Patterned-based time series segmentation (PTSS) is an important task for many time series data mining applications. In this paper, according to the characteristics of PTSS, a generalized model is proposed for PTSS. Fi...Patterned-based time series segmentation (PTSS) is an important task for many time series data mining applications. In this paper, according to the characteristics of PTSS, a generalized model is proposed for PTSS. First, a new inter-pretation for PTSS is given by comparing this problem with the prototype-based clustering (PC). Then, a novel model, called clustering-inverse model (CI-model), is presented. Finally, two algorithms are presented to implement this model. Our experimental results on artificial and real-world time series demonstrate that the proposed algorithms are quite effective.展开更多
Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography(CT)images.The segmentation of hepatic organ is more intricate task,owing to the fact that it posses...Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography(CT)images.The segmentation of hepatic organ is more intricate task,owing to the fact that it possesses a sizeable quantum of vascularization.This paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT scans.The effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or not.This involves segmentation of the region of interest(ROI)from the segmented liver,extraction of the shape and texture features from the segmented ROI and classification of the ROIs as tumorous or not by using a classifier based on the extracted features.In this work,the proposed seed point selection technique has been used in level set algorithm for segmentation of liver region in CT scans and the ROIs have been extracted using Fuzzy C Means clustering(FCM)which is one of the algorithms to segment the images.The dataset used in this method has been collected from various repositories and scan centers.The outcome of this proposed segmentation model has reduced the area overlap error that could offer the intended accuracy and consistency.It gives better results when compared with other existing algorithms.Fast execution in short span of time is another advantage of this method which in turns helps the radiologist to ascertain the abnormalities instantly.展开更多
井下斜坡道的定位与建图是实现井下斜坡道无人驾驶的关键技术之一,矿山井下斜坡道区域为典型非结构化环境特征,且道路具有一定倾斜角度,采用传统SLAM算法无法获得精确里程计信息,导致定位与建图精度难以满足无人矿卡行驶需求。针对上述...井下斜坡道的定位与建图是实现井下斜坡道无人驾驶的关键技术之一,矿山井下斜坡道区域为典型非结构化环境特征,且道路具有一定倾斜角度,采用传统SLAM算法无法获得精确里程计信息,导致定位与建图精度难以满足无人矿卡行驶需求。针对上述问题,通过研究激光SLAM(Simultaneous Localization And Mapping)算法LeGO-LOAM,笔者提出一种适用于矿山井下斜坡道环境的定位与建图方法。首先,针对井下斜坡道口两侧均为光滑水泥墙壁,特征点稀少问题,设计了基于人工路标的辅助增强定位方法,有效增加点云特征数量,从而优化位姿估计结果,避免建图漂移现象;然后在特征预处理阶段,提出了一种基于激光点云高度差与坡度信息融合的提取地面点高效算法,通过改善地面地点的选取策略,针对倾斜坑洼路面仍能有效识别地面点,解决了井下斜坡道定位与建图倾斜角度大、误差大等问题;其次,基于CVC(Curved-Voxel Clustering)聚类算法设计了一种斜坡道点云曲率体素聚类算法,采用曲率体素和基于哈希的数据结构对点云进行分割,大幅提高在井下稀疏、噪声环境下点云聚类的鲁棒性;最后,运用Scan-To-Map进行点云匹配,同时兼顾点云配准的性能与速度。在中钢集团山东某井下斜坡道的现场实验证明:与原算法相比精度提升13.15%,Z轴误差降低22.3%,地图质量明显提升,能有效解决井下无人驾驶建图及定位的难题。展开更多
基于激光雷达(Light Detection And Ranging,LiDAR)数据重建树体三维模型并精准获取林木空间枝干结构参数对林木性状评价、森林动态经营管理与可视化研究具有重要意义。为此提出一种基于骨架细化提取的树木模型重建方法。首先,采用Focus...基于激光雷达(Light Detection And Ranging,LiDAR)数据重建树体三维模型并精准获取林木空间枝干结构参数对林木性状评价、森林动态经营管理与可视化研究具有重要意义。为此提出一种基于骨架细化提取的树木模型重建方法。首先,采用FocusS350/350 PLUS三维激光扫描仪获取3块不同树龄橡胶树的样地数据。然后,作为细化建模的重点,将枝干点云从原始树点中分离出来,再将其过度分割为若干点云簇,通过相邻点云簇判断是否有分枝以及动态确定骨架点间距,并将其运用在空间殖民算法以此来生成树的三维骨架点和骨架点连通性链表,根据连通链表结构自动识别树木中的主枝干和各个一级分枝,再通过广义圆柱体生成树干完成树木三维重建。最后,利用数字孪生技术对这3块不同树龄样地树木进行三维实景建模,使其穿越时空在同一空间中重现,以便更为直观地观察树木在生长过程中的形态变化。该算法得到的橡胶树胸径与实测值比对为,决定系数(R^(2))>0.91,均方根误差(root mean square Error,RMSE)<1.00 cm;主枝干与一级枝干的分枝角为,R^(2)>0.91,RMSE<2.93;一级枝干直径为,R^(2)>0.90,RMSE<1.41 cm;将3个树龄放在一起计算其生长参数,并与实测值进行对比,发现该算法同样适用于异龄林样地的各个生长参数计算。同时发现橡胶树的一级枝条的直径越大,其相对应的叶团簇体积就越大。运用人工智能的理论模型来处理林木的激光点云数据,旨在为森林的可视化以及树木骨架结构的智能化分析与处理等研究领域提供有价值的参考。展开更多
Efficient parameterization of point-sampled surfaces is a fundamental problem in the field of digital geometry processing. In order to parameterize a given point-sampled surface for minimal distance distortion, a diff...Efficient parameterization of point-sampled surfaces is a fundamental problem in the field of digital geometry processing. In order to parameterize a given point-sampled surface for minimal distance distortion, a differentialslbased segmentation and parameterization approach is proposed in this paper. Our approach partitions the point-sampled geometry based on two criteria： variation of Euclidean distance between sample points, and angular difference between surface differential directions. According to the analysis of normal curvatures for some specified directions, a new projection approach is adopted to estimate the local surface differentials. Then a k-means clustering （k-MC） algorithm is used for partitioning the model into a set of charts based on the estimated local surface attributes. Finally, each chart is parameterized with a statistical method -- multidimensional scaling （MDS） approach, and the parameterization results of all charts form an atlas for compact storage.展开更多
文摘Patterned-based time series segmentation (PTSS) is an important task for many time series data mining applications. In this paper, according to the characteristics of PTSS, a generalized model is proposed for PTSS. First, a new inter-pretation for PTSS is given by comparing this problem with the prototype-based clustering (PC). Then, a novel model, called clustering-inverse model (CI-model), is presented. Finally, two algorithms are presented to implement this model. Our experimental results on artificial and real-world time series demonstrate that the proposed algorithms are quite effective.
文摘Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography(CT)images.The segmentation of hepatic organ is more intricate task,owing to the fact that it possesses a sizeable quantum of vascularization.This paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT scans.The effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or not.This involves segmentation of the region of interest(ROI)from the segmented liver,extraction of the shape and texture features from the segmented ROI and classification of the ROIs as tumorous or not by using a classifier based on the extracted features.In this work,the proposed seed point selection technique has been used in level set algorithm for segmentation of liver region in CT scans and the ROIs have been extracted using Fuzzy C Means clustering(FCM)which is one of the algorithms to segment the images.The dataset used in this method has been collected from various repositories and scan centers.The outcome of this proposed segmentation model has reduced the area overlap error that could offer the intended accuracy and consistency.It gives better results when compared with other existing algorithms.Fast execution in short span of time is another advantage of this method which in turns helps the radiologist to ascertain the abnormalities instantly.
文摘井下斜坡道的定位与建图是实现井下斜坡道无人驾驶的关键技术之一,矿山井下斜坡道区域为典型非结构化环境特征,且道路具有一定倾斜角度,采用传统SLAM算法无法获得精确里程计信息,导致定位与建图精度难以满足无人矿卡行驶需求。针对上述问题,通过研究激光SLAM(Simultaneous Localization And Mapping)算法LeGO-LOAM,笔者提出一种适用于矿山井下斜坡道环境的定位与建图方法。首先,针对井下斜坡道口两侧均为光滑水泥墙壁,特征点稀少问题,设计了基于人工路标的辅助增强定位方法,有效增加点云特征数量,从而优化位姿估计结果,避免建图漂移现象;然后在特征预处理阶段,提出了一种基于激光点云高度差与坡度信息融合的提取地面点高效算法,通过改善地面地点的选取策略,针对倾斜坑洼路面仍能有效识别地面点,解决了井下斜坡道定位与建图倾斜角度大、误差大等问题;其次,基于CVC(Curved-Voxel Clustering)聚类算法设计了一种斜坡道点云曲率体素聚类算法,采用曲率体素和基于哈希的数据结构对点云进行分割,大幅提高在井下稀疏、噪声环境下点云聚类的鲁棒性;最后,运用Scan-To-Map进行点云匹配,同时兼顾点云配准的性能与速度。在中钢集团山东某井下斜坡道的现场实验证明:与原算法相比精度提升13.15%,Z轴误差降低22.3%,地图质量明显提升,能有效解决井下无人驾驶建图及定位的难题。
文摘基于激光雷达(Light Detection And Ranging,LiDAR)数据重建树体三维模型并精准获取林木空间枝干结构参数对林木性状评价、森林动态经营管理与可视化研究具有重要意义。为此提出一种基于骨架细化提取的树木模型重建方法。首先,采用FocusS350/350 PLUS三维激光扫描仪获取3块不同树龄橡胶树的样地数据。然后,作为细化建模的重点,将枝干点云从原始树点中分离出来,再将其过度分割为若干点云簇,通过相邻点云簇判断是否有分枝以及动态确定骨架点间距,并将其运用在空间殖民算法以此来生成树的三维骨架点和骨架点连通性链表,根据连通链表结构自动识别树木中的主枝干和各个一级分枝,再通过广义圆柱体生成树干完成树木三维重建。最后,利用数字孪生技术对这3块不同树龄样地树木进行三维实景建模,使其穿越时空在同一空间中重现,以便更为直观地观察树木在生长过程中的形态变化。该算法得到的橡胶树胸径与实测值比对为,决定系数(R^(2))>0.91,均方根误差(root mean square Error,RMSE)<1.00 cm;主枝干与一级枝干的分枝角为,R^(2)>0.91,RMSE<2.93;一级枝干直径为,R^(2)>0.90,RMSE<1.41 cm;将3个树龄放在一起计算其生长参数,并与实测值进行对比,发现该算法同样适用于异龄林样地的各个生长参数计算。同时发现橡胶树的一级枝条的直径越大,其相对应的叶团簇体积就越大。运用人工智能的理论模型来处理林木的激光点云数据,旨在为森林的可视化以及树木骨架结构的智能化分析与处理等研究领域提供有价值的参考。
基金This work is supported by the National Grand Fundamental Research 973 Program of China under Grant No.2002CB312101National Natural Science Foundation of China(NSFC)under Grant Nos. 60503056,60333010the Natural Science Foundation of Zhejiang Province under Grant No.R106449.
文摘Efficient parameterization of point-sampled surfaces is a fundamental problem in the field of digital geometry processing. In order to parameterize a given point-sampled surface for minimal distance distortion, a differentialslbased segmentation and parameterization approach is proposed in this paper. Our approach partitions the point-sampled geometry based on two criteria： variation of Euclidean distance between sample points, and angular difference between surface differential directions. According to the analysis of normal curvatures for some specified directions, a new projection approach is adopted to estimate the local surface differentials. Then a k-means clustering （k-MC） algorithm is used for partitioning the model into a set of charts based on the estimated local surface attributes. Finally, each chart is parameterized with a statistical method -- multidimensional scaling （MDS） approach, and the parameterization results of all charts form an atlas for compact storage.
