为了得到准确、稠密的视差图，提出了一种利用自适应能量聚集和动态规划的立体匹配算法。

In order to get precise and dense disparity maps, a stereo matching algorithm using adaptive cost aggregation and dynamic programming is proposed.

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提出了一种利用遗传算法解决立体匹配问题的方法以获得稠密的视差图。

An approach to addressing the stereo correspondence problem is presented using genetic algorithms (GAs) to obtain a dense disparity map.

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立体匹配是计算机视觉领域的一个重要研究课题，为了得到准确、稠密的视差图，提出了一种基于颜色与空间距离的置信度传播立体匹配算法。

In order to get dense and correct disparity, a stereo matching algorithm based on color and space distance and using belief propagation was proposed.

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