WO2020238111A1 - 一种基于半峰值概率密度分布的三维重建方法 - Google Patents
一种基于半峰值概率密度分布的三维重建方法 Download PDFInfo
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- WO2020238111A1 WO2020238111A1 PCT/CN2019/122016 CN2019122016W WO2020238111A1 WO 2020238111 A1 WO2020238111 A1 WO 2020238111A1 CN 2019122016 W CN2019122016 W CN 2019122016W WO 2020238111 A1 WO2020238111 A1 WO 2020238111A1
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- 230000002285 radioactive effect Effects 0.000 claims abstract description 38
- 238000000034 method Methods 0.000 claims abstract description 31
- 239000011159 matrix material Substances 0.000 claims description 12
- 238000003384 imaging method Methods 0.000 claims description 8
- 230000011218 segmentation Effects 0.000 claims description 3
- 238000010586 diagram Methods 0.000 abstract description 12
- 238000011160 research Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
- 230000005251 gamma ray Effects 0.000 description 5
- 230000005855 radiation Effects 0.000 description 5
- 238000001514 detection method Methods 0.000 description 3
- 238000010521 absorption reaction Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000005202 decontamination Methods 0.000 description 1
- 230000003588 decontaminative effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000002059 diagnostic imaging Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 239000012857 radioactive material Substances 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
Definitions
- the invention relates to the technical field of signal processing, in particular to a three-dimensional reconstruction method based on half-peak probability density distribution.
- Nuclear energy has become the world's third largest energy source, and the constant occurrence of nuclear accidents has made nuclear safety a major issue that needs to be resolved.
- the present invention provides a three-dimensional reconstruction method based on the half-peak probability density distribution.
- a three-dimensional reconstruction method based on half-peak probability density distribution including the following steps:
- the contour L XY is the radioactive source corresponding to the i-th spatial layer Reconstruction contour
- step (1) perform 3D voxel segmentation modeling in the imaging area, define the search space as a spatial rectangular coordinate system, and count the maximum value Z max and minimum value Z of the Z-axis coordinates of the point cloud data min , let
- [] is the rounding function
- m1 is the thickness of each space layer
- N is the number of space layers obtained after the space is cut
- step (2) judge and assign the three-dimensional point cloud data, and set the coordinate of any point P in the point cloud as (x p , y p , z p ), and z p satisfies:
- the point P in the i-th spatial layers i.e., the i-th spatial layers and the Z i Z i + 1 sandwiched between a space plane, a plane including the Z i, but does not include the plane Z i + 1;
- P point coordinates (x p , y p , z p ) are transformed into P'(x p , y p , z i ); all the coordinates of the point cloud data are traversed, and all the point cloud data are sequentially projected onto the corresponding plane.
- step (3) the specific process is: the projection plane Z i as an example, the Z plane of the coordinates P i of all projected points' (x p, y p, z i) is a simplified plane coordinate P "(x p ,y p ), count the maximum X max and minimum X min of the X-axis coordinate, and the maximum Y max and the minimum Y min of the Y-axis coordinate;
- [] is a rounding function
- m2 is the grid spacing
- E is the number of columns in the data plane Z i X-axis direction is bisected
- F is the number of lines Z i plane data is equally divided in the Y-axis direction;
- the Z i plane is divided into E*F grids with a side length of m2; the plane data is mapped to a F*E two-dimensional matrix, and the matrix elements a 11 , a 12 ... a jk ... a FE initial The values are all zero, if the point cloud data coordinates:
- the matrix element values express the membership relationship between the point cloud data and the position, and perform the statistical reconstruction of the three-dimensional probability density distribution of the points on the Zi plane to obtain the three-dimensional intensity Graph, which is a three-dimensional probability density graph.
- step (4) from the three-dimensional probability density map, it can be seen that the peak value of the map is w max , w max/2 is the half-peak value, and the half-peak value w max/2 is the plane parallel to the XOY plane. After the probability density graphs intersect, the contour L XY is obtained. This relationship is expressed as follows:
- SLC (X, Y) represents the slice plane at the peak point W max
- G (X, Y, Z) represents the three-dimensional probability density map
- W max/2 represents the half-high probability density plane.
- the method of the present invention can be applied to the field of nuclear power, and can realize rapid reconstruction of radioactive sources after a nuclear accident and accurate reconstruction of the contours of decommissioned radioactive sources.
- the present invention accurately reconstructs the point cloud by the half-peak probability density distribution method, and can obtain the recognized shape of the radioactive source in each direction.
- the present invention can obtain the evaluation index of the reconstructed three-dimensional image from multiple directions.
- the Compton camera is installed on the mobile robot, and the amount of data acquired is large. Compared with the ordinary radioactive source reconstruction method, the point cloud sampling of the present invention is more comprehensive.
- Figure 1 is a schematic diagram of a three-dimensional reconstruction method based on half-peak probability density distribution.
- Figure 2 shows the sample point cloud diagram
- Fig. 3 is a schematic structural diagram of constructing slices along the Z-axis direction of the sample point cloud.
- Figure 4 is a point cloud diagram in the i-th spatial layer.
- Fig. 5 is a schematic diagram of projecting the point cloud in the i-th spatial layer onto the Z i plane.
- Figure 6 is a three-dimensional probability density diagram obtained by constructing the i-th spatial layer.
- Fig. 7 is the density distribution contour of the half-peak plane of the three-dimensional probability density map, that is, the contour of the reconstructed radiation source corresponding to the i-th spatial layer.
- the present invention provides a three-dimensional reconstruction method based on half-peak probability density distribution, as shown in Fig. 1, which specifically includes the following steps:
- the three-dimensional point cloud information of the radioactive source is obtained by Compton imaging robot;
- Compton imaging robot includes Compton camera, gamma ray detector and mobile robot;
- Gamma ray detector passes Kang
- the principle of Puton scattering obtains three-dimensional point cloud information.
- the mobile robot has a positioning system.
- the Compton camera is installed on the mobile robot.
- the gamma-ray detector is a part of the Compton camera.
- the Compton camera is used to obtain the location information of the radioactive source.
- Compton imaging robot can realize multi-angle and multi-point measurement of radioactive sources. By using Compton imaging robot, Compton scattering information from different positions can be obtained.
- the gamma-ray detector can track the input energy E 0 , wavelength ⁇ 0 , scattering energy E 1 , and wavelength ⁇ 1 of the radiation source, because the relationship between energy and scattering angle is expressed as
- me and c are the mass of the electron and the speed of light respectively, and the scattering angle ⁇ can be calculated;
- a cone For each Compton scattering process, a cone can be obtained. If two gamma rays emitted from the same point have the Compton scattering effect, the two corresponding cones will intersect at the source point, and the intersection point is The radioactive source point to be selected. In actual situations, the radioactive source is often not a single point but occupies a certain three-dimensional space of a certain size and shape. Compton scattering around the radioactive source will generate multiple radioactive source points. Collect the three-dimensional point cloud data of the radioactive source.
- the three-dimensional point cloud refers to the part of the point cloud information collected by the gamma ray detector, which is not complete; in fact, the acquisition of the point cloud is a dynamic
- the radioactive source is reconstructed in 3D through the steps of this method.
- the reconstructed radioactive source information is the target point that the mobile robot moves. The mobile robot will continuously collect and update the points when approaching the target point. Cloud information, and the 3D reconstruction model of the radioactive source is also being updated.
- step (1) the specific process of slicing the three-dimensional point cloud along the Z-axis direction is: performing three-dimensional voxel segmentation modeling in the imaging area, defining the search space as a spatial rectangular coordinate system, and counting The maximum value Z max and the minimum value Z min of the Z-axis coordinate of the three-dimensional point cloud data, let
- [] is the rounding function
- m1 is the thickness of each space layer
- N is the number of space layers obtained after the space is divided
- the scattered point information in the i-th spatial layer refers to the three-dimensional point cloud data information in the i-th spatial layer.
- step (2) the specific process of extracting the scattered point information in the i-th spatial layer and projecting it to the Z i plane is as follows:
- the three-dimensional point cloud data is judged and assigned, and the coordinate of any point P in the point cloud is (x p , y p , z p ), and z p satisfies:
- P point coordinates (x p , y p , z p ) are transformed into P'(x p , y p , z i ); all the coordinates of the point cloud data are traversed, and all the point cloud data are sequentially projected onto the corresponding plane.
- step (3) the specific process of constructing the membership functions of the grids and scattered points in the Z i plane is: taking the projection plane Z i as an example, the coordinates of all the projection points in the Z i plane P'(x p ,y p ,z i ) is simplified to plane coordinates P”(x p ,y p ), and count the maximum X max and minimum X min of X-axis coordinates, and the maximum Y max of Y-axis coordinates. The minimum value Y min ; let
- [] is a rounding function
- m2 is the grid spacing
- E is the number of columns in the data plane Z i X-axis direction is bisected
- F is the number of lines Z i plane data is equally divided in the Y-axis direction;
- the Z i plane is divided into E*F grids with a side length of m2, as shown in Figure 5; the plane data is mapped to a F*E two-dimensional matrix, and the matrix elements a 11 , a 12 ... a jk ...a
- the initial values of FE are all zero, if the point cloud data coordinates:
- the matrix element value expressing the degree of membership relation point cloud data with the position refers to an element corresponding to the ranks of the values
- the three-dimensional Zi point aggregated distribution on a plane The probability density distribution is statistically reconstructed to obtain a three-dimensional intensity map, that is, the three-dimensional probability density map is shown in Figure 6.
- step (4) is as follows: from the three-dimensional probability density diagram, it can be seen that the peak value of the three-dimensional probability density diagram is w max , w max/2 is the half-peak value, and the half-peak value w max/2 is the plane parallel to XOY Plane, the contour L XY is obtained after the plane intersects the three-dimensional probability density map, as shown in Figure 6-7; this relationship is expressed as follows:
- SLC (X, Y) represents the slice plane at the peak point, that is, the peak point is used as a plane parallel to the XOY plane;
- G (X, Y, Z) represents the three-dimensional probability density map, W max/2 represents the half height Probability density plane.
- R XY represents the accuracy description on the equivalent recognition unidirectional plane.
- R XY represents the plane where the equivalent recognition contour L XY is located On the accuracy description.
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Abstract
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Claims (5)
- 一种基于半峰值概率密度分布的三维重建方法,其特征是,包括以下步骤:(1)将三维点云沿Z轴方向切片,得到N个空间层;(2)提取第i个空间层内散点信息并将其投影至Z i平面;(3)构建Z i平面各网格与散点的隶属度函数,绘制三维概率密度图;(4)过三维概率密度图半峰值w max/2作平行于XOY平面的平面,该平面与三维概率密度图相交后获得轮廓L XY,该轮廓L XY为第i个空间层对应的放射源重建轮廓;(5)依次叠加N个空间层对应的放射源重建轮廓,获得放射源三维重建模型。
- 根据权利要求1所述的一种基于半峰值概率密度分布的三维重建方法,其特征是,步骤(2)具体的过程为:对三维点云数据进行判断并赋值,设点云中任意一个点P坐标为(x p,y p,z p),z p满足:z i≤z p<z i+1可知P点在第i个空间层,第i个空间层即Z i与Z i+1平面所夹空间,包括Z i平面,但不包括Z i+1平面;将该点从此空间投影至该空间所在的下平面即Z i平面,令z p=z i则P点坐标(x p,y p,z p)变换为P’(x p,y p,z i);遍历点云数据所有坐标,将所有点云数据依次投影至对应的平面中。
- 根据权利要求1所述的一种基于半峰值概率密度分布的三维重建方法,其特征是,步骤(3)具体的过程为:以投影平面Z i为例,将Z i平面内所有投影点的坐标P’(x p,y p,z i)简化为平面坐标P”(x p,y p),统计X轴坐标的最大值X max、最小值X min,Y轴坐标的最大值Y max、最小值Y min;令其中,[]为取整函数,m2为网格步长,E为Z i平面数据在X轴方向被等分的列数,F为Z i平面数据在Y轴方向被等分的行数;此时,Z i平面被划分为E*F个边长为m2的网格;将该平面数据映射为F*E的二维矩阵,设矩阵元素a 11,a 12…a jk…a FE初始值皆为零,若点云数据坐标:则a jk=a jk+1遍历Z i平面内所有点,得到矩阵A FE,矩阵元素值表达了点云数据与该位置的隶属度关系,将Zi平面上的点聚集分布情况进行三维概率密度分布统计重构,得到三维强度图,也即三维概率密度图。
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