Image analysis device, image analysis method, and image analysis program

Abstract

To make it possible to analyze orientation information of an object with high accuracy.SOLUTION: An image analysis device includes: a binarization processing part for receiving an image obtained by photographing materials including an object and creating a binarized image obtained by separating and binarizing the object in the image and a background; a contour extraction part for extracting a contour of the object from the binarized image; a calculation part for calculating an orientation in each pixel of the extracted contour; an enclosing contour detection part for detecting an enclosing contour enclosing the periphery of the pixel in the contour about each pixel inside the object by a prescribed detection method; and an orientation calculation part for calculating orientation information of the pixel by using the orientation of the enclosing contour about each pixel inside the object.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present disclosure relates to an image analysis device, an image analysis method, and an image analysis program. [Background technology]

[0002] Conventionally, there are techniques related to image detection of an object.

[0003] For example, there is a technology for obtaining indexes related to particle angles (see Patent Document 1). In addition, there is an image analysis technology that can calculate the feature amount of a three-dimensional structure with a small amount of calculation and display the distribution in a specific direction in an easily understandable manner (see Patent Document 2). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2021-135824 A [Patent Document 2] JP 2018-91765 A Summary of the Invention [Problem to be solved by the invention]

[0005] Here, image analysis is being carried out on composite materials in which fillers are added to resins, etc. When the aspect ratio of the filler is small (for example, 10 or less), the contribution of the orientation of the filler ends to the overall orientation of the filler increases. In the evaluation of filler orientation, attention is often focused on the main orientation of the filler (the axial direction for fibers, or the normal direction of the main surface for plate-like particles), so the orientation at the ends is considered an error.

[0006] When the technology of Patent Document 1 is used, for example, image analysis of an object such as a fiber is assumed. In image analysis of an object such as a fiber, the contour of the object is extracted from a two-dimensional slice image taken from a three-dimensional image, and the orientation is calculated based on this. Since fibers often have a large aspect ratio, the influence of the end on the overall orientation is small. However, as described above, in the case of a filler with a small aspect ratio, the influence of the end cannot be ignored and a large error occurs, so a method of calculating the orientation of the filler only from the orientation of the contour is not appropriate. Similarly, when the technology of Patent Document 2 is used, the orientation and length of the object are calculated based on the extracted contour, and the influence of the end is a concern.

[0007] The present disclosure has been made in consideration of the above circumstances, and aims to provide an image analysis device, an image analysis method, and an image analysis program capable of analyzing orientation information of an object with high accuracy. [Means for solving the problem]

[0008] The image analysis device disclosed herein includes a binarization processing unit that receives an image of a material containing an object, separates the object from the background in the image, and creates a binarized image by binarizing the image; a contour extraction unit that extracts the contour of the object from the binarized image; a calculation unit that calculates the orientation of each pixel of the extracted contour; a surrounding contour detection unit that detects, for each pixel inside the object, a surrounding contour of the contour that surrounds the pixel using a predetermined detection method; and an orientation calculation unit that calculates orientation information of each pixel inside the object using the orientation of the surrounding contour.

[0009] Furthermore, in the image analysis device of the present disclosure, the enclosing contour detection unit may, in a first detection process, group contour pixels according to an angle from a target object pixel, and further create subgroups within each group according to a distance from the target pixel, and select, from among the distance subgroups within each angle group, a contour pixel of a subgroup closest to the target pixel as a candidate for the enclosing contour, and, in a second detection process, select, as a candidate for the enclosing contour, a contour pixel for which the angle or dot product between a direction vector from the target object pixel to each contour pixel and a contour orientation vector satisfy a predetermined condition, and detect, as the enclosing contour, a contour pixel selected as a candidate in both the first detection process and the second detection process.

[0010] In the image analyzing device of the present disclosure, the encircling contour detection section may set a detection radius in detecting the encircling contour to a range that is at least a predetermined double a predetermined length of the object end.

[0011] In addition, in the image analysis device of the present disclosure, the orientation calculation unit may calculate an orientation vector for each pixel inside the object by calculating a predetermined tensor using the number of pixels in the enclosing contour and the orientation vector of the enclosing contour. Effect of the Invention

[0012] According to the image analysis device, image analysis method, and image analysis program of the present disclosure, it is possible to analyze the orientation information of an object with high accuracy. [Brief description of the drawings]

[0013] [Figure 1] FIG. 1 is a diagram showing a schematic diagram of the orientation of the normal direction of the surface of an object. [Diagram 2] FIG. 2 is a block diagram showing the configuration of the image analysis device of this embodiment. [Diagram 3] FIG. 3 is a block diagram showing a hardware configuration of the image analyzing apparatus. [Figure 4]FIG. 4 shows an example of an original image and a binarized image to be analyzed. [Diagram 5] FIG. 5 shows an example of extracting the contour of a filler region separated by binarization processing, in which the contour of a white portion is extracted. [Figure 6] FIG. 6 shows an example of calculating the orientation vector of each pixel of the contour, in which the orientation vector of the contour of the portion surrounded by the dotted line is calculated. [Figure 7] FIG. 7 shows an example of detecting the surrounding contour for a pixel of interest. [Figure 8] FIG. 8 is a diagram illustrating an example of grouping. [Figure 9] FIG. 9 shows the distribution of the obtained alignment vectors and the alignment angles calculated based on the alignment vectors. [Figure 10] FIG. 10 shows a comparative example of the orientation analysis results obtained by a general method. [Figure 11] FIG. 11 is a flowchart showing the flow of image analysis processing by the image analyzing device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Hereinafter, an embodiment of the present disclosure will be described in detail with reference to the drawings. First, an overview of the embodiment will be described.

[0015] Analysis of composite materials in which fillers are added to resins and the like is being conducted. Composite materials containing fillers can be given or improved in various properties by changing the type / combination of the base material and filler. For example, hexagonal boron nitride particles (h-BN particles) are plate-shaped fillers in which a planar structure consisting of a hexagonal network of nitrogen atoms and boron atoms is laminated, and since they have high thermal conductivity in the planar direction, they are often used for applications such as heat dissipation materials. However, h-BN particles have thermal conductivity anisotropy and have low thermal conductivity in the lamination direction. Therefore, in order for h-BN composite materials to exhibit good thermal conductivity, it is desirable for the h-BN particles to be oriented in the desired direction. For example, in the case of a heat sink, it is desirable to control the planar direction of the h-BN particles to be aligned perpendicular to the thickness direction of the heat sink.

[0016] In light of the above, in order to create materials with the desired properties, it is important to control and evaluate the orientation of the filler. Therefore, it is important to evaluate the orientation of the filler, which is generally performed by image analysis. Note that the filler to be analyzed is not limited to the h-BN particles shown above.

[0017] FIG. 1 is a diagram showing the orientation of the normal direction of the surface of an object. (d1) is the normal direction of the main surface, and (d2) is the normal direction of the side surface. When a disk-shaped or rectangular filler is considered, the normal vectors of the main surface and the side surface of the filler are perpendicular to each other. Therefore, when the aspect ratio of the filler is small, the ratio of the orientation of the side surface (end portion) increases. For example, h-BN particles have good thermal conductivity in the in-plane direction, so it is important to evaluate the orientation in the in-plane direction (i.e., the orientation of the main surface), and it is desirable to exclude the orientation of the side surface. However, since the main surface and the side surface are not distinguished in normal orientation analysis, a large error in orientation occurs in a filler with a low aspect ratio. Therefore, in the image analysis of this embodiment, a method is used that enables a more accurate evaluation of the filler orientation by considering the surrounding contour of the pixel of interest (object pixel of interest). Since the surrounding contour contains many contours of the main surface of the filler, an orientation close to the main orientation can be calculated even in pixels near the end portions.

[0018] Fig. 2 is a block diagram showing the configuration of the image analysis device of this embodiment. As shown in Fig. 2, the image analysis device 100 includes a binarization processing unit 110, a contour extraction unit 112, a calculation unit 114, a surrounding contour detection unit 116, and an orientation calculation unit 118.

[0019] Fig. 3 is a block diagram showing a hardware configuration of the image analyzing device 100. As shown in Fig. 3, the image analyzing device 100 has a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a storage 14, an input unit 15, a display interface (I / F) 16, and a communication interface (I / F) 17. Each component is connected to each other via a bus 19 so as to be able to communicate with each other.

[0020] The CPU 11 is a central processing unit, and executes various programs and controls each part. That is, the CPU 11 reads a program from the ROM 12 or the storage 14, and executes the program using the RAM 13 as a working area. The CPU 11 controls each of the above components and performs various arithmetic processing according to the program stored in the ROM 12 or the storage 14. In this embodiment, an image analysis program is stored in the ROM 12 or the storage 14.

[0021] The ROM 12 stores various programs and various data. The RAM 13 temporarily stores programs or data as a working area. The storage 14 is configured with a storage device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive), and stores various programs including an operating system, and various data.

[0022] The input unit 15 includes a pointing device such as a mouse, and a keyboard, and is used to perform various input operations.

[0023] The display interface 16 is, for example, a liquid crystal display, and displays various information. The display interface 16 may function as the input unit 15 by adopting a touch panel system.

[0024] The communication interface 17 is an interface for communicating with other devices such as terminals, and uses standards such as Ethernet (registered trademark), FDDI, and Wi-Fi (registered trademark).

[0025] Next, a description will be given of each functional configuration of the image analyzing device 100. Each functional configuration is realized by the CPU 11 reading out an image analysis program stored in the ROM 12 or the storage 14, expanding it in the RAM 13, and executing it.

[0026] In the image analysis device 100 of this embodiment, an example will be described in which an image of a cross section of a material containing a filler is received and analyzed. A procedure for applying this embodiment will be described using an example of a two-dimensional image of a cross section of a composite material having a resin as a base material and h-BN particles as a filler, taken by a scanning electron microscope.

[0027] FIG. 4 shows examples of an original image and a binary image to be analyzed. (a1) is the original image, and (a2) is a binary image obtained by performing a binary processing described later on the original image. The number of pixels in the image is 145 × 124, and the thickness of the L-shaped filler in the center is about 20 pixels. Note that the filler in the composite material is an example of an object in the present disclosure, and the resin part corresponds to the background.

[0028] The binarization processing unit 110 separates the object from the background in the image and creates a binarized image. In order to separate the resin and the filler, the binarization processing unit 110 performs binarization based on the brightness value. Here, in order to perform a clearer separation, an averaging filter is applied to the original image, and binarization is performed based on the calculated average brightness value (a2). Note that small white areas remain in the resin area and small black areas remain in the filler area, but these can be removed according to their area (or volume).

[0029] The contour extraction unit 112 extracts the contour of an object from a binarized image. FIG. 5 shows an example of extracting the contour of a filler region separated by binarization processing, in which the contour of a white portion is extracted. Here, contour extraction is performed based on the gradient of brightness values. After calculating the gradient of brightness values ​​for each pixel, pixels whose gradient magnitude is equal to or greater than a threshold value are extracted as contours. Note that in FIG. 5, processing is performed to remove contours with small areas, so the example shows that contours of small black regions within the filler are not extracted.

[0030] The calculation unit 114 calculates the orientation at each pixel of the extracted contour. Here, an orientation vector is calculated for each pixel of the extracted contour. In this embodiment, the orientation of the contour refers to a local normal vector at each position of the contour. FIG. 6 shows an example of calculating the orientation vector of each pixel of the contour, where the orientation vector of the contour of the portion surrounded by the dotted line is calculated. Here, the luminance value gradient obtained in the previous step is simply used as the orientation vector. Therefore, the orientation vector of the contour is inward (toward the inside of the filler).

[0031] The enclosing contour detection unit 116 detects, for each pixel inside the object, the enclosing contour that surrounds the pixel from among the contours by a predetermined detection method. The detection method uses the following first and second detection processes to select candidates for the enclosing contour, and detects the contour pixels selected as candidates as the enclosing contour. Note that the inside of the object refers to the area inside the contour extracted by the contour extraction unit 112.

[0032] FIG. 7 shows an example of detecting the surrounding contour for a pixel of interest. If the surrounding contour is detected simply based on the distance from the filler pixel of interest, extra contours (contours of different fillers) will also be detected. To alleviate this, the following two processes are applied, and a contour pixel that simultaneously satisfies these conditions is regarded as the surrounding contour. The search radius for detecting contour pixels is set to 50 pixels. In detecting the surrounding contour, the detection radius should be set to a range of at least twice the specified length of the object end (side length, plate thickness, and diameter).

[0033] In the first detection process, first, the contour pixels are grouped according to the angle from the pixel of interest. FIG. 8 is a diagram showing an example of grouping. In the example shown in the figure, the pixels are divided every 45°, and eight angle groups are created. Next, subgroups are created for the contour pixels in each angle group according to the distance from the pixel of interest. In each angle group, the contour pixels of the subgroup closest to the pixel of interest are set as candidates for the surrounding contour. This is to select a contour that is close in distance as the surrounding contour. In this way, the surrounding contour detection unit 116 creates subgroups in each group according to the distance from the pixel of interest as the first detection process, and selects the contour pixels of the subgroup closest to the pixel of interest from among the distance subgroups in each angle group as candidates for the surrounding contour.

[0034] In the second detection process, pixel vectors are used. First, a direction vector from the filler pixel of interest to each contour pixel is obtained. The dot product of this direction vector and the contour orientation vector obtained by the calculation unit 114 is calculated, and if the value is negative, the pixel is determined to be a candidate for the enclosing contour. In this way, in the second detection process, the enclosing contour detection unit 116 selects contour pixels that satisfy a predetermined condition (for example, the dot product value is negative) as candidates for the enclosing contour based on the angle between the direction vector from the pixel of interest to each pixel of the contour and the orientation vector of the contour.

[0035] The encircling contour detection unit 116 detects, for each pixel inside the object, candidate contour pixels that simultaneously satisfy the selection criteria of the first detection process and the second detection process, as an encircling contour.

[0036] The orientation calculation unit 118 calculates the orientation information of each pixel inside the object using the orientation of the surrounding contour. Here, for each pixel of the filler, an orientation vector is calculated based on the orientation vector of the surrounding contour of the pixel. An example of a method for calculating the orientation vector is shown below. First, the following second-order tensor A is calculated for each pixel of the filler using the following formula (1).

number

[0037] However, n edge is the number of pixels in the enclosing contour, p edge is the orientation vector of the enclosing contour. The eigenvalues ​​and eigenvectors of A are obtained, and the eigenvector corresponding to the maximum eigenvalue is set as the orientation vector of the filler pixel. FIG. 9 shows the obtained orientation vectors and the distribution of the orientation angles calculated based on the orientation vectors. It can be confirmed that the orientation of each filler is calculated satisfactorily. As described above, the orientation calculation unit 118 calculates the orientation vector of each pixel inside the object by calculating a predetermined tensor using the number of pixels of the enclosing contour and the orientation vector of the enclosing contour.

[0038] For reference, a comparative example of the orientation analysis result by a general method is shown in Fig. 10. In this method, first, the orientation tensor A of the following equation (2) is calculated for each pixel of the filler.

number

[0039] where N is the number of pixels surrounding the pixel of interest. The number of surrounding pixels does not distinguish between filler, contour, and background. The surrounding area here is a rectangular area of ​​L pixels by L pixels centered on the pixel of interest. ￣I (￣ is a superscript) is the brightness value gradient, and is calculated based on the brightness value before binarization. The eigenvalues ​​and eigenvectors of A are found, and the eigenvector corresponding to the maximum eigenvalue is used as the orientation vector of the filler pixel.

[0040] As shown in FIG. 10, when the surrounding area is changed, in the two cases where the surrounding area is changed, (b1) L=3 and (b2) L=21, a generally good orientation is obtained by setting the surrounding area large (b2). However, in the center of the thick filler on the left and the end area shown by the circled dashed line, an orientation different from the purpose of the method of this embodiment is calculated. By setting the surrounding area wider than these cases, the influence of the end is further mitigated, but in this case, it is susceptible to the influence of the orientation of nearby different fillers, so it can be said that this is not appropriate. On the other hand, in the method of this embodiment, as shown in FIG. 7, the contour can be detected over a wide range while mitigating the false detection of different fillers, so that an orientation that meets the purpose is calculated even at the end as shown in FIG. 9.

[0041] Next, a description will be given of the operation of the image analysis device 100 according to this embodiment. Fig. 11 is a flowchart showing the flow of image analysis processing by the image analysis device 100. The CPU 11 reads out an image analysis program from the ROM 12 or storage 14, deploys it in the RAM 13, and executes it, thereby performing the image analysis processing.

[0042] In step S100, the CPU 11 functions as the binarization processor 110 to separate an object from a background in an image and to binarize the image to generate a binarized image.

[0043] In step S102, the CPU 11, functioning as the contour extraction unit 112, extracts the contour of an object from the binarized image.

[0044] In step S104, the CPU 11 functioning as the calculation unit 114 calculates the orientation of each pixel of the extracted contour.

[0045] In step S106, the CPU 11, functioning as the surrounding contour detection unit 116, groups the contour pixels according to the angle from the pixel of interest.

[0046] In step S108, the CPU 11, functioning as the encircling contour detection unit 116, creates subgroups for the contour pixels in each angle group according to the distance from the pixel of interest, and selects the subgroup closest to the pixel of interest as a candidate for the encircling contour.

[0047] In step S110, CPU 11, functioning as enclosing contour detection unit 116, selects contour pixels whose inner product value satisfies a predetermined condition based on the angle between the direction vector from the pixel of interest to each pixel of the contour and the orientation vector of the contour, as candidates for the enclosing contour.

[0048] In step S112, the CPU 11, functioning as the encircling contour detection unit 116, detects, for each pixel inside the object, candidate contour pixels that simultaneously satisfy the selection conditions of the first detection process and the second detection process, as an encircling contour.

[0049] In step S114, the orientation calculation unit 118 of the CPU 11 calculates, for each pixel inside the object, the orientation information of that pixel using the orientation of the surrounding contour, using the above formula (1).

[0050] As described above, the image analyzing device according to this embodiment can analyze the orientation information of an object with high accuracy.

[0051] The present disclosure is not limited to the above-described embodiment, and various modifications and applications are possible without departing from the spirit and scope of the present invention.

[0052] In the above embodiment, the image analysis process executed by the CPU by reading the software (program) may be executed by various processors other than the CPU. In this case, examples of the processor include a PLD (Programmable Logic Device) whose circuit configuration can be changed after manufacturing such as an FPGA (Field-Programmable Gate Array), and a dedicated electric circuit which is a processor having a circuit configuration designed exclusively for executing a specific process such as an ASIC (Application Specific Integrated Circuit) or a GPU (Graphics Processing Unit). The image analysis process may be executed by one of these various processors, or may be executed by a combination of two or more processors of the same or different types (for example, a plurality of FPGAs, a combination of a CPU and an FPGA, etc.). The hardware structure of these various processors is, more specifically, an electric circuit that combines circuit elements such as semiconductor elements.

[0053] In the above embodiment, the image analysis processing program is stored (installed) in advance in a ROM or storage, but the present invention is not limited to this. The program may be provided in a form recorded on a non-transitory recording medium such as a CD-ROM (Compact Disk Read Only Memory), a DVD-ROM (Digital Versatile Disk Read Only Memory), or a USB (Universal Serial Bus) memory. The program may also be downloaded from an external device via a network. [Explanation of symbols]

[0054] 100 Image analysis device 110 Binarization processing unit 112 Contour Extraction Unit 114 Calculation section 116 Enclosing Contour Detector 118 Orientation Calculation Section

Claims

1. a binarization processing unit that receives an image of a material including an object, separates the object from a background in the image, and creates a binarized image; a contour extraction unit that extracts a contour of an object from the binarized image; A calculation unit for calculating an orientation at each pixel of the extracted contour; a surrounding contour detection unit that detects, for each pixel inside the object, a surrounding contour that surrounds the pixel from among the contours using a predetermined detection method; an orientation calculation unit that calculates orientation information of each pixel inside the object using the orientation of the surrounding contour; An image analysis device comprising:

2. The encircling contour detection unit detects the encircling contour by As a first detection process, the contour pixels are grouped according to an angle from a target object pixel, and further, subgroups are created within each group according to a distance from the target pixel, and a contour pixel of a subgroup that is closest to the target pixel among distance subgroups within each angle group is selected as a candidate for the enclosing contour; As a second detection process, a contour pixel whose inner product value satisfies a predetermined condition is selected as a candidate for the enclosing contour based on a direction vector from the object pixel of interest to each pixel of the contour and an orientation vector of the contour; The image analyzing device according to claim 1 , wherein contour pixels selected as candidates in both the first detection process and the second detection process are detected as the enclosing contour.

3. The image analyzing device according to claim 2 , wherein the encircling contour detection section sets a detection radius in detecting the encircling contour to a range equal to or greater than a predetermined multiple of a predetermined length of an object end portion.

4. The image analysis device according to claim 1 , wherein the orientation calculation unit calculates, for each pixel inside the object, an orientation vector of the pixel by a predetermined calculation using the number of pixels of the enclosing contour and an orientation vector of the enclosing contour.

5. Accepting an image of a material including an object, separating the object from the background in the image, and creating a binarized image; Extracting a contour of an object from the binarized image; Calculating the orientation at each pixel of the extracted contour; For each pixel inside the object, a surrounding contour that surrounds the pixel is detected from among the contours by a predetermined detection method; calculating orientation information for each pixel inside the object using the orientation of the enclosing contour; The image analysis method includes the steps of:

6. Accepting an image of a material including an object, separating the object from the background in the image, and creating a binarized image; Extracting a contour of an object from the binarized image; Calculating the orientation at each pixel of the extracted contour; For each pixel inside the object, a surrounding contour that surrounds the pixel is detected from among the contours by a predetermined detection method; calculating orientation information for each pixel inside the object using the orientation of the enclosing contour; An image analysis program that causes a computer to perform the following processing:

Images