Target, target center positioning method, and target calibration plate calibration method

Abstract

This application belongs to the field of vision system calibration technology, specifically relating to a target, a target center localization method, and a target calibration plate calibration method. The target provided in this application includes a circular positioning area and an annular recognition area; the annular recognition area surrounds the circular positioning area, which has a triangular mesh, comprising triangles or sectors symmetrically arranged around its center; the target center point is located at the centrally symmetrical point of the triangles or sectors; an annular coding band is provided inside the annular recognition area, comprising multiple arc-shaped coding strips; these arc-shaped coding strips are centrally symmetrical around the target center point. The target center of this application is the intersection point of the legs of multiple triangles in the triangular mesh. Even if the target is partially occluded, or the target center is occluded, the target center point can still be accurately located by connecting the intersection points of the legs of the unoccluded triangles in the triangular mesh.

Description

Technical Field

[0001] This application belongs to the field of vision system calibration technology, and in particular relates to a target, a target center localization method, and a target calibration plate calibration method. Background Technology

[0002] Vision system calibration, also known as camera calibration, aims to obtain camera parameters. These parameters include: intrinsic camera parameters, extrinsic camera parameters, and distortion parameters. Intrinsic camera parameters refer to the camera's imaging characteristics, such as focal length. Extrinsic camera parameters refer to the camera's position and orientation relative to the world coordinate system. Distortion parameters are used to correct image distortion caused by the camera lens.

[0003] Photogrammetric calibration is a common method for camera calibration. This method involves acquiring images of a calibration board using the camera, obtaining the two-dimensional image coordinates and corresponding three-dimensional world coordinates of known feature points on the board, and then solving for the camera parameters.

[0004] However, photogrammetric calibration methods are easily affected by harsh industrial production environments, such as unstable lighting conditions, complex backgrounds, and target occlusion. Specifically, the traditional target center is circular. When the camera captures data from different angles, the circular center becomes an ellipse of varying shapes, resulting in inaccurate center point positioning. If the elliptical target center is partially occluded, the difficulty of center point positioning increases further. Due to unstable lighting conditions, the target center is prone to overexposure or underexposure, and increased noise under complex background conditions can also lead to blurred target centers, making them unrecognizable and unpositionable. This is detrimental to high-precision photogrammetric calibration using industrial cameras. Summary of the Invention

[0005] One of the purposes of this application is to provide a target that can be accurately identified and located under the influence of harsh industrial production environments.

[0006] One of the purposes of this application is to provide a target center localization method that can accurately identify and locate the target center in complex spatial lighting environments.

[0007] One of the purposes of this application is to provide a calibration method for a target calibration plate that enables accurate calibration of the target calibration plate in complex lighting environments.

[0008] To achieve the above and other related objectives, this application provides a target comprising a circular positioning area and an annular recognition area; the annular recognition area is disposed around the circular positioning area.

[0009] The circular positioning area has a triangular grid, which includes triangles or sectors that are symmetrical about the center; the center point of the target is located at the center symmetrical point of the triangles or sectors.

[0010] The annular recognition area is provided with an annular coding band, which includes multiple arc-shaped coding strips; the multiple arc-shaped coding strips are centrally symmetrical around the center point of the target.

[0011] This application also provides a target center localization method, including the following steps:

[0012] S1, perform image acquisition on the target as described above to obtain a triangular mesh image within the circular positioning area of ​​the target;

[0013] S2, in the triangular mesh image, select a corner point and preset the initial position x of the corner point. 0 ;

[0014] S3, obtain the sampling points Δ near the corner points in the triangular mesh image. i Intensity value; sampling point Δ i The coordinates are (u i ,v i And the sampling point Δ is represented by the intensity surface function f(x). i The intensity value;

[0015] S4, Constructing a polynomial function Used to fit the intensity surface function f(x); polynomial function for:

[0016] Wherein, c1~c 10 The coefficients of the polynomial,

[0017] S5 makes the corner points x in the triangular mesh image * Δ at various nearby sampling points i Within the area, the following formula is satisfied.

[0018]

[0019] Where c = [c1, c2, ..., c 10 ] T Let N be the coefficient vector of a polynomial function, and N be a set of coefficients at the corner points x. * n sampling points Δ i =(u i ,v i The set of x; t Based on the corner point x * A sequence is constructed with the current position as the center; the optimal solution is found for the coefficient vector c of the polynomial function in the above formula;

[0020] S6, Substitute the optimal solution for c into the polynomial function. In the middle, solve for the first derivative. x * The value of x is obtained by solving for x. * The value is used as the corner point x in the triangular mesh image in step S3. * The current location;

[0021] S7, repeat steps S3 to S6 until corner point x * The value of x varies within a predetermined threshold, thus determining the corner point x. * The target center.

[0022] This application also provides a method for calibrating a target calibration plate, comprising the following steps:

[0023] Obtain a target calibration plate, wherein the target calibration plate is provided with the target as described above;

[0024] Obtain the physical dimensions of the target calibration plate;

[0025] Image acquisition is performed on the target calibration plate;

[0026] The target center is located using the target center localization method described above;

[0027] Establish a world coordinate system using the plane where the target calibration plate is located, and obtain the coordinates of the target center in the world coordinate system.

[0028] Substitute the coordinates of the target center in the world coordinate system into the camera's perspective imaging model to calculate the camera's initial intrinsic and extrinsic parameters and distortion coefficients, thus completing the calibration of the target calibration plate.

[0029] This application has at least the following beneficial effects:

[0030] Compared to the circular center of the target in the background technology, when the camera captures data from different angles, the circular center of the target becomes an ellipse of different shapes, resulting in inaccurate center point positioning. However, the target center of this application is the intersection of the waists of multiple triangles in a triangular mesh. Even if the target is partially obscured or the target center is obscured, the target center point can still be accurately located by connecting the waists of the unobscured triangles in the triangular mesh.

[0031] Compared to the circular center of the target in the background technology, the target center is prone to overexposure or underexposure due to unstable lighting conditions, and the increased noise under complex background conditions can also lead to blurring of the target center. The target center of the application is the corner point of the triangular mesh. By detecting the corner point of the triangular mesh, the target center point can be accurately located.

[0032] The target center localization method is adopted, which involves fitting the monkey saddle surface and specifically constructing a polynomial function. The intensity surface function f(x) is used to fit the target's triangular mesh to accurately detect the corner points, thereby precisely locating the center position of the encoded target. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a schematic diagram of a target according to an embodiment of this application;

[0035] Figure 2 This is a schematic diagram of a calibration plate including a target array according to an embodiment of this application;

[0036] Figure 3 This is a schematic diagram illustrating a calibration board photographed from different shooting angles according to an embodiment of this application; Detailed Implementation

[0037] The following specific embodiments illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this application can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0038] In the description of this application, it should be noted that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the accompanying drawings and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this application. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0039] This application provides a target, which is circular in shape and includes a circular positioning area and an annular recognition area. The annular recognition area is set around the circular positioning area. The circular positioning area is used to assist in locating the center point of the target, and the annular recognition area is used to carry the target's encoded information.

[0040] Reference Figure 1 The circular positioning area is a white circular pattern, with a triangular grid consisting of three black triangles in the center. These black triangles share a common vertex, the target's center point, and are centrally symmetrical about this point. The target's center point can be located at the center of the circular positioning area. Alternatively, the black triangles in the triangular grid can be replaced with black sectors.

[0041] Triangular mesh images are captured by a camera in complex environments. Due to variations in shooting angle and lighting conditions, triangular meshes of the same color exhibit inconsistent light intensity (hereinafter referred to as intensity) within the triangular mesh image. For example, areas closer to the light source are brighter, while areas farther from the light source are less intense. This intensity distribution of the triangular mesh can be represented by a monkey saddle surface. The monkey saddle surface is a cubic surface with three valley-like depressions, each corresponding to a specific point on the surface. Figure 1 There are three black triangles in the image. Points where two or more edges intersect in a triangular mesh image are called corner points. Corner points are also intensity extrema, including intensity maxima and intensity minima. The target center point is a corner point.

[0042] Compared to the circular center of the target in the background technology, when the camera captures data from different angles, the circular center of the target becomes an ellipse of different shapes, resulting in inaccurate center point positioning. However, the target center of this application is the intersection of the waists of multiple triangles in a triangular mesh. Even if the target is partially obscured or the target center is obscured, the target center point can still be accurately located by connecting the waists of the unobscured triangles in the triangular mesh.

[0043] Compared to the circular center of the target in the background technology, the target center is prone to overexposure or underexposure due to unstable lighting conditions, and the increased noise under complex background conditions can also lead to blurring of the target center. The target center of the application is the corner point of the triangular mesh. By detecting the corner point of the triangular mesh, the target center point can be accurately located.

[0044] The annular recognition area is a black circular pattern, within which is an annular coding strip. The ratio of the outer diameter of the annular coding strip to the outer diameter of the white circular pattern in the circular positioning area is a preset value. This preset value allows for accurate location of the annular coding strip after the white circular pattern in the positioning area has been determined. The annular coding strip consists of multiple arc-shaped coding bars. These arc-shaped coding bars are centrally symmetrical around the center point of the target. The color distribution of the arc-shaped coding bars differs from the color distribution of other parts of the annular recognition area; for example, the arc-shaped coding bars may be a black and white pattern, while the rest of the annular recognition area may be a monochrome pattern.

[0045] The target coding information can be carried by the position and arrangement of multiple arc-shaped coding bars.

[0046] Specifically, the annular coding band can be divided into 8-14 equal parts, meaning the number of coding positions in the target's annular coding band can be 8-14. Considering the relationship between the number of coding equal parts and the coding capacity, the target's coding bit length is preferably 12 bits, denoted by n.

[0047] When n is 12, the number of effective targets is calculated using the following formula: in Let d represent the Euler totient function, and let d represent each positive integer from 1 to n. The number of effective targets N is 351.

[0048] Since the coding strips are circular, they can be encoded in either clockwise or counterclockwise order. To ensure the uniqueness of the encoding, decoding is also performed in the same order. Binary encoding can be used, where the arc-shaped coding strips are white and represented as "1", and the portion of the circular coding strip not occupied by the arc-shaped coding strips is black and represented as "0". Different positions and arrangements of the arc-shaped coding strips can form different binary sequences.

[0049] To ensure accurate decoding even after target rotation, the binary sequence represented by the target is converted to a decimal value, and the smallest decimal value is selected as the target's encoding value. For example, when n is 12, there are 12 different binary sequences after rotation, which, when converted to decimal, correspond to 12 different decimal values. The smallest decimal value is selected as the target's encoding value. This encoding process creates a calibration board composed of multiple different targets, as shown below. Figure 2 As shown.

[0050] This embodiment provides a target center localization method, including the following steps:

[0051] S1, acquire images of a circular target containing a triangular mesh image to obtain a triangular mesh image within the circular positioning area of ​​the target;

[0052] Step S1 also includes image preprocessing of the captured circular target.

[0053] When the circular target is a black and white target, an adaptive maximum inter-class variance method (such as the Otsu algorithm) can be used to perform image binarization segmentation to reduce the probability of incorrect segmentation of different color regions of the circular target.

[0054] When the circular target is a colored target, grayscale processing can be used to turn the colored target into a grayscale image. Then, image binarization is used to divide the different regions of the target. Finally, line scanning target extraction is used to mark the different regions of the target image on the original color image.

[0055] Regardless of whether the circular target is black and white or colored, image denoising and grayscale stretching preprocessing methods can be used. For example, a Gaussian filter can be used for image denoising. Grayscale stretching can enhance image contrast, making the distinction between the target and the background more obvious.

[0056] S2, in the triangular mesh image, the corner point x * The initial position is preset to x. 0 ;

[0057] Where, corner point x * Let x be an extreme point of intensity in the triangular mesh image; the intensity here can be the gray level of a single-color image or the intensity of one of the colors in a color image; the initial position x 0 This can be obtained by finding the center of the circular target;

[0058] The center of a circular target can be obtained by multiple concentric circles. These concentric circles include the outer circle of the circular positioning area and the outer circle of the annular recognition area. Specifically, a formula is used...

[0059]

[0060] Where r1 is the radius of the outer circle of the annular recognition area, and r2 is the radius of the outer circle of the circular positioning area; The outer circle of the annular recognition area is the center. The outer center of the circular positioning area; (u C ,v C K is the center of the circular target. u K v are the coefficients of the equation.

[0061] Combining the above formulas, we obtain an approximate solution for the coordinates of the center of the circular target:

[0062]

[0063] in,

[0064] S3, Obtain the corner points x in the triangular mesh image. * Δ at various nearby sampling points i The intensity value, sampling point Δ i The coordinates are (u i ,v i And the sampling point Δ is represented by the intensity surface function f(x). i The intensity value;

[0065] When sampling point Δ i coordinates (u) i ,v iWhen the coordinates of the sampling point Δ are non-integer coordinates, i.e., sub-pixel coordinates, bilinear interpolation can be used to approximate the sampling point Δ. i The intensity was sampled.

[0066] S4, Constructing a polynomial function Used to fit the intensity surface function f(x); polynomial function It can be represented as:

[0067] Wherein, c1~c 10 The coefficients of the polynomial,

[0068] Make the corner points x in the triangular mesh image * Δ at various nearby sampling points i Within the area, the following formula is satisfied.

[0069]

[0070] Where c = [c1, c2, ..., c 10 ] T Let N be the coefficient vector of a polynomial function, and N be a set of coefficients at the corner points x. * n sampling points Δ i =(u i ,v i A set of )

[0071] S5, find the optimal solution for the value of c in the following formula:

[0072]

[0073] Where, x t Based on the corner point x * The current position is the center of a set of sequences, if the current estimated corner point x * The position is x 0 Then x t It is x 0 The current position is the center of a set of sequences.

[0074] The specific steps for finding the optimal solution for the value of c include constructing a linear system Ac-b=0, and using the least squares method to find the optimal solution for the value of c, where the matrix... b = [f(x) t +Δ1),…,f(x t +Δ n )] T , where (u i ,v i ) represents the sampling point Δ i The coordinates.

[0075] S6, Substitute the optimal solution for c into the polynomial function. In the middle, solve for the first derivative. x * The value of x is obtained by solving for x. * The value is used as the corner point x in the triangular mesh image in step S3. * The current location;

[0076] S7, repeat steps S3 to S6 until corner point x * The value of x varies within a predetermined threshold, thus determining the corner point x. * The target center.

[0077] In order to quickly calculate the different corner points x in step S7 * The corresponding value of c is determined by using matrix A as a fixed value, i.e., different corner points x * Each selected sampling point Δ i The coordinates are the same. Therefore, the fitting problem in repeated steps S3 to S6 can be solved by constructing the same equation AC = B, where matrix B is obtained by connecting all m corner points x. * The matrix B is filled with the corresponding vectors, meaning that matrix B is an m×n matrix, and each column represents a corner point x. * The intensity values ​​of n nearby sampling points. When the number of sampling points n≥10, the equation AC=B is an overdetermined problem, meaning the number of equations exceeds the number of unknowns. The least squares method can be used to solve it. Only one calculation is needed for matrix M=(A T A) -1 A T Then, we can obtain the x-axis of each corner point using C = M·B. * The corresponding value of c.

[0078] In step S6, for solving the problem that satisfies the first derivative x * The value must also satisfy the second derivative. At this moment, the corner point x * A saddle point is a point that is both a maximum in one direction and a minimum in another. Since the triangular mesh pattern in this application is a curved surface, and its intensity distribution resembles the shape of a monkey saddle surface, the target center can be considered to be located at a saddle point on the monkey saddle surface. Therefore, corner points that do not conform to the saddle point rule are excluded, i.e., points that do not satisfy the second derivative rule are excluded. The corner point x * Substitute the values ​​into the calculation of step S7 to improve the computational efficiency in step S7.

[0079] Second derivative The specific formula is as follows:

[0080]

[0081] Where, x d This is the degeneracy critical point, which is used to calculate the location of the saddle point.

[0082] Since the above equation is for an overdetermined linear system, meaning the number of equations in the linear system is greater than the number of unknowns, the optimal solution in the linear system can be found by using the least squares method.

[0083] The degradation critical point x can be obtained by solving the above formula. d Combined with the formula x t+1 =x t +x d The degradation critical point x is obtained d Corrected corner point x t+1 Then use the corner point x t+1 Replace the original corner point x in step S5 t By iterating through steps S5 to S7, the corner point x that meets the requirements is obtained. * .

[0084] To detect all corner points on a triangular mesh image, we can also use a conditional expression... To find the corner point x * For satisfying The corner point x * The values ​​can be substituted into steps S3 to S7 to perform calculations to determine whether it is the required corner point x. * Since non-compliant corner points x can be eliminated in steps S3 to S6. * This eliminates the need for iterative calculations in S7, thus allowing for the rapid acquisition of the required corner point x. * .

[0085] The specific decoding process using the above-mentioned target is as follows:

[0086] 1) Use the target center localization method described above to locate the target center;

[0087] 2) Determine the target's location and identification areas based on the target's center;

[0088] The position of the annular coding strip can be determined by defining a preset ratio between the outer diameter of the annular coding strip and the outer diameter of the white circular pattern in the positioning area, after determining the white circular pattern. For example, a rectangular search box can be used for searching, with the longer side of the rectangular search box set to 5 times the outer diameter of the white circular pattern, excluding annular coding strips that are outside the range of the rectangular search box.

[0089] 3) Due to the influence of acquisition angle and ambient light, the ring-shaped coding band of the acquired target recognition area is a discontinuous ellipse. Therefore, it is necessary to perform an affine transformation on the discontinuous elliptical ring-shaped coding band to obtain a discontinuous circular ring-shaped coding band before decoding.

[0090] The above decoding method is easy to identify in image processing under complex lighting conditions, and has low decoding time complexity. It can meet the requirements of high-precision calibration and measurement ecosystem in complex visual task scenarios at various scales.

[0091] This embodiment provides a calibration method for a target calibration plate, including the following steps:

[0092] Reference Figure 3 A calibration plate with a target pattern is obtained; the target on the calibration plate has a ring recognition area and a circular positioning area located in the center of the ring recognition area, and the center of the circular positioning area has a triangular grid image symmetrical along the center; the calibration plate can be printed with high precision with an accuracy of ±0.01mm.

[0093] A camera is used to capture images of the calibration board from different angles. A predetermined number of calibration board images must be obtained, for example, no fewer than 30 sets. Calibration board images that do not fully display the target pattern are excluded and not included in the count.

[0094] The target center is located using the target center localization method described above. A world coordinate system is established using the plane where the calibration plate is located. Since the physical dimensions of the calibration plate are known, the coordinates P(X) of the target center point in the world coordinate system can be obtained. W ,Y W Z W ), and set the coordinates P(X) W ,Y W Z W Substitute the camera's perspective imaging model into the calculation of the camera's initial intrinsic and extrinsic parameters and distortion coefficients to complete the calibration of the target calibration plate.

[0095] Specifically, since the plane where the calibration plate is located is the Z-plane, i.e., Z... W =0, substituting into the camera perspective imaging model, we get:

[0096]

[0097] Where D is the camera intrinsic parameter matrix and s is the scale factor.

[0098] make get Where H is the homography matrix, representing the mapping relationship between 2D points on the calibration plate image plane and 2D points on the calibration plate. Substituting the camera's perspective imaging model, we can obtain the homography matrix H.

[0099]

[0100] The least-squares solution of the homography matrix H can be obtained by using the Singular Value Decomposition (SVD) method, which allows us to calculate the camera's initial intrinsic parameters, extrinsic parameters, and distortion coefficients, thereby completing the calibration of the target calibration plate.

[0101] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.

Claims

1. A target, characterized in that, It includes a circular positioning area and an annular recognition area; the annular recognition area is arranged around the circular positioning area. The circular positioning area has a triangular grid, which includes triangles or sectors that are symmetrical about the center; the center point of the target is located at the center symmetrical point of the triangles or sectors. The annular recognition area is provided with an annular coding band, which includes multiple arc-shaped coding strips; the multiple arc-shaped coding strips are centrally symmetrical around the center point of the target.

2. A target center localization method, characterized in that, Includes the following steps: S1, Image acquisition is performed on the target as described in claim 1 to obtain a triangular mesh image within the circular positioning area of ​​the target; S2, in the triangular mesh image, an angle point is selected, and an initial position x of the angle point is preset 0 ; S3, obtaining intensity values of each sampling point Δ near the corner point in the triangular mesh image i The coordinates of sampling point Δ i are (u i ,v i ), and the intensity values of sampling point Δ i are represented by an intensity surface function f(x) S4, Constructing a polynomial function Used to fit the intensity surface function f(x); polynomial function for: Wherein, c1~c 10 The coefficients of the polynomial, S5, make the corner point x * near each sampling point Δ i region, satisfy the following formula Where c = [c1, c2, ..., c 10 ] T Let N be the coefficient vector of a polynomial function, and N be a set of coefficients at the corner points x. * n sampling points Δ i =(u i ,v i The set of x; t Based on the corner point x * A sequence is constructed with the current position as the center; the optimal solution is found for the coefficient vector c of the polynomial function in the above formula; S6, substitute the optimal solution for the value of c into the polynomial function. In the middle, solve for the first derivative. x * The value of x is obtained by solving for x. * The value is used as the corner point x in the triangular mesh image in step S3. * The current location; S7, repeating steps S3 to S6 until the corner point x * has a value that varies within a range less than a predetermined threshold, and further determining that the corner point x * is the target center.

3. The target center localization method according to claim 2, characterized in that, The target is circular. In step S2, the selected corner point is the center of the circular target.

4. The target center localization method according to claim 3, characterized in that, The center of the circular target is calculated using multiple concentric circles, including the outer circle of the circular positioning area and the outer circle of the annular recognition area.

5. The target center localization method according to claim 2, characterized in that, In step S3, when the coordinates (u i ,v i ) of the sampling point Δ i are non-integer coordinate points, the intensity of the sampling point Δ i is sampled by using a bilinear interpolation method.

6. The target center localization method according to claim 2, characterized in that, In step S5, the specific steps for finding the optimal solution for the value of c include constructing a linear system Ac-b=0, and using the least squares method to find the optimal solution for the value of c, wherein the matrix... b = [f(x) t +Δ1),…,f(x t +Δ n )] T , where (u i ,v i ) represents the sampling point Δ i The coordinates.

7. The target center localization method according to claim 6, characterized in that, In step S7, the matrix A used is a fixed value.

8. The target center localization method according to claim 2, characterized in that, In step S6, only those satisfying the second derivative... The corner point x * Substitute this into step S7 for calculation. Wherein, the second derivative The specific formula is as follows: Where, x d This is the critical point of degradation.

9. The target center localization method according to claim 8, characterized in that, Degradation critical point x d Substitute into the expression x t+1 =x t +x d Get the corner point x t+1 Then use the corner point x t+1 Replace the original corner point x in step S5 t After steps S5 to S7, the corner point x that meets the requirements is obtained. * .

10. A calibration method for a target calibration plate, characterized in that, Includes the following steps: Obtain a target calibration plate, wherein the target calibration plate is provided with the target as described in claim 1; Obtain the physical dimensions of the target calibration plate; Image acquisition is performed on the target calibration plate; The target center is located using the target center localization method as described in claims 2 to 9; Establish a world coordinate system using the plane where the target calibration plate is located, and obtain the coordinates of the target center in the world coordinate system. Substitute the coordinates of the target center in the world coordinate system into the camera's perspective imaging model to calculate the camera's initial intrinsic and extrinsic parameters and distortion coefficients, thus completing the calibration of the target calibration plate.

Images