在步态识别中,最关键的是获取步态特征之后如何选择最佳投影方向,且计算复杂度较小.因此,根据对现有算法的分析,提出一种基于轮廓特征的的广义步态识别算法.在传统的线性判别分析方法基础上,通过重新定义样本类间离散矩阵寻找最佳投影方...在步态识别中,最关键的是获取步态特征之后如何选择最佳投影方向,且计算复杂度较小.因此,根据对现有算法的分析,提出一种基于轮廓特征的的广义步态识别算法.在传统的线性判别分析方法基础上,通过重新定义样本类间离散矩阵寻找最佳投影方向,使不同的目标映射到同一低维空间中,在保留同类结构信息的同时最大化不同类的间距.首先对每个序列进行运动轮廓提取,根据轮廓解卷绕方法将二维轮廓形状转换为一维距离信号,并通过广义线性判别分析方法(Generalized Linear Discriminative Analysis,GLDA)得到最佳投影空间,最终利用支持向量机(Support Vector M achine,SVM)完成分类识别.实验结果表明,该算法简单有效,具有更高的识别率,并且计算代价及处理速度明显优于其他现有算法.展开更多
This paper explores brain CT slices segmentation technique and some related problems, including contours segmentation algorithms, edge detector, algorithm evaluation and experimental results. This article describes a ...This paper explores brain CT slices segmentation technique and some related problems, including contours segmentation algorithms, edge detector, algorithm evaluation and experimental results. This article describes a method for contour-based segmentation of anatomical structures in 3D medical data sets. With this method, the user manually traces one or more 2D contours of an anatomical structure of interest on parallel planes arbitrarily cutting the data set. The experimental results showes the segmentation based on 3D brain volume and 2D CT slices. The main creative contributions in this paper are: (1) contours segmentation algorithm; (2) edge detector; (3) algorithm evaluation.展开更多
文摘在步态识别中,最关键的是获取步态特征之后如何选择最佳投影方向,且计算复杂度较小.因此,根据对现有算法的分析,提出一种基于轮廓特征的的广义步态识别算法.在传统的线性判别分析方法基础上,通过重新定义样本类间离散矩阵寻找最佳投影方向,使不同的目标映射到同一低维空间中,在保留同类结构信息的同时最大化不同类的间距.首先对每个序列进行运动轮廓提取,根据轮廓解卷绕方法将二维轮廓形状转换为一维距离信号,并通过广义线性判别分析方法(Generalized Linear Discriminative Analysis,GLDA)得到最佳投影空间,最终利用支持向量机(Support Vector M achine,SVM)完成分类识别.实验结果表明,该算法简单有效,具有更高的识别率,并且计算代价及处理速度明显优于其他现有算法.
文摘This paper explores brain CT slices segmentation technique and some related problems, including contours segmentation algorithms, edge detector, algorithm evaluation and experimental results. This article describes a method for contour-based segmentation of anatomical structures in 3D medical data sets. With this method, the user manually traces one or more 2D contours of an anatomical structure of interest on parallel planes arbitrarily cutting the data set. The experimental results showes the segmentation based on 3D brain volume and 2D CT slices. The main creative contributions in this paper are: (1) contours segmentation algorithm; (2) edge detector; (3) algorithm evaluation.