传统网格聚类方法由于没有考虑到相邻网格内的数据点对考查网格的影响，存在不能平滑聚类以及聚类边界判断不清的情况。
Traditional grid clustering methods fail to consider the affect of neighbored grids, and may result in unsmoothed clustering, wrong judgement of clustering boundary, etc.
滑移边界条件下的模拟结果具有良好的周期性，各个周期的过渡性也更加平滑，与实际情况更加接近。
The simulation has good period property, the transition between each period is more smooth and is more close to the real result.
针对边界部分有重叠的图象，提出了一种基于网格匹配的快速对准算法，并通过平滑因子对图象实现了无缝拼接。
In this paper, we present a fast stitching algorithm for the overlapping images based on grid matching, which makes images matching correctly, stitching images seamless and smooth.
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