Multiple sequence alignment （MSA） is the alignment among more than two molecular biological sequences, which is a fundamental method to analyze evolutionary events such as mutations, insertions, deletions, and re-ar...Multiple sequence alignment （MSA） is the alignment among more than two molecular biological sequences, which is a fundamental method to analyze evolutionary events such as mutations, insertions, deletions, and re-arrangements. In theory, a dynamic programming algorithm can be employed to produce the optimal MSA. However, this leads to an explosive increase in computing time and memory consumption as the number of sequences increases （Taylor, 1990）. So far, MSA is still regarded as one of the most challenging problems in bioinformatics and computational biology （Chatzou et al., 2016）.展开更多
点云配准是基于机器视觉进行工业复杂零件三维非接触精密测量的关键环节。为了提高点云配准的效率和准确性,提出一种基于改进法线计算的快速点特征直方图(Fast Point Feature Histograms, FPFH)特征描述子的点云配准方法。采用重心最近...点云配准是基于机器视觉进行工业复杂零件三维非接触精密测量的关键环节。为了提高点云配准的效率和准确性,提出一种基于改进法线计算的快速点特征直方图(Fast Point Feature Histograms, FPFH)特征描述子的点云配准方法。采用重心最近邻体素滤波器对点云进行预处理,减少点的数量同时保留表面细微特征。为解决传统迭代最近点(Iterative Closest Point, ICP)算法对初始位置敏感且收敛速度慢的问题,采用基于改进特征描述子的采样一致性(Sample Consensus Initial Alignment, SAC-IA)初始配准算法进行粗配准,使用基于KDtree加速的ICP算法进行精配准。本文选用三组点云数据,用不同的点云配准方法进行了测试。实验结果显示,在点云添加2%与5%噪声的情况下处理不同规模的点云数据时,所提出的方法配准所用时间和均方根误差(Root Mean Square Error, RMSE,ERMS)仍优于其它两种对比方法。展开更多
基金supported by the National Key R&D Program of China (Nos. 2017YFB0202600, 2016YFC1302500, 2016YFB0200400 and 2017YFB0202104)the National Natural Science Foundation of China (Nos. 61772543, U1435222, 61625202, 61272056 and 61771331)Guangdong Provincial Department of Science and Technology (No. 2016B090918122)
文摘Multiple sequence alignment （MSA） is the alignment among more than two molecular biological sequences, which is a fundamental method to analyze evolutionary events such as mutations, insertions, deletions, and re-arrangements. In theory, a dynamic programming algorithm can be employed to produce the optimal MSA. However, this leads to an explosive increase in computing time and memory consumption as the number of sequences increases （Taylor, 1990）. So far, MSA is still regarded as one of the most challenging problems in bioinformatics and computational biology （Chatzou et al., 2016）.
