KR102258578B1 - System for data analysis and method for analysing digital image correlation using adaptive subset size - Google Patents

Abstract

The present invention relates to a data analysis system and a digital image correlation analysis method using the adaptive subset size using the same. The digital image correlation analysis method performed by the data analysis system for analyzing the deformation of an object includes: a) a speckle pattern One or more reference images are acquired at predetermined time intervals on the surface of the object on which the (speckle pattern) is formed, and the number of cases in which two images are selected without overlap using the reference images is calculated, and any of the two images Setting a seed point at a predetermined interval in a region of interest set on one reference image, and setting a reference subset of a predetermined size around each seed point; b) The ROI of the target image is divided into a target subset corresponding to the reference subset, and a matching distance between the matched subset and the reference subset is calculated by performing a subset matching algorithm between the target subset and the reference subset, and the matching Generating a convergence map by repeatedly determining a convergence size at a point where the distance converges for each seed point; c) calculating a plurality of convergence maps according to the calculated number of cases, and determining an adaptive subset size by adding a standard deviation to the average of the convergence sizes for each seed point for the calculated convergence map; And d) performing a Digital Image Correlation (DIC) analysis between the reference image and the target image using the adaptive subset size to track the displacement of the subset caused by the deformation of the object, wherein the The subset matching algorithm calculates a cross-correlation between the reference subset and a target image while increasing the size of the reference subset step by step and expresses it as a correlation coefficient, and a target subset in which a location of a pixel having a maximum correlation coefficient matches the reference subset. It is judged by the location of.

Description

Digital image correlation analysis method using data analysis system and adaptive subset size {SYSTEM FOR DATA ANALYSIS AND METHOD FOR ANALYSING DIGITAL IMAGE CORRELATION USING ADAPTIVE SUBSET SIZE}

The present invention relates to a data analysis system capable of automatically optimizing a subset size for digital image correlation analysis, and a digital image correlation analysis method using an adaptive subset size.

Recently, as a non-destructive inspection method for measuring materials and cracks of a target structure, digital image correlation (DIC) is expanding. DIC is a technique that evaluates the deformation and motion of the entire area of the object to be evaluated in a non-contact method with few restrictions on the surrounding environment, and uses a digital image processing technique to obtain the gradient field of displacement and deformation.

Specifically, DIC is to evaluate the deformation on the surface of an object by comparing the change in gray intensity with respect to the measurement surface in an image acquired as an image. For this, since the change information of the gray intensity must be given so that the deformation of the object surface to be measured can sufficiently reflect the evaluation algorithm, an artificial random pattern, that is, a speckle pattern, is generally formed in the region to be analyzed of the object. . The speckle pattern is formed by painting arbitrary large and small shapes on the analysis evaluation surface using an air spray gun.

DIC's evaluation algorithms include techniques using SSSIG, subset entropy, and co-occurrence matrix.

The Sum of square of subset intensity gradient (SSSIG) technique calculates the speckle pattern gradients within the subset and sets a random threshold to determine the region of interest. , ROI).

The subset entropy technique is a method of determining a uniform single subset size within an ROI by calculating entropy with the sum of the absolute values of the deviations of gray intensity of eight neighboring pixels around a set reference point.

The gray-level co-occurrence matrix method determines a uniform single subset size within the ROI by calculating a parameter called Offset using the combination of the gray intensity of pixels of two subsets at a specific distance in the image. That's how to do it.

As described above, the DIC evaluation algorithms according to the prior art commonly evaluate a speckle pattern at a specific position in an image to calculate a single subset size to be applied to all regions at once. In particular, since it is assumed that the speckle pattern is uniformly distributed in all areas, there is a limitation in that an error may occur even if the DIC is performed by applying the calculated subset size when the speckle pattern is spatially biased. In addition, there is a problem in that the subjective intervention of the user is essential in setting the threshold value for determining the subset size, and thus it acts as a factor that lowers the reliability of the result.

US Patent Publication No. 20160154926 (Name of invention: DIGITAL IMAGE CORRELATION SYSTEM AND METHOD, digital image correlation system and method)

In order to solve the above-described problem, the present invention calculates different adaptive subset sizes suitable for digital image correlation analysis at every position in the ROI regardless of the degree of deflection of the speckle pattern according to an embodiment of the present invention. The objective is to optimize the size of a subset required for each location where digital image correlation analysis is performed without subjective intervention, and to increase the accuracy of digital image correlation analysis for minute deformation.

However, the technical problem to be achieved by the present embodiment is not limited to the technical problem as described above, and other technical problems may exist.

In the digital image correlation analysis method using the adaptive subset size performed by the data analysis system for analyzing the deformation of the object according to an embodiment of the present invention as a technical means for achieving the above technical problem, a) Acquire one or more reference images at predetermined time intervals on the surface of an object on which a speckle pattern is formed, calculate the number of cases in which two images are selected without overlap using the reference images, and the two images Setting a seed point at a predetermined interval in a region of interest set on any one of the reference images, and setting a reference subset of a predetermined size around each seed point; b) The ROI of the target image is divided into a target subset corresponding to the reference subset, and a matching distance between the matched subset and the reference subset is calculated by performing a subset matching algorithm between the target subset and the reference subset, and the matching Generating a convergence map by repeatedly determining a convergence size at a point where the distance converges for each seed point; c) calculating a plurality of convergence maps according to the calculated number of cases, and determining an adaptive subset size by adding a standard deviation to the average of the convergence sizes for each seed point for the calculated convergence map; And d) performing a Digital Image Correlation (DIC) analysis between the reference image and the target image using the adaptive subset size to track the displacement of the subset caused by the deformation of the object, wherein the The subset matching algorithm calculates a cross-correlation between the reference subset and a target image while increasing the size of the reference subset step by step and expresses it as a correlation coefficient, and a target subset in which a location of a pixel having a maximum correlation coefficient matches the reference subset. It is judged by the location of.

In addition, the data analysis system for analyzing the deformation of an object according to another embodiment of the present invention is a digital image correlation using an adaptive subset size for each position for all regions within a region of interest (ROI) of the object. A memory in which a program for performing the analysis method is recorded; And a processor for executing the program, wherein the processor acquires one or more images at predetermined time intervals from the surface of the object on which the speckle pattern is formed by executing the program to obtain a reference image Set, set a seed point at a preset interval in the region of interest set for each of the reference images, set a reference subset of a preset size around each seed point, and set the region of interest of the target image. Dividing into target subsets corresponding to the reference subset, and calculating a matching distance between the matched subset and the reference subset by performing a subset matching algorithm between the target subset and the reference subset, and converging at a point in time when the matching distance converges The size is repeatedly determined for each seed point to generate a convergence map, and for the convergence map, an adaptive subset size is determined by adding a standard deviation to the average of the convergence size for each seed point, and the adaptive A digital image correlation (DIC) analysis between the reference image and the target image is performed using the subset size to track the displacement of the subset caused by the deformation of the object, and the subset matching algorithm includes the size of the reference subset. The correlation between the reference subset and the target image is calculated and expressed as a correlation coefficient, and the location of the pixel having the maximum correlation coefficient is determined as the location of the target subset matching the reference subset.

According to the above-described problem solving means of the present invention, it is possible to automatically calculate the optimized subset size required for DIC analysis in all areas within the ROI of the object, thereby being applicable to speckle patterns of various shapes and sizes, and It can be applied irrespective of the shape and material of.

In addition, since the present invention can automatically calculate and optimize the adaptive subset size without user intervention, DIC analysis is possible based on high reliability, and random noise is calculated by calculating the adaptive subset size using a plurality of reference images. It is possible to increase the precision and accuracy of DIC analysis by reducing the error caused by and preventing the subset size from being excessively large or small.

1 is a diagram showing the configuration of a data analysis system for digital image correlation analysis according to an embodiment of the present invention.
FIG. 2 is a block diagram illustrating the control device of FIG. 1.
3 is a flowchart illustrating a digital image correlation analysis method using an adaptive subset size according to an embodiment of the present invention.
4 is a diagram illustrating a process of setting initial parameters in the ROI of FIG. 3.
5 is a diagram illustrating a process of setting the convergence map of FIG. 3.
6 is a diagram illustrating a process of determining the adaptive subset size of FIG. 3.
FIG. 7 is a diagram illustrating a process of performing DIC analysis by applying the adaptive subset size of FIG. 6.
8 is a diagram illustrating an experiment environment for verifying DIC analysis of a data analysis system according to an embodiment of the present invention.
9 is a diagram illustrating an amount of output for each size and a distribution for each location of an adaptive subset size calculated by the data analysis system of FIG. 8.
10 is a diagram illustrating a verification result of an adaptive subset size using a test image.
11 is a diagram illustrating a seed point randomly selected within an ROI.
12 is a diagram illustrating a single subset size calculated using the SSSIG technique at each seed point of FIG. 11.
13 is a diagram illustrating a verification result of a single subset size calculated using the SSSIG technique of FIG. 12.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art can easily implement the present invention. However, the present invention may be implemented in various different forms and is not limited to the embodiments described herein. In the drawings, parts irrelevant to the description are omitted in order to clearly describe the present invention, and similar reference numerals are attached to similar parts throughout the specification.

Throughout the specification, when a part is said to be "connected" with another part, this includes not only "directly connected" but also "electrically connected" with another element interposed therebetween. . In addition, when a part "includes" a certain component, it means that other components may be further included, and one or more other features, not excluding other components, unless specifically stated to the contrary. It is to be understood that it does not preclude the presence or addition of any number, step, action, component, part, or combination thereof.

In the present specification, the'terminal' may be a wireless communication device with guaranteed portability and mobility, and may be any kind of handheld-based wireless communication device such as a smart phone, a tablet PC, or a notebook. In addition, the'terminal' may be a wired communication device such as a PC that can access other terminals or servers through a network. In addition, a network refers to a connection structure in which information exchange between nodes such as terminals and servers is possible, and includes a local area network (LAN), a wide area network (WAN), and the Internet (WWW). : World Wide Web), wired and wireless data networks, telephone networks, wired and wireless television networks, etc.

Examples of wireless data networks include 3G, 4G, 5G, 3GPP (3rd Generation Partnership Project), LTE (Long Term Evolution), WIMAX (World Interoperability for Microwave Access), Wi-Fi, Bluetooth communication, infrared communication, and ultrasound. Communication, Visible Light Communication (VLC), LiFi, and the like are included, but are not limited thereto.

The following examples are detailed descriptions for aiding understanding of the present invention, and do not limit the scope of the present invention. Accordingly, the invention of the same scope performing the same function as the present invention will also belong to the scope of the present invention.

Hereinafter, an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram showing the configuration of a data analysis system for digital image correlation analysis according to an embodiment of the present invention, and FIG. 2 is a block diagram illustrating a control device of FIG. 1.

Referring to FIG. 1, the data analysis system acquires one or more images of the surface of the object from the camera 10 and the camera 10 for photographing the object subjected to the speckle pattern, and digital image correlation using the acquired images. It includes a control device 100 that performs (Digital Image Correlation, DIC) analysis. In this case, the control device 100 acquires a plurality of images with the same camera position and setting at regular time intervals.

As shown in FIG. 2, the control device 100 includes a communication module 110, a memory 120, a processor 130, and a database 140.

In detail, the communication module 110 is synchronized with the camera 10 to provide a communication interface required to acquire an image of a real-time object surface. Furthermore, the communication module 110 may receive a data request from a user terminal and transmit data as a response thereto.

Here, the communication module 110 may be a device including hardware and software necessary for transmitting and receiving a signal such as a control signal or a data signal through a wired or wireless connection with another network device.

The memory 120 records a program for performing a digital image correlation analysis method using an adaptive subset size capable of optimizing different adaptive subset sizes suitable for DIC analysis at every position in the ROI of the object. In addition, the processor 130 performs a function of temporarily or permanently storing data processed. Here, the memory 120 may include a volatile storage medium or a non-volatile storage medium, but the scope of the present invention is not limited thereto.

The processor 130 controls the entire process of providing a digital image correlation analysis method using the adaptive subset size. After dividing the speckle image acquired on the surface of the object into subsets, the moving displacement of the subset between the speckle images is calculated. Trace and interpret the deformation of the object. At this time, since the processor 130 can accurately track displacement when the speckle feature in the subset is sufficient, the digital image is applied by applying an adaptive subset of an appropriate size to each position according to the speckle pattern randomly distributed in the speckle image. Increase the accuracy of correlation analysis. Each operation performed by the processor 130 will be described in more detail later.

Here, the processor 130 may include all types of devices capable of processing data, such as a processor. Here, the'processor' may refer to a data processing device embedded in hardware having a circuit physically structured to perform a function represented by, for example, a code included in a program or an instruction. As an example of a data processing device built into the hardware as described above, a microprocessor, a central processing unit (CPU), a processor core, a multiprocessor, and an application-specific integrated (ASIC) circuit), a field programmable gate array (FPGA), and the like, but the scope of the present invention is not limited thereto.

The database 140 stores data accumulated while performing a digital image correlation analysis method using an adaptive subset size. For example, the database 140 may store image information, subset size information, DIC analysis information, and the like acquired from a camera.

3 is a flow chart illustrating a digital image correlation analysis method using an adaptive subset size according to an embodiment of the present invention, FIG. 4 is a diagram illustrating an initial parameter setting process in the ROI of FIG. 3, and FIG. 5 is 3 is a diagram illustrating a process of setting the convergence map of FIG. 3, FIG. 6 is a diagram illustrating a process of determining the adaptive subset size of FIG. 3, and FIG. 7 is a DIC analysis by applying the adaptive subset size of FIG. It is a diagram explaining the process to be performed.

Referring to FIG. 3, the control device 100 acquires a plurality of images of the surface of the object subjected to the speckle pattern with the same camera position and the same camera setting at regular time intervals. At this time, since the speckle pattern is randomly distributed within the image, the size, density, and intensity of gray in the pixel vary according to surface positions.

, j=1, 2, 3, …, m)를 설정한다. 기준 서브셋(S_i) 크기는 다음 수학식 1과 같다. Accordingly, the control device 100 subdivides the ROI and sets initial parameters in the ROI in order to optimize the subset size according to each position in the ROI (S1). That is, as shown in FIG. 4, _{a size parameter for arranging seed points O i} at regular intervals within the ROI and determining the size of the mood subset S _i around each seed point O _i

, j=1, 2, 3,… , m). The size of the reference subset (S _i ) is shown in Equation 1 below.

[Equation 1]

In Equation 1, in order to set the position of _{O i in units of integer pixels, one length of S i} is set to an odd number. In addition, in order to consider all ROI regions, the interval of _{O i} is set to be smaller than the minimum value _{of the S i size.}

In addition, the control device 100 defines a plurality of images acquired from the camera as a reference image R, and calculates the number of all cases in which two images are selected without overlapping by using the reference images.

5, the control device 100 when the ROI is set in the first reference image (R ₁₎ of the two images, based on a subset (S _i) and the second reference to with images (R ₂₎ mathematics After the matching distance is evaluated using a Normalized Cross Correlation (NCC) algorithm represented by Equation 2, a convergence map is set using the matching distance for each seed point (S2).

[Equation 2]

The NCC algorithm is to calculate the cross-correlation between the _{reference subset (S i} ) and the second reference image (R _{2 ), and divides the second reference image (R 2} ) into subsets of the same size as the reference subset (S _{i ).} , It is a quantitative representation of how much each of the divided subsets _{coincide with the reference subset (S i ).} In this case, the NCC algorithm expresses the degree of correspondence with _{a real number called the correlation coefficient (C NCC} ), and the pixel position with the maximum correlation coefficient in the second reference image is the position of the target subset that most matches the _{reference subset (S i ).} A subset matching algorithm to determine is performed.

When the size of the reference subset (S _i ) is small, the characteristics of the speckle pattern in the subset are insufficient, so that it is not clearly distinguished from other subsets, so that the subset matching using the NCC algorithm is not accurately performed, thus the maximum correlation in the second reference image. The positions of the coefficients appear in various ways. However, when _{the size of the reference subset S i} increases beyond a certain size, the subset matching is performed accurately _{, and the position of the target subset S i} ′ appears constant, and the position of the maximum correlation coefficient appears constant because there is no transformation between images. . Thus, it can be determined based on the subset gamyeo by increasing the size of the (S _i) in a stepwise manner if the size of the subset by checking whether the location of the maximum correlation coefficient constant through an iterative calculation process for performing NCC algorithm sufficient.

Based on the subset (S _i) and the matching destination subset if that "a seed point O _i of the matching distance D _i (S _i) ', the matching distance (D _i) is to be calculated by the equation (3).

[Equation 3]

,

)는 기준 서브셋(S_i)의 씨드 포인트(O_i)의 픽셀 위치 좌표이고, (

,

)는 매칭된 대상 서브셋(S_i')의 씨드 포인트(O_i')의 픽셀 위치 좌표를 각각 나타낸다. 제어 장치(100)는 매칭 거리의 기울이가 '0'이 되는 시작 지점을 기준으로 컨버징 사이즈를 결정할 수 있으며, 각 씨드 포인트(O_i)마다 동일한 서브셋 매칭 알고리즘을 반복적으로 수행하여 컨버전스 맵을 산출한다. In Equation 3 (

,

) Is the pixel position coordinate of the seed point (O _i ) of the reference subset (S _{i ), and (}

,

) Represents the pixel location coordinates of the "seed point (O _i a)"), the matched destination subset (S _i, respectively. The control device 100 may determine the convergence size based on a starting point at which the inclination of the matching distance becomes '0', and repeatedly performs the same subset matching algorithm for _{each seed point O i to generate a convergence map.} Calculate.

As shown in FIG. 6, the control device 100 selects two reference images without overlapping in order to consider the influence of random noise generated by lighting, shot noise, and micro-vibration of the camera when acquiring the reference image. A plurality of convergence maps are calculated by applying the number of cases, and an adaptive subset size is automatically determined by adding a standard deviation to the average of the converging sizes for each seed point (S3). Therefore, it is possible to optimize the adaptive subset size for all regions within the ROI.

As shown in FIG. 7, the control device 100 performs an NCC algorithm between the reference image and the deformed image using the adaptive subset size to track the displacement of the subset caused by the deformation of the object, thereby reducing the displacement and strain rate of the object. Perform DIC analysis to calculate (S4).

FIG. 8 is a diagram illustrating an experiment environment for verifying DIC analysis of a data analysis system according to an embodiment of the present invention, and FIG. 9 is a distribution of output by size and location of an adaptive subset size calculated by the data analysis system of FIG. 8 FIG. 10 is a diagram illustrating a verification result of an adaptive subset size using a test image.

As shown in FIG. 8, the data analysis system includes a camera 10, a control device, and a scanning stage 20 on which a speckle surface-treated aluminum plate specimen 21 is placed, wherein the scanning stage 20 is a specimen. (21) is moved in the x and y directions in 0.5 µm increments. The data analysis system may use macro lenses to observe fine speckle patterns.

In addition, the speckle surface-treated aluminum plate specimen 21 has a size of 500x500x3mm³, the speckle pattern is surface-treated using stone spray, and the ROI is a speckle pattern to observe the effect of the spatially deflected speckle pattern. Randomly set the size of 300x300 pixels in this not dense location. In addition, _{the size of the reference subset S i} ranges from a minimum of 7 x 7 pixels to a maximum of 55 x 55 pixels, and accordingly _{, the spacing of the seed points O i} is 6 pixels, and a total of 2500 is set.

This data analysis system may acquire 15 reference images from the camera 10 and acquire 3 test images moved at regular intervals of 200 μm, 500 μm, and 1 mm using the scanning stage 20.

The control device 100 performs the NCC algorithm by increasing the size of the reference subset at each seed point set in the ROI for all image combinations capable of calculating two images without duplication using the reference image, and performs the NCC algorithm for each seed point. 105 converging sizes are calculated, and the adaptive subset size is determined by adding a standard deviation to the mean of the converging sizes as shown in FIG. 9.

As shown in Fig. 9, the calculation range of the adaptive subset size varies from a minimum of 9x9 pixels to a maximum of 23 x 23 pixels, but about 90% or more is concentrated between 11 x 11 pixels and 17 x 17 pixels, of which 13 It has the most frequency at x 13 pixels.

To verify the size of the adaptive subset calculated in this way, the NCC algorithm is performed by applying the adaptive subset size to 3 test images. As shown in FIG. 10, if the subset matching result is calculated as an error distance using the difference between the matching distance and the actual distance converted in pixel units, in all 2500 seed points in the verification result using the test image. You can check that no error has occurred.

11 is a diagram illustrating a seed point randomly selected within an ROI, FIG. 12 is a diagram illustrating a single subset size calculated using the SSSIG technique at each seed point of FIG. 11, and FIG. 13 is It is a diagram explaining the verification result of the single subset size calculated using the SSSIG technique.

The SSSIG technique, one of the methods of determining the size of a subset, calculates speckle pattern gradients within the subset while increasing the size of the reference subset after selecting a random location within the ROI, and using the standard deviation error (Standard deviation). error, SD error) is calculated, and a single subset size is calculated by applying a threshold value. Using this SSSIG technique, as shown in Figs. 11 and 12, the speckle pattern slope in the subset is calculated from two randomly selected seed points, and the SD error value is calculated to determine the size of a single subset for DIC analysis. do. SD error can be expressed as in Equation 4 below.

[Equation 4]

In Equation 4, D(η) is the variance value of noise calculated using two images photographed at regular time intervals on the object surface, and f _x and f _y are calculated as the difference between adjacent points in the x and y directions. It is the first derivative of the gray intensity. The SD error threshold is set to 0.005.

As shown in FIG. 13, a single subset size of 7 x 7 pixels and 9 x 9 pixels is calculated using the SSSIG technique, and the calculated single subset size is (a) a 200 μm test image (7 x 7 pixels), (b) 500 µm test image (7 x 7 pixels), (c) 1 mm test image (7 x 7 pixels), (d) 200 µm test image (9 x 9 pixels), (e) 500 µm test image ( 9 x 9 pixels), (f) 1 mm test image (9 x 9 pixels) can be used to verify.

That is, FIG. 13 shows the result of performing the NCC algorithm on the test image by applying two single subset sizes of 7 x 7 pixels and 9 x 9 pixels, and Table 1 shows the subset matching according to an embodiment of the present invention. The verification result using the algorithm and SSSIG technique is quantitatively expressed as the number of seed points with errors out of 2500 seed points.

[Table 1]

Table 1 compares the error rate of the adaptive subset size for the test image and the single subset size calculated by the SSSIG technique.When the 7 x 7 pixels single subset size calculated using the SSSIG technique is applied, the 200 μm test image Errors occurred at 829, 134 in the 500 μm test image, and 924 seed points in the 1 mm test image. When a single subset size of 9 x 9 pixels was applied, 67 in the 200 μm test image, and in the 500 μm test image. In the 8, 1 mm test images, it can be seen that an error occurs at 71 seed points. As shown in FIG. 13, it can be seen that an error occurs differently depending on the test image used even though the same subset size is applied due to random noise.

When the subset size is calculated by the SSSIG method, the size of the subset calculated according to the position where the speckle pattern slope is calculated varies, so it is impossible to apply it to images where the speckle pattern is not evenly distributed, and the threshold value is arbitrarily set by the user. Because of the setting, the reliability of the calculated subset size is degraded, and an error may occur even when the calculated subset size is applied due to the influence of random noise generated during image acquisition.

However, since the subset matching algorithm according to an embodiment of the present invention optimizes the subset size required for DIC analysis in all areas within the ROI, it can be applied even if the speckle pattern in the image is biased, and the subset size is automatically adjusted without user intervention. Because it is calculated and optimized, DIC analysis based on high reliability is possible, and errors due to random noise are reduced through adaptive subset size calculation using multiple reference images, and the size of the subset is prevented from being excessively large or small. I can. Accordingly, the present invention may enable more precise and accurate analysis in DIC analysis.

The digital image correlation analysis method using the adaptive subset size according to the embodiment of the present invention described above may also be implemented in the form of a recording medium including instructions executable by a computer such as a program module executed by a computer. have. Such recording media include computer-readable media, and computer-readable media may be any available media that can be accessed by a computer, and include both volatile and nonvolatile media, removable and non-removable media. In addition, computer readable media includes computer storage media, which are volatile and nonvolatile embodied in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. , Removable and non-removable media are included.

The above description of the present invention is for illustrative purposes only, and those of ordinary skill in the art to which the present invention pertains will be able to understand that other specific forms can be easily modified without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are illustrative and non-limiting in all respects. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as being distributed may also be implemented in a combined form.

The scope of the present invention is indicated by the claims to be described later rather than the detailed description, and all changes or modified forms derived from the meaning and scope of the claims and their equivalent concepts should be construed as being included in the scope of the present invention. do.

100: control device
110: communication module 120: memory
130: processor 140: database

Claims (8)

In a digital image correlation analysis method using an adaptive subset size performed by a data analysis system for analyzing a deformation of an object,
a) One or more reference images are acquired at predetermined time intervals on the surface of an object on which a speckle pattern is formed, and the number of cases in which two images are selected without overlapping using the reference images is calculated, and the Setting a seed point at a predetermined interval in a region of interest set on one of the two images, and setting a reference subset of a predetermined size around each seed point;
b) The ROI of the target image is divided into a target subset corresponding to the reference subset, and a matching distance between the matched subset and the reference subset is calculated by performing a subset matching algorithm between the target subset and the reference subset, and the matching Generating a convergence map by repeatedly determining a convergence size at a point where the distance converges for each seed point;
c) calculating a plurality of convergence maps according to the calculated number of cases, and determining an adaptive subset size by adding a standard deviation to the average of the convergence sizes for each seed point for the calculated convergence map; And
d) performing a Digital Image Correlation (DIC) analysis between the reference image and the target image using the adaptive subset size to track the displacement of the subset caused by the deformation of the object,
The subset matching algorithm,
Computing a correlation coefficient between the reference subset and a target image while gradually increasing the size of the reference subset, and determining the location of the pixel having the maximum correlation coefficient as the location of the target subset matching the reference subset. The digital image correlation analysis method using the adaptive subset size. The method of claim 1,
The size of the reference subset (S _i ) is determined by Equation 1 below, and the spacing between the seed points is set to be smaller than the minimum value of the size of the reference subset.
[Equation 1]

: Size parameter, j=1, 2, 3,… , m The method of claim 1,
The subset matching algorithm,
A digital image correlation analysis method using an adaptive subset size to calculate a _{correlation coefficient (C NCC} ) using a Normalized Cross Correlation (NCC) algorithm represented by Equation 2 below.
[Equation 2]

: Size parameter, j=1, 2, 3,… , m The method of claim 3,
The subset matching algorithm,
If the location of the pixel having the maximum correlation coefficient is constant through an iterative calculation process of performing the NCC algorithm while increasing the size of the reference subset stepwise, the matching distance converges to determine the converging size of the adaptive subset. That is, a digital image correlation analysis method using an adaptive subset size. The method of claim 1,
The matching distance (D _i ) is calculated by Equation 3 below. A digital image correlation analysis method using an adaptive subset size.
[Equation 3]

(

,

): Pixel position coordinates of the seed point (O _i ) of the reference subset (S _i)
(

,

): Pixel position coordinates of the seed point (O _i ') of the matched target subset (S _i') The method of claim 5,
The subset matching algorithm,
The converging size is determined based on the starting point at which the slope of the matching distance D _i becomes '0', and the convergence map is calculated by determining a converging size based on the matching distance for each seed point. , Digital image correlation analysis method. In the data analysis system for analyzing the deformation of the object,
A memory in which a program for performing a digital image correlation analysis method using an adaptive subset size for each position in all regions in a region of interest (ROI) of an object is recorded; And
And a processor for executing the program,
The processor, by executing the program,
One or more reference images are acquired at predetermined time intervals on the surface of the object on which the speckle pattern is formed, and the number of cases in which two images are selected without overlap using the reference images is calculated, and Set a seed point at a preset interval in a region of interest set on any one of the reference images, and set a reference subset of a preset size around each seed point,
The ROI of the target image is divided into a target subset corresponding to the reference subset, and a matching distance between the matched subset and the reference subset is calculated by performing a subset matching algorithm between the target subset and the reference subset, and the matching distance is A convergence map is generated by repeatedly determining the convergence size at the point of convergence for each seed point,
A plurality of convergence maps are calculated according to the calculated number of cases, and an adaptive subset size is determined by adding a standard deviation to the average of the converging sizes for each seed point for the calculated convergence map,
Performing a digital image correlation (DIC) analysis between the reference image and the target image using the adaptive subset size to track the displacement of the subset caused by the deformation of the object,
The subset matching algorithm calculates a cross-correlation between the reference subset and a target image while increasing the size of the reference subset step by step and expresses it as a correlation coefficient, and a location of a pixel having a maximum correlation coefficient matches the reference subset. To determine the location of the subset, data analysis system. A computer-readable recording medium on which a program for performing the digital image correlation analysis method using the adaptive subset size according to claim 1 is recorded.

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