Industrial data mining usually deals with data from different sources.These heterogeneous datasets describe the same object in different views.However,samples from some of the datasets may be lost.Then the remaining s...Industrial data mining usually deals with data from different sources.These heterogeneous datasets describe the same object in different views.However,samples from some of the datasets may be lost.Then the remaining samples do not correspond one-to-one correctly.Mismatched datasets caused by missing samples make the industrial data unavailable for further machine learning.In order to align the mismatched samples,this article presents a cooperative iteration matching method(CIMM)based on the modified dynamic time warping(DTW).The proposed method regards the sequentially accumulated industrial data as the time series.Mismatched samples are aligned by the DTW.In addition,dynamic constraints are applied to the warping distance of the DTW process to make the alignment more efficient.Then a series of models are trained with the cumulated samples iteratively.Several groups of numerical experiments on different missing patterns and missing locations are designed and analyzed to prove the effectiveness and the applicability of the proposed method.展开更多
The fundamental step to get a Statistical Shape Model (SSM) is to align all the training samples to the same spatial modality. In this paper, we propose a new 3D alignment method for organic training samples matching,...The fundamental step to get a Statistical Shape Model (SSM) is to align all the training samples to the same spatial modality. In this paper, we propose a new 3D alignment method for organic training samples matching, whose modalities are orientable and surface figures could be recognized. It is a feature based alignment method which matches two models depending on the distribution of surface curvature. According to the affine transformation on 2D Gaussian map, the distances between the corresponding parts on surface could be minimized. We applied our proposed method on 5 cases left lung training samples alignment and 4 cases liver training samples alignment. The experiment results were performed on the left lung training samples and the liver training samples. The availability of proposed method was confirmed.展开更多
Suboptimal alignments always reveal additional interesting biological features and have been successfully used to informally estimate the significance of an optimal alignment. Besides, traditional dynamic programming ...Suboptimal alignments always reveal additional interesting biological features and have been successfully used to informally estimate the significance of an optimal alignment. Besides, traditional dynamic programming algorithms for sequence comparison require quadratic space, and hence are infeasible for long protein or DNA sequences. In this paper, a space-efficient sampling algorithm for computing suboptimal alignments is described. The algorithm uses a general gap model, where the cost associated with gaps is given by an affine score, and randomly selects an alignment according to the distribution of weights of all potential alignments. If x and y are two sequences with lengths n and m, respectively, then the space requirement of this algorithm is linear to the sum of n and m. Finally, an example illustrates the utility of the algorithm.展开更多
视频帧之间不仅具有空间相关性,还存在时间相关性。根据低分辨率视频重建高分辨率视频时,可以利用相邻的多帧信息对齐到目标帧,以指导当前帧的恢复。相邻帧之间的对齐一般采用光流指导的可变形卷积进行显式对齐,这种方法克服了可变形卷...视频帧之间不仅具有空间相关性,还存在时间相关性。根据低分辨率视频重建高分辨率视频时,可以利用相邻的多帧信息对齐到目标帧,以指导当前帧的恢复。相邻帧之间的对齐一般采用光流指导的可变形卷积进行显式对齐,这种方法克服了可变形卷积的不稳定性,但会影响帧中高频信息的恢复,降低对齐信息的准确性并放大伪影。为解决上述问题,提出了一种基于隐式对齐的视频超分模型IAVSR(Implicit Alignment Video Super-Resolution)。IAVSR通过偏移量和原始值将光流编码到特定像素位置,以此计算光流预对齐的信息而不是利用插值函数插值获得,随后利用光流指导的可变形卷积对计算后的预对齐特征进行重对齐,以帮助高频信息的恢复。在双向传播中利用前两帧传播的信息进行对齐来指导当前帧的恢复,并引入残差网络结构,在提高对齐信息准确性的同时避免引入过多的参数。在REDS4公开数据集上的实验结果表明,IAVSR的峰值信噪比(PSNR)比基准模型提高了0.6 dB,且模型训练时的收敛速度提升了20%。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the Key National Natural Science Foundation of China(No.U1864211)the National Natural Science Foundation of China(No.11772191)the Natural Science Foundation of Shanghai(No.21ZR1431500)。
文摘Industrial data mining usually deals with data from different sources.These heterogeneous datasets describe the same object in different views.However,samples from some of the datasets may be lost.Then the remaining samples do not correspond one-to-one correctly.Mismatched datasets caused by missing samples make the industrial data unavailable for further machine learning.In order to align the mismatched samples,this article presents a cooperative iteration matching method(CIMM)based on the modified dynamic time warping(DTW).The proposed method regards the sequentially accumulated industrial data as the time series.Mismatched samples are aligned by the DTW.In addition,dynamic constraints are applied to the warping distance of the DTW process to make the alignment more efficient.Then a series of models are trained with the cumulated samples iteratively.Several groups of numerical experiments on different missing patterns and missing locations are designed and analyzed to prove the effectiveness and the applicability of the proposed method.
文摘The fundamental step to get a Statistical Shape Model (SSM) is to align all the training samples to the same spatial modality. In this paper, we propose a new 3D alignment method for organic training samples matching, whose modalities are orientable and surface figures could be recognized. It is a feature based alignment method which matches two models depending on the distribution of surface curvature. According to the affine transformation on 2D Gaussian map, the distances between the corresponding parts on surface could be minimized. We applied our proposed method on 5 cases left lung training samples alignment and 4 cases liver training samples alignment. The experiment results were performed on the left lung training samples and the liver training samples. The availability of proposed method was confirmed.
基金supported by the National Natural Science Foundation of China (Grant No.10771133)
文摘Suboptimal alignments always reveal additional interesting biological features and have been successfully used to informally estimate the significance of an optimal alignment. Besides, traditional dynamic programming algorithms for sequence comparison require quadratic space, and hence are infeasible for long protein or DNA sequences. In this paper, a space-efficient sampling algorithm for computing suboptimal alignments is described. The algorithm uses a general gap model, where the cost associated with gaps is given by an affine score, and randomly selects an alignment according to the distribution of weights of all potential alignments. If x and y are two sequences with lengths n and m, respectively, then the space requirement of this algorithm is linear to the sum of n and m. Finally, an example illustrates the utility of the algorithm.
文摘视频帧之间不仅具有空间相关性,还存在时间相关性。根据低分辨率视频重建高分辨率视频时,可以利用相邻的多帧信息对齐到目标帧,以指导当前帧的恢复。相邻帧之间的对齐一般采用光流指导的可变形卷积进行显式对齐,这种方法克服了可变形卷积的不稳定性,但会影响帧中高频信息的恢复,降低对齐信息的准确性并放大伪影。为解决上述问题,提出了一种基于隐式对齐的视频超分模型IAVSR(Implicit Alignment Video Super-Resolution)。IAVSR通过偏移量和原始值将光流编码到特定像素位置,以此计算光流预对齐的信息而不是利用插值函数插值获得,随后利用光流指导的可变形卷积对计算后的预对齐特征进行重对齐,以帮助高频信息的恢复。在双向传播中利用前两帧传播的信息进行对齐来指导当前帧的恢复,并引入残差网络结构,在提高对齐信息准确性的同时避免引入过多的参数。在REDS4公开数据集上的实验结果表明,IAVSR的峰值信噪比(PSNR)比基准模型提高了0.6 dB,且模型训练时的收敛速度提升了20%。
