采用3D Gaussian Facet模型的亚体素表面检测

提出一种基于Gaussian facet模型的3D边缘检测算法.首先利用Gaussian加权最小二乘拟合,引入空间权因子表达图像采样点对模型参数估计的相对重要度,扩展了经典Haralick facet模型,建立了3D Gaussian facet模型及其计算公式;然后采用抗噪性好的3DIDDG算子估计梯度方向,并在该梯度方向上计算二阶方向导数过零点,以获得表面点亚体素位置.实验结果表明,该算法能有效地降低邻近边缘干涉对检测结果的影响,可更好地提取尺寸较小的结构边缘.

物联网环境下多方位传感器数据的关联性挖掘(英文)

Sensors are ubiquitous in the Internet of Things for measuring and collecting data.Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data.Since the Internet of Things contains many sorts of sensors,the measurement data collected by these sensors are multi-type data,sometimes containing temporal series information.If we separately deal with different sorts of data,we will miss useful information.This paper proposes a method to discover the correlation in multi-faceted data,which contains many types of data with temporal information,and our method can simultaneously deal with multi-faceted data.We transform high-dimensional multi-faceted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models,thenmine the correlation in multi-faceted data by discover the structure of the multivariate Gaussian Graphical Models.With a real data set,we verifies our method,and the experiment demonstrates that the method we propose can correctly find out the correlation among multi-faceted measurement data.

基于改进双向扩散滤波的红外背景抑制算法

针对复杂背景下红外弱小目标图像背景抑制难题,提出了一种基于曲面拟合的双向扩散滤波红外背景抑制新算法。采用高斯Facet模型拟合邻域图像曲面,采用综合方向导数梯度(IDDG)算子描述拟合图像的灰度特征,进而对双向扩散滤波进行改进,并将其与IDDG算子相结合,发展出了具有解析形式的改进的双向扩散滤波算法,给出了该算法关键参数的自适应选取方法。与传统的背景抑制算法相比,本文算法对图像灰度特征的描述更准确,并能据此在前向扩散和后向扩散之间自适应地切换,从而实现了在抑制背景杂波的同时增强目标能量,且能够克服传统算法处理椒盐噪声方面的缺陷。理论分析与仿真实验表明,本文算法对包含强纹理结构的复杂背景杂波具有良好的抑制作用和稳健的适应作用,对于信噪比为0.8的图像,可获得21.6的信噪比增益。