CN111402307B - Method for processing electric water level image - Google Patents

Method for processing electric water level image Download PDF

Info

Publication number
CN111402307B
CN111402307B CN202010186017.6A CN202010186017A CN111402307B CN 111402307 B CN111402307 B CN 111402307B CN 202010186017 A CN202010186017 A CN 202010186017A CN 111402307 B CN111402307 B CN 111402307B
Authority
CN
China
Prior art keywords
image
data
spots
spot
value
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202010186017.6A
Other languages
Chinese (zh)
Other versions
CN111402307A (en
Inventor
张益恭
苏婕
杨磊
王建成
陈林飞
程向明
张桢君
张冠军
冒蔚
铁琼仙
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Yunnan Astronomical Observatory of CAS
Original Assignee
Yunnan Astronomical Observatory of CAS
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Yunnan Astronomical Observatory of CAS filed Critical Yunnan Astronomical Observatory of CAS
Priority to CN202010186017.6A priority Critical patent/CN111402307B/en
Publication of CN111402307A publication Critical patent/CN111402307A/en
Application granted granted Critical
Publication of CN111402307B publication Critical patent/CN111402307B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/30Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/33Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration
    • G06T5/70Denoising; Smoothing
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/30Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/35Determination of transform parameters for the alignment of images, i.e. image registration using statistical methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20024Filtering details
    • G06T2207/20032Median filtering

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Probability & Statistics with Applications (AREA)
  • Quality & Reliability (AREA)
  • Image Processing (AREA)

Abstract

本申请公开了一种电水准图像的处理方法,其特征在于,包括以下步骤:取得转轴前和转轴后预设数量的原始电水准图像;根据原始电水准图像,确定x、y坐标系和需搜寻像斑数量;对所取得的原始电水准图像进行中值滤波处理得到去噪电水准图像;根据灰度值,在所述去噪电水准图像中寻找所确定数量的像斑,并分别确定所搜寻到的像斑的中心位置;在所述去噪电水准图像中按照像斑的中心位置在x、y坐标系中的坐标进行排序编号;将去噪电水准图像上相同编号的像斑的整理x轴、y轴坐标值的数据列,进行统计分析,判断是否存在数据异常的像斑点。本发明的一个技术效果在于,在无需调整光路及机械结构的情况下,图像处理结果能够得到较为稳定的处理结果。

Figure 202010186017

The present application discloses a method for processing electric level images, which is characterized in that it includes the following steps: obtaining a preset number of original electric level images before and after the rotating shaft; Searching for the number of image spots; performing median filtering on the obtained original electrical level image to obtain a denoised electrical level image; according to the gray value, searching for a determined number of image spots in the denoised electrical level image, and determining respectively The central position of the searched image spot; in the denoising electric level image, sort and number according to the coordinates of the center position of the image spot in the x, y coordinate system; the same numbered image spot on the denoising electric level image Organize the data columns of the x-axis and y-axis coordinate values, and perform statistical analysis to determine whether there are abnormal data spots. A technical effect of the present invention is that, without adjusting the optical path and the mechanical structure, the image processing result can obtain a relatively stable processing result.

Figure 202010186017

Description

一种电水准图像的处理方法A processing method of electric level image

技术领域technical field

本申请属于天文仪器检测领域,具体地说,涉及一种电水准图像的处理方法。The application belongs to the field of detection of astronomical instruments, and in particular relates to a processing method of electric level images.

背景技术Background technique

多功能天文经纬仪是一种天体测量仪器,为了能够得到高精度的测量结果,需要测定并扣除各种仪器误差的影响。水平差是其中的一个主要仪器误差,该误差主要来源于仪器基墩的偏斜和仪器的制造误差以及安装误差。还由于环境温度的变化以及仪器热变形的影响,水平差是在不断变化。因此,如何对天体测量仪器的水平差进行实时测定是提高仪器观测精度的关键。为了实时测定水平差的变化,多功能天文经纬仪采用电水准检测仪来测定水平差。其主要原理为:采用水银面作为基准反射面,通过自准直的方式,利用模拟CCD相机获取仪器转轴前后的电水准图像,该图像通常包含3×3的像斑阵列(见图9和图10)。The multifunctional astronomical theodolite is a kind of astrometric instrument. In order to obtain high-precision measurement results, it is necessary to measure and deduct the influence of various instrument errors. The level difference is one of the main instrument errors, which mainly comes from the deflection of the instrument pier and the manufacturing error and installation error of the instrument. Also due to changes in ambient temperature and the effects of thermal deformation of the instrument, the level difference is constantly changing. Therefore, how to measure the level difference of astrometric instruments in real time is the key to improving the observation accuracy of the instruments. In order to measure the change of the level difference in real time, the multifunctional astronomical theodolite uses an electric level detector to measure the level difference. Its main principle is: using the mercury surface as the reference reflection surface, and using the analog CCD camera to obtain the electric level image before and after the rotating shaft of the instrument through self-collimation, the image usually contains a 3×3 image spot array (see Fig. 9 and Fig. 10).

但是由于电水准自准直光路的光轴偏置影响,水银反射面氧化以及所配置的模拟CCD相机量子效率较低、背景噪声较大,所得到的电水准图像信噪比较低,并且像斑亮度不均匀,图像较为劣质,不利于得到稳定的处理结果。光机结构的一些应力变化以及光路的装调结果不够理想能够导致光路偏置,使得所获取的电水准图像出现像斑亮度不一致的情况(如图1—图8所示),这样会使某个像斑信噪比过低而导致识别错误(图10),从而影响最终的定心结果。However, due to the influence of the optical axis offset of the electric level autocollimation optical path, the oxidation of the mercury reflective surface, and the low quantum efficiency of the analog CCD camera configured, and the large background noise, the signal-to-noise ratio of the obtained electric level image is low, and the image The brightness of the spot is uneven, and the image is relatively inferior, which is not conducive to obtaining a stable processing result. Some stress changes in the optical-mechanical structure and the unsatisfactory adjustment results of the optical path can lead to the deviation of the optical path, resulting in inconsistent brightness of the image spots in the obtained electrical level images (as shown in Figure 1-8), which will cause some The signal-to-noise ratio of each image spot is too low, which leads to recognition errors (Figure 10), thus affecting the final centering result.

因此,有必要提供一种电水准图像的处理方法。Therefore, it is necessary to provide a method for processing electrical level images.

发明内容Contents of the invention

本发明的一个目的是提供一种电水准图像处理方法的新技术方案。An object of the present invention is to provide a new technical solution for an electrical level image processing method.

根据本发明的一个方面,本发明提供的一种电水准图像的处理方法,包括以下步骤:According to one aspect of the present invention, a method for processing an electrical level image provided by the present invention includes the following steps:

取得转轴前和转轴后预设数量的原始电水准图像;Obtain a preset number of raw level images before and after the pivot;

根据原始电水准图像,确定x、y坐标系和需搜寻像斑数量;According to the original electrical level image, determine the x, y coordinate system and the number of image spots to be searched;

对所取得的原始电水准图像进行中值滤波处理得到去噪电水准图像;转轴前预设数量的原始电水准图像所对应的去噪电水准图像为A组;转轴后预设数量的原始电水准图像所对应的去噪电水准图像为B组;Perform median filtering on the obtained original electrical level images to obtain denoised electrical level images; the denoised electrical level images corresponding to the preset number of original electrical level images before the rotation are group A; the preset number of original electrical level images after the rotation The denoised electric level image corresponding to the level image is group B;

根据灰度值,在所述去噪电水准图像中寻找所确定数量的像斑,并分别确定所搜寻到的像斑的中心位置;Searching for the determined number of image spots in the denoised electrical level image according to the gray value, and determining the center positions of the searched image spots respectively;

在所述去噪电水准图像中按照像斑的中心位置在x、y坐标系中的坐标进行排序编号;Sorting and numbering in the denoising electrical level image according to the coordinates of the central position of the image spot in the x, y coordinate system;

分别将A组和B组中的去噪电水准图像上相同编号的像斑的整理x轴、y轴坐标值的数据列,进行统计分析,判断是否存在数据异常的像斑点;若存在,剔除该像斑的x轴,y轴坐标值数据列,剩余像斑的中心位置定位数据参与后续的数据处理工作;若不存在,所有像斑的中心位置定位数据参与后续的数据处理工作。Organize the data columns of the x-axis and y-axis coordinate values of the same numbered image spots on the denoised electrical level images in group A and group B respectively, and perform statistical analysis to judge whether there are image spots with abnormal data; if they exist, remove them The x-axis and y-axis coordinate value data columns of the image spot, and the central position positioning data of the remaining image spots participate in the subsequent data processing; if they do not exist, the central position positioning data of all image spots participate in the subsequent data processing.

可选地,所述在所述去噪电水准图像中寻找所确定数量的像斑的具体方法为:找到去噪电水准图像中灰度值最大的点,根据像斑大小选取定心区域面积,如该区域均值大于图像背景值,认为该点为目标像斑,基于原始电水准图像x、y坐标系数据对像斑进行定心处理,并将滤波后图像的像斑位置像素值赋0,之后寻找下一个像斑至达到所确定的数量为止。Optionally, the specific method for finding the determined number of image spots in the denoised electrical level image is: find the point with the largest gray value in the denoised electrical level image, and select the area of the centering area according to the size of the image spots , if the average value of the area is greater than the background value of the image, the point is considered as the target image spot, and the image spot is centered based on the x and y coordinate system data of the original electrical level image, and the pixel value of the image spot position in the filtered image is assigned 0 , and then look for the next image spot until the determined number is reached.

可选地,所述像斑的中心位置按照以下公式进行确定,Optionally, the central position of the image spot is determined according to the following formula,

Figure BDA0002414210070000021
Figure BDA0002414210070000021

Figure BDA0002414210070000022
Figure BDA0002414210070000022

其中:

Figure BDA0002414210070000023
in:
Figure BDA0002414210070000023

x、y分别为图像上的x、y坐标,x0、y0分别为经过定心处理后的中心坐标值,I′(x,y)是经过修正的象元亮度值,I(x,y)为原始的象元亮度值,T为所取的门限值。x, y are the x, y coordinates on the image respectively, x 0 , y 0 are the center coordinate values after centering processing, I′(x, y) is the corrected pixel brightness value, I(x, y) is the original pixel brightness value, and T is the threshold value taken.

可选地,所述像斑的排序编号方法为:按照x坐标值从小到大进行排序,y值随x值进行排序,确定列数;根据每列所包含像斑数,以y坐标值从小到大进行排序。Optionally, the sorting and numbering method of the image spots is: sort according to the x coordinate value from small to large, and the y value is sorted with the x value to determine the number of columns; according to the number of image spots contained in each column, the y coordinate value is small Sort to large.

可选地,判断是否存在数据异常的像斑点的方法为:Optionally, the method for judging whether there is an abnormal data spot is as follows:

特征值m=[std_n_x_flag)]2+[data_n_v_flag)]2Eigenvalue m=[std_n_x_flag)] 2 +[data_n_v_flag)] 2 ;

其中std()为标准偏差求解,flag为转轴前数据或转轴后数据标识;Among them, std() is the solution of the standard deviation, and flag is the identification of the data before the rotation axis or the data after the rotation axis;

data_n_x_flag中符号含义为:data_n_x表示第n个像斑中心点x坐标列;The meaning of the symbol in data_n_x_flag is: data_n_x represents the x-coordinate column of the center point of the nth image spot;

data_n_y_flag中符号含义为:data_n_y表示第n个像斑中心点y坐标列;The meaning of the symbol in data_n_y_flag is: data_n_y indicates the y coordinate column of the center point of the nth image spot;

结合常规无异常数据确定阈值n;Determine the threshold n in combination with conventional non-abnormal data;

阈值n=2×(avg(m1~k)+3×std(m1~k));Threshold n=2×(avg(m 1~k )+3×std(m 1~k ));

其中avg()为均值求解,std()为标准偏差求解,k为无异常点数量,m1~k为无异常点特征值列;Among them, avg() is the solution for the mean value, std() is the solution for the standard deviation, k is the number of no abnormal points, and m 1~k is the eigenvalue column without abnormal points;

当m≥n,该数据点数据异常,将该数据点数据剔除,不参与后续的数据处理工作;When m≥n, the data of this data point is abnormal, the data of this data point will be eliminated, and will not participate in the subsequent data processing work;

当m<n,该数据点为有效点,保留并参与后续数据处理工作。When m<n, the data point is a valid point, which is reserved and participates in subsequent data processing.

本发明的一个技术效果在于,在无需调整光路及机械结构的情况下,图像处理结果能够得到较为稳定的处理结果。A technical effect of the present invention is that, without adjusting the optical path and the mechanical structure, the image processing result can obtain a relatively stable processing result.

附图说明Description of drawings

此处所说明的附图用来提供对本申请的进一步理解,构成本申请的一部分,本申请的示意性实施例及其说明用于解释本申请,并不构成对本申请的不当限定。在附图中:The drawings described here are used to provide a further understanding of the application and constitute a part of the application. The schematic embodiments and descriptions of the application are used to explain the application and do not constitute an improper limitation to the application. In the attached picture:

图1是光路偏置导致所获取像斑亮度分布不均示例1;Figure 1 is an example 1 of the uneven brightness distribution of the acquired image spots caused by the bias of the optical path;

图2是光路偏置导致所获取像斑亮度分布不均示例2;Figure 2 is an example 2 of the uneven brightness distribution of the acquired image spots caused by the bias of the optical path;

图3是光路偏置导致所获取像斑亮度分布不均示例3;Figure 3 is an example 3 of the uneven brightness distribution of the acquired image spots caused by the bias of the optical path;

图4是光路偏置导致所获取像斑亮度分布不均示例4;Figure 4 is an example 4 of the uneven brightness distribution of the acquired image spots caused by the bias of the optical path;

图5是光路偏置导致所获取像斑亮度分布不均示例5;Figure 5 is an example 5 of the uneven brightness distribution of the acquired image spots caused by the bias of the optical path;

图6是光路偏置导致所获取像斑亮度分布不均示例6;Figure 6 is an example 6 of the uneven brightness distribution of the acquired image spots caused by the bias of the optical path;

图7是光路偏置导致所获取像斑亮度分布不均示例7;Figure 7 is an example 7 of the uneven brightness distribution of the acquired image spots caused by the bias of the optical path;

图8是光路偏置导致所获取像斑亮度分布不均示例8;Figure 8 is an example 8 of the uneven brightness distribution of the acquired image spots caused by the bias of the optical path;

图9是电水准图像正确识别示例;Fig. 9 is an example of correct recognition of an electric level image;

图10是劣质电水准图像错误识别示例;Figure 10 is an example of wrong recognition of a poor-quality electric level image;

图11是2018年12月27日所获取的0221号目标的部分电水准图像数据列;Figure 11 is part of the electrical level image data series of target No. 0221 acquired on December 27, 2018;

图12是一些实施例中选取7个点的电水准数据结果;Fig. 12 is the electric level data result of choosing 7 points in some embodiments;

图13是一些实施例中选取6个点的电水准数据结果。Fig. 13 is the electric level data result of 6 points selected in some embodiments.

具体实施方式Detailed ways

以下将配合附图及实施例来详细说明本申请的实施方式,藉此对本申请如何应用技术手段来解决技术问题并达成技术功效的实现过程能充分理解并据以实施。The implementation of the present application will be described in detail below with reference to the accompanying drawings and examples, so as to fully understand and implement the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects.

本发明提供的一种电水准图像的处理方法,应用于电水准仪测量水平差,针对仪器转轴前后电水准系统终端CCD或CMOS探测器所拍摄的电水准图像序列,由于光路偏置导致像斑点阵亮度分布不均,部分像斑由于信噪比过低难以识别的劣质电水准图像。在一些实施例中,包括以下步骤:The method for processing the electric level image provided by the present invention is applied to the electric level to measure the level difference. For the electric level image sequence captured by the terminal CCD or CMOS detector of the electric level system before and after the rotating shaft of the instrument, the image spot array is caused by the bias of the optical path. The brightness distribution is uneven, and some image spots are difficult to identify due to the low signal-to-noise ratio of low-quality electric level images. In some embodiments, the following steps are included:

对单幅原始电水准图像进行图像数据的读取,确定x、y坐标系,通过目测,确定需搜寻的像斑数量。以图9所示的3×3像斑阵列为例,在这9个像斑中,只寻找其中8个较亮的像斑。Read the image data of a single original electrical level image, determine the x, y coordinate system, and determine the number of image spots to be searched through visual inspection. Taking the 3×3 image spot array shown in FIG. 9 as an example, among the 9 image spots, only 8 brighter image spots are searched.

取得转轴前和转轴后预设数量的原始电水准图像,即对天文仪器,如多功能天文经纬仪,在转轴转动前连续取得预设数量的原始电水准图像,然后在转轴转动后连续取得预设数量的原始电水准图像。转轴前预设数量的原始电水准图像所对应的去噪电水准图像为A组;转轴后预设数量的原始电水准图像所对应的去噪电水准图像为B组。在通常操作中,转轴前和转轴后分别取100张原始电水准图像,共200张原始电水准图像。Obtain a preset number of original electrical level images before and after the rotating shaft, that is, for astronomical instruments, such as multi-functional astronomical theodolite, continuously obtain a preset number of original electrical level images before the rotating shaft rotates, and then continuously obtain the preset number after the rotating shaft rotates Quantity of raw electrical level images. The denoised electrical level images corresponding to the preset number of original electrical level images before the rotation are group A; the denoised electrical level images corresponding to the preset number of original electrical level images after the rotation are group B. In normal operation, 100 original electric level images are taken before and after the rotating shaft respectively, totaling 200 original electric level images.

根据灰度值,在每一张去噪电水准图像中寻找所确定数量的像斑,并分别确定所搜寻到的像斑的中心位置。在所述去噪电水准图像中寻找所确定数量的像斑的具体方法为:找到去噪电水准图像中灰度值最大的点,根据像斑大小选取定心区域面积,如该区域均值大于图像背景值,认为该点为目标像斑,基于原始电水准图像x、y坐标系数据对像斑进行定心处理,并将滤波后图像的像斑位置像素值赋0,之后寻找下一个像斑,至达到所确定的数量为止。所述像斑的中心位置按照以下公式进行确定,According to the gray value, a determined number of image spots are searched in each denoised electric level image, and the center positions of the searched image spots are respectively determined. The specific method for finding the determined number of image spots in the denoised electrical level image is: find the point with the largest gray value in the denoised electrical level image, select the area of the centering area according to the size of the image spot, if the average value of the area is greater than Image background value, consider this point as the target image spot, center the image spot based on the x and y coordinate system data of the original electrical level image, assign the pixel value of the image spot position of the filtered image to 0, and then search for the next image spot Spots until the determined number is reached. The central position of the image spot is determined according to the following formula,

Figure BDA0002414210070000051
Figure BDA0002414210070000051

Figure BDA0002414210070000052
Figure BDA0002414210070000052

其中:

Figure BDA0002414210070000053
in:
Figure BDA0002414210070000053

x、y分别为图像上的x、y坐标,x0、y0分别为经过定心处理后的中心坐标值,I'(x,y)是经过修正的象元亮度值,I(x,y)为原始的象元亮度值,T为所取的门限值。x and y are the x and y coordinates on the image respectively, x 0 and y 0 are the center coordinate values after centering processing respectively, I'(x, y) is the corrected pixel brightness value, I(x, y) is the original pixel brightness value, and T is the threshold value taken.

在所述去噪电水准图像中按照像斑的中心位置在x、y坐标系中的坐标进行排序编号。所述像斑的排序编号方法为:按照x坐标值从小到大进行排序,y值随x值进行排序,确定列数;根据每列所包含像斑数,以y坐标值从小到大进行排序。以3×3像斑阵列为例,在这9个像斑中,只寻找其中8个较亮的像斑为例,首先以x坐标值从小到大进行排序,y值随x值进行排序。这样像斑最终的排序为:第一列;第二列;第三列;之后确定三列每列所包含的像斑数分别为:3,3,2。按照每列所包含像斑数,以y坐标值从小到大进行排序。最终得到的中心点,分布与原始图像中的3×3中的对应位置是有序对应的。In the denoised electrical level image, the ordering and numbering are carried out according to the coordinates of the center positions of the image spots in the x, y coordinate system. The sorting and numbering method of the image spots is: sort according to the x coordinate value from small to large, and the y value is sorted according to the x value, and the number of columns is determined; according to the number of image spots contained in each column, the y coordinate value is sorted from small to large . Taking the 3×3 image spot array as an example, among the 9 image spots, only 8 brighter image spots are found as an example. First, the x coordinate values are sorted from small to large, and the y values are sorted according to the x values. In this way, the final sorting of the image spots is: the first column; the second column; the third column; after that, the number of image spots contained in each of the three columns is determined to be: 3, 3, and 2 respectively. According to the number of image spots contained in each column, the y coordinate values are sorted from small to large. The distribution of the final center point corresponds to the corresponding position in the 3×3 in the original image in an orderly manner.

分别将A组和B组中的去噪电水准图像上相同编号的像斑的整理x轴、y轴坐标值的数据列,进行统计分析,判断是否存在数据异常的像斑点;若存在,剔除该像斑的x轴,y轴坐标值数据列,剩余像斑的中心位置定位数据参与后续的数据处理工作;若不存在,所有像斑的中心位置定位数据参与后续的数据处理工作。Organize the data columns of the x-axis and y-axis coordinate values of the same numbered image spots on the denoised electrical level images in group A and group B respectively, and perform statistical analysis to judge whether there are image spots with abnormal data; if they exist, remove them The x-axis and y-axis coordinate value data columns of the image spot, and the central position positioning data of the remaining image spots participate in the subsequent data processing; if they do not exist, the central position positioning data of all image spots participate in the subsequent data processing.

判断是否存在数据异常的像斑点的方法为:The method of judging whether there is an abnormal data spot is as follows:

特征值m=[std(data_n_x_flag)]2+[std(data_n_y_flag)]2Eigenvalue m=[std(data_n_x_flag)] 2 +[std(data_n_y_flag)] 2 ;

其中std()为标准偏差求解,flag为转轴前数据或转轴后数据标识,例如转轴前数据标识为zq,转轴后数据标识zh;data_n_x_flag中符号含义为:Among them, std() is the standard deviation solution, and flag is the data before or after the rotation axis. For example, the data before the rotation is marked as zq, and the data after the rotation is marked as zh; the meaning of the symbols in data_n_x_flag is:

data_n_x表示第n个像斑中心点x坐标列;data_n_y_flag中符号含义为:data_n_x indicates the x-coordinate column of the center point of the nth image spot; the meaning of the symbol in data_n_y_flag is:

data_n_y表示第n个像斑中心点y坐标列;data_n_y indicates the y coordinate column of the center point of the nth image spot;

结合常规无异常数据确定阈值n;Determine the threshold n in combination with conventional non-abnormal data;

阈值n=2×(avg(m1~k)+3×std(m1~k));Threshold n=2×(avg(m 1~k )+3×std(m 1~k ));

其中avg()为均值求解,std()为标准偏差求解,k为无异常点数量,m1~k为无异常点特征值列;Among them, avg() is the solution for the mean value, std() is the solution for the standard deviation, k is the number of no abnormal points, and m 1~k is the eigenvalue column without abnormal points;

当m≥n,该数据点数据异常,将该数据点数据剔除,不参与后续的数据处理工作;When m≥n, the data of this data point is abnormal, the data of this data point will be eliminated, and will not participate in the subsequent data processing work;

当m<n,该数据点为有效点,保留并参与后续数据处理工作。When m<n, the data point is a valid point, which is reserved and participates in subsequent data processing.

所取得的数据在后续的其他操作中,本领域技术人员根据图像情况及像斑数据点的统计学分析情况,选取所需的数据。In other follow-up operations of the obtained data, those skilled in the art select the required data according to the image situation and the statistical analysis of the image spot data points.

通过本发明的方法,能够降低某个像斑信噪比过低而导致识别错误,从而影响最终的定心结果,降低了由于光路偏置所导致的像斑亮暗不均对数据处理结果的影响,在无需调整光路及机械结构的情况下,得到较为稳定的处理结果。Through the method of the present invention, it is possible to reduce the identification error caused by the low signal-to-noise ratio of a certain image spot, thereby affecting the final centering result, and reducing the influence of uneven brightness and darkness of the image spot caused by the bias of the optical path on the data processing result. Influenced, without adjusting the optical path and mechanical structure, a relatively stable processing result can be obtained.

在一个具体的实施例1中,参见图11,采用本发明所提出的处理方法对云南天文台多功能天文经纬仪于2018年12月27日所获取的0221号目标的电水准数据进行处理(仅示意出转轴前和转轴后各19张原始电水准图像,实际数据为共转轴前和转轴后各100张原始电水准图像),根据常规无异常数据确定阈值n为30,如表1所示,In a specific embodiment 1, referring to Fig. 11, the electric level data of No. 0221 target acquired by the multifunctional astronomical theodolite of Yunnan Astronomical Observatory on December 27, 2018 is processed by using the processing method proposed by the present invention (only for illustration There are 19 original electrical level images before and after the rotating shaft, the actual data is 100 original electrical level images before and after the rotating shaft), and the threshold n is determined to be 30 according to the conventional data without abnormalities, as shown in Table 1.

表1 2018年12月27日0221号目标的电水准数据特征值统计Table 1 Statistics of the eigenvalues of the electricity level data of target No. 0221 on December 27, 2018

Figure BDA0002414210070000071
Figure BDA0002414210070000071

计算所得数据点7转轴前的特征值[std(data_7_x_zq)]2+[std(data_7_y_zq)]2为5434.9远远大于阈值30。将该点数值用于后续计算转轴前后像斑位置的变化量,如图12中选用7个点数据(数据点1至数据点7)得出的结果所示,可以看出计算所得电水准光路像斑转轴前后(Y轴方向)量度坐标位置变化量有较大的弥散,表现出明显的跳变,可以判断该点为异常数据点,不能够真实反映多功能天文经纬仪转轴前后水平差的变化,因此需要将该点数据剔除,参见图13,剔除后采用6个点的数据(数据点1-数据点6)计算转轴前后x轴、y轴坐标值变化的变化量,计算结果如图13所示,结果表明x轴、y轴两个方向变化量的弥散较小,能够较为准确地反映多功能天文经纬仪水平差的变化,计算处理结果可以进一步的参与后续的数据处理。The calculated eigenvalue [std(data_7_x_zq)] 2 + [std(data_7_y_zq)] 2 before the rotation axis of data point 7 is 5434.9, which is far greater than the threshold value of 30. The value of this point is used in the subsequent calculation of the amount of change in the position of the image spot before and after the rotation axis. As shown in the results obtained by selecting 7 point data (data point 1 to data point 7) in Figure 12, it can be seen that the calculated electric level optical path The amount of change in the measurement coordinate position before and after the rotation axis of the image spot (Y-axis direction) has a large dispersion, showing obvious jumps. It can be judged that this point is an abnormal data point, which cannot truly reflect the change of the level difference before and after the rotation axis of the multi-functional astronomical theodolite , so it is necessary to remove the data of this point, see Figure 13, after the removal, use the data of 6 points (data point 1-data point 6) to calculate the change of the x-axis and y-axis coordinate values before and after the rotation axis, and the calculation result is shown in Figure 13 As shown, the results show that the dispersion of the changes in the x-axis and y-axis directions is small, which can more accurately reflect the change of the level difference of the multifunctional astronomical theodolite, and the calculation and processing results can further participate in subsequent data processing.

如在说明书及权利要求当中使用了某些词汇来指称特定成分或方法。本领域技术人员应可理解,不同地区可能会用不同名词来称呼同一个成分。本说明书及权利要求并不以名称的差异来作为区分成分的方式。如在通篇说明书及权利要求当中所提及的“包含”为一开放式用语,故应解释成“包含但不限定于”。“大致”是指在可接收的误差范围内,本领域技术人员能够在一定误差范围内解决所述技术问题,基本达到所述技术效果。说明书后续描述为实施本申请的较佳实施方式,然所述描述乃以说明本申请的一般原则为目的,并非用以限定本申请的范围。本申请的保护范围当视所附权利要求所界定者为准。For example, certain terms are used in the description and claims to refer to specific components or methods. Those skilled in the art should understand that different regions may use different terms to refer to the same component. The description and claims do not use the difference in name as a way to distinguish components. As mentioned throughout the specification and claims, "comprising" is an open term, so it should be interpreted as "including but not limited to". "Approximately" means that within an acceptable error range, those skilled in the art can solve the technical problem within a certain error range and basically achieve the technical effect. The subsequent description of the specification is a preferred implementation mode for implementing the application, but the description is for the purpose of illustrating the general principle of the application, and is not intended to limit the scope of the application. The scope of protection of the present application should be defined by the appended claims.

还需要说明的是,术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含,从而使得包括一系列要素的商品或者系统不仅包括那些要素,而且还包括没有明确列出的其他要素,或者是还包括为这种商品或者系统所固有的要素。在没有更多限制的情况下,由语句“包括一个……”限定的要素,并不排除在包括所述要素的商品或者系统中还存在另外的相同要素。It should also be noted that the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a good or system comprising a set of elements includes not only those elements but also includes items not expressly listed. other elements of the product, or elements inherent in the commodity or system. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the article or system comprising said element.

上述说明示出并描述了发明的若干优选实施例,但如前所述,应当理解发明并非局限于本文所披露的形式,不应看作是对其他实施例的排除,而可用于各种其他组合、修改和环境,并能够在本文所述发明构想范围内,通过上述教导或相关领域的技术或知识进行改动。而本领域人员所进行的改动和变化不脱离发明的精神和范围,则都应在发明所附权利要求的保护范围内。The above description shows and describes several preferred embodiments of the invention, but as previously stated, it should be understood that the invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other embodiments. Combinations, modifications and circumstances, and can be modified within the scope of the inventive concept described herein, by the above teachings or by skill or knowledge in the relevant field. However, changes and changes made by those skilled in the art do not depart from the spirit and scope of the invention, and should be within the protection scope of the appended claims of the invention.

Claims (1)

1. A method for processing an electric level image is characterized by comprising the following steps:
acquiring a preset number of original electric water level images in front of and behind the rotating shaft;
determining an x coordinate system, a y coordinate system and the number of image spots to be searched according to the original electric level image;
carrying out median filtering processing on the obtained original electric level image to obtain a de-noised electric level image; the denoising electric level images corresponding to the original electric level images with the preset number in front of the rotating shaft are A groups; b, taking the denoising electric water level images corresponding to the original electric water level images with the preset number behind the rotating shaft as a group B;
searching the determined number of image spots in the de-noising electric level image according to the gray value, and respectively determining the central positions of the searched image spots;
sequencing and numbering the de-noised electric quasi-images according to the coordinates of the central positions of the image spots in an x coordinate system and a y coordinate system;
respectively carrying out statistical analysis on data columns of coordinate values of an x axis and a y axis of the image spots with the same number on the de-noised electric quasi-images in the group A and the group B, and judging whether image spot points with abnormal data exist or not; if the image spot exists, the x-axis coordinate value data array and the y-axis coordinate value data array of the image spot are removed, and the central position positioning data of the residual image spots participate in the subsequent data processing work; if not, the central position positioning data of all the image spots participate in the subsequent data processing work;
the specific method for finding the determined number of image spots in the denoised electrical level image is as follows: finding a point with the maximum gray value in the de-noised electric level image, selecting the area of a centering area according to the size of an image spot, if the average value of the area is larger than the background value of the image, regarding the point as a target image spot, centering the image spot based on x and y coordinate coefficients of the original electric level image, assigning 0 to the pixel value of the image spot position of the filtered image, and then searching the next image spot until the determined number is reached;
the central position of the image spot is determined according to the following formula,
Figure FDA0004108322850000011
Figure FDA0004108322850000012
wherein:
Figure FDA0004108322850000013
x and y are x and y coordinates on the image, respectively 0 、y 0 Respectively, the central coordinate values after centeringI' (x, y) is the modified pixel luminance value, I (x, y) is the original pixel luminance value, T is the threshold taken;
the image spot sequencing and numbering method comprises the following steps: sorting according to the x coordinate values from small to large, sorting the y values along with the x values, and determining the number of columns; sorting the image spots in each row from small to large according to the number of the image spots in each row;
the method for judging whether the image spots with abnormal data exist comprises the following steps:
characteristic value m- [ std (data _ n _ x _ flag)] 2 +[std(data_n_y_flag)] 2
Wherein std () is standard deviation solution, and flag is data before the rotating shaft or data identification after the rotating shaft; the symbol meaning in data _ n _ x _ flag is: data _ n _ x represents an x coordinate column of the center point of the nth image spot; the symbol meaning in data _ n _ y _ flag is: data _ n _ y represents the y coordinate column of the center point of the nth image spot;
determining a threshold value n by combining conventional abnormal-free data;
threshold n =2 × (avg (m) 1~k )+3×std(m 1~k ));
Wherein avg () is the mean solution, std () is the standard deviation solution, k is the number of outliers, m 1~k A characteristic value column without abnormal points;
when m is larger than or equal to n, the data point data is abnormal, the data point data is removed, and subsequent data processing work is not involved;
when m is less than n, the data point is a valid point and is reserved and participates in the subsequent data processing work.
CN202010186017.6A 2020-03-17 2020-03-17 Method for processing electric water level image Active CN111402307B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010186017.6A CN111402307B (en) 2020-03-17 2020-03-17 Method for processing electric water level image

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010186017.6A CN111402307B (en) 2020-03-17 2020-03-17 Method for processing electric water level image

Publications (2)

Publication Number Publication Date
CN111402307A CN111402307A (en) 2020-07-10
CN111402307B true CN111402307B (en) 2023-04-18

Family

ID=71432558

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010186017.6A Active CN111402307B (en) 2020-03-17 2020-03-17 Method for processing electric water level image

Country Status (1)

Country Link
CN (1) CN111402307B (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010054960A (en) * 2008-08-29 2010-03-11 Ricoh Co Ltd Optical scanner and image forming apparatus
CN103679167A (en) * 2013-12-18 2014-03-26 杨新锋 Method for processing CCD images
CN107358630A (en) * 2017-07-10 2017-11-17 长春工程学院 A kind of ccd image processing localization method and removable welding fume extractor
CN107515471A (en) * 2017-10-13 2017-12-26 中国科学院云南天文台 A device and method for improving the uniformity of laser output energy distribution
CN107646115A (en) * 2015-05-28 2018-01-30 脱其泰有限责任公司 Image analysis system and correlation technique

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010054960A (en) * 2008-08-29 2010-03-11 Ricoh Co Ltd Optical scanner and image forming apparatus
CN103679167A (en) * 2013-12-18 2014-03-26 杨新锋 Method for processing CCD images
CN107646115A (en) * 2015-05-28 2018-01-30 脱其泰有限责任公司 Image analysis system and correlation technique
CN107358630A (en) * 2017-07-10 2017-11-17 长春工程学院 A kind of ccd image processing localization method and removable welding fume extractor
CN107515471A (en) * 2017-10-13 2017-12-26 中国科学院云南天文台 A device and method for improving the uniformity of laser output energy distribution

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
《CCD图像数字定心算法的比较》;李展 等;《天文学报》;20090731;全文 *
摄影测量与遥感学;《测绘文摘》;20050930(第03期);全文 *

Also Published As

Publication number Publication date
CN111402307A (en) 2020-07-10

Similar Documents

Publication Publication Date Title
CN111080582B (en) Method for detecting defects of inner and outer surfaces of workpiece
CN109671078B (en) Method and device for detecting product surface image abnormity
US20080247630A1 (en) Defect inspecting apparatus and defect-inspecting method
CN110766095B (en) Defect detection method based on image gray level features
CN109934839A (en) A Vision-Based Workpiece Detection Method
CN111047586B (en) A Pixel Equivalent Measurement Method Based on Machine Vision
CN112613429A (en) Machine vision-based reading method suitable for multi-view image pointer instrument
KR101782363B1 (en) Vision inspection method based on learning data
CN104103543B (en) Wafer defect dimension correction method
CN115147350A (en) A method for dimension detection of clamp parts based on machine vision
CN112085708A (en) Method and equipment for detecting defects of straight line edge in product outer contour
CN115452845B (en) LED screen surface damage detection method based on machine vision
CN113614774A (en) Method and system for defect detection in image data of target coating
CN111145198B (en) Non-cooperative target motion estimation method based on rapid corner detection
CN111402307B (en) Method for processing electric water level image
KR20000034922A (en) Removal of noise from a signal obtained with an imaging system
CN112819842B (en) Workpiece contour curve fitting method, device and medium suitable for workpiece quality inspection
CN114387232A (en) Wafer center positioning, wafer gap positioning and wafer positioning calibration method
CN112950598A (en) Method, device and equipment for detecting flaw of workpiece and storage medium
CN116908185A (en) Method and device for detecting appearance defects of article, electronic equipment and storage medium
EP3997664A1 (en) Lens calibration method for digital imaging apparatus
CN107563991A (en) The extraction of piece surface fracture laser striation and matching process
CN110874837A (en) Automatic defect detection method based on local feature distribution
JP2001266126A (en) Method and device for detecting defect and method of manufacturing for mask
CN115760860A (en) Multi-type workpiece dimension visual measurement method based on DXF file import

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant