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中文摘要：
为避免点云数据处理过程中的过光顺和局部失真现象，利用丛于核函数的蚁群聚类算法对点云数据进行分析，在高维特征空间达到线性可聚的口的。通过核函数将散乱数据点的曲率及法矢映射到高维特征空间，并将它们l住特征空间的加午义距离作为相似性的度量，米分析町能的噪声点和局部特征。对法矢进行光顺调整时，采用类内方差自适应地确定凋整阈值。实验结果表明，该算法比经典算法有明显的改善，并且较好地保留了原始数据的一些特征信息。
英文摘要：
To avoid phenomena of excessive smoothing and the local distortion, a kernel function based ant colony clustering algorithm was proposed to analyze the point clouds data, which was linear in high-dimensional feature space. Curvature and normal were mapped into feature space hy kernel function, using weighted dl.stance as the similarity measure to analyze the possible noise points and local features. Interclass variance was adopted to calculate the threshold adaptively when smoothing the normal vector. Experimental results showed that the presented algorithm had significant improvements than the classical algorithms which preserved some feature information of original da ta.
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An efficient joint implementation of three stages for fast computation of color space conversation i
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