公开(公告)号：WO2015050596A3

公开(公告)日：2015-04-09

申请号：PCT/US2014/042562

申请日：2014-06-16

Applicant: HOWARD UNIVERSITY

Inventor： ANDERSON, John ,  NDOYE, Mandoye ,  ODE, Oludotun ,  OGWORONJO, Henry Chidozie

IPC: G06T7/20 ,  F41H11/136

Abstract: The mathematical majorize-minimize principle is applied in various ways to process the image data to provide a more reliable image from the backscatter data using a reduced amount of memory and processing resources. A processing device processes the data set by creating an estimated image value for each voxel in the image by iteratively deriving the estimated image value through application of a majorize-minimize principle to solve a maximum a posteriori (MAP) estimation problem associated with a mathematical model of image data from the data. A prior probability density function for the unknown reflection coefficients is used to apply an assumption that a majority of the reflection coefficients are small. The described prior probability density functions promote sparse solutions automatically estimated from the observed data.

公开(公告)号：WO2015050596A9

公开(公告)日：2015-04-09

申请号：PCT/US2014/042562

申请日：2014-06-16

Applicant: HOWARD UNIVERSITY

Inventor： ANDERSON, John ,  NDOYE, Mandoye ,  ODE, Oludotun ,  OGWORONJO, Henry Chidozie

IPC: G06T7/20 ,  F41H11/136

Abstract: The mathematical majorize-minimize principle is applied in various ways to process the image data to provide a more reliable image from the backscatter data using a reduced amount of memory and processing resources. A processing device processes the data set by creating an estimated image value for each voxel in the image by iteratively deriving the estimated image value through application of a majorize-minimize principle to solve a maximum a posteriori (MAP) estimation problem associated with a mathematical model of image data from the data. A prior probability density function for the unknown reflection coefficients is used to apply an assumption that a majority of the reflection coefficients are small. The described prior probability density functions promote sparse solutions automatically estimated from the observed data.

公开(公告)号：WO2015050596A2

公开(公告)日：2015-04-09

申请号：PCT/US2014/042562

申请日：2014-06-16

Applicant: HOWARD UNIVERSITY

Inventor： ANDERSON, John ,  NDOYE, Mandoye ,  ODE, Oludotun ,  OGWORONJO, Henry Chidozie

IPC: G01S13/02

CPC classification number: G01S7/292 ,  G01S7/2923 ,  G01S13/0209 ,  G01S13/885 ,  G01S13/887 ,  G01S13/89 ,  G06K9/00791 ,  G06K9/6277

Abstract: The mathematical majorize-minimize principle is applied in various ways to process the image data to provide a more reliable image from the backscatter data using a reduced amount of memory and processing resources. A processing device processes the data set by creating an estimated image value for each voxel in the image by iteratively deriving the estimated image value through application of a majorize-minimize principle to solve a maximum a posteriori (MAP) estimation problem associated with a mathematical model of image data from the data. A prior probability density function for the unknown reflection coefficients is used to apply an assumption that a majority of the reflection coefficients are small. The described prior probability density functions promote sparse solutions automatically estimated from the observed data.

Abstract translation: 数学主要最小化原理以各种方式应用于处理图像数据，以使用减少量的存储器和处理资源从后向散射数据提供更可靠的图像。 处理装置通过使用主要最小化原理迭代地导出估计图像值来求解与数学模型相关联的最大后验（MAP）估计问题，通过为图像中的每个体素创建估计图像值来处理数据集 的图像数据。 用于未知反射系数的先验概率密度函数用于应用大多数反射系数小的假设。 所描述的先验概率密度函数促进从观测数据自动估计的稀疏解。