topic in recent years. In this paper, a novel method of moving object segmentation based on spatiotemporal Markov Random Field(MRE) is proposed. Firstly, two observations and two initial labels are derived from the three successive images with the same method in the first scheme. Secondly, the AND-label is obtained with the AND-operation on the two initial labels. Finally, the image segmented with the color clustering algorithm is regarded as prior knowledge, with which the corresponding Gibbs energy function is redefined, and the maximum a posteriori estimator, which is determined by using the iterated conditional mode algorithm, is employed to get optimized labels. The new MRF model contributes to the weakening of the noise and to the elimination of the covered-uncovered background and to the recovery of the uniform moving regions. -
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