Method, System, and Program for Calculating Dispersion Evaluation Value of Silica

Abstract

To provide a method, system, and program for computing a silica dispersion evaluation value while avoiding deterioration in accuracy of the evaluation value due to binarization.SOLUTION: A silica dispersion evaluation value computation method is provided, comprising extracting multiple clusters of pixels with relatively higher intensity than the surroundings from an electron microscopy image (SEM image), a captured grayscale image of a rubber surface, identifying a centroid of each of the clusters of pixels, and computing a silica dispersion evaluation value based on the identified centroids.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present disclosure relates to a method, a system, and a program for calculating a dispersion evaluation value of silica.

Background Art

[0002] In order to evaluate the dispersion of silica in rubber, it has been carried out to evaluate by human eyes based on a SEM image (scanning electron microscope image) in which the intensity of silica appears high (white). However, there are empirical values and variations among people in manual operation, and a method that can quantitatively evaluate is desired.

[0003] In paragraph 0056 of Patent Document 1, there is a description of obtaining the average aggregate area of vulcanized rubber. In Patent Document 1, there is a description of binarizing a SEM image with a threshold value determined by the Otsu method and calculating the aggregate area of the silica portion based on the binarized image.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, in a SEM image of a rubber surface, even if the object to be photographed is the same rubber, the intensity unevenness is different depending on the photographing conditions, and if appropriate binarization is not performed, it is considered that the accuracy of the evaluation value is impaired. The Otsu method described in Patent Document 1 can relatively appropriately determine a threshold value for binarization for an image in which two peaks appear in the intensity histogram of the image, but a SEM image of a rubber surface has no clear peak in intensity, and an appropriate threshold value cannot be determined by the Otsu method. Not only the Otsu method, but also no appropriate method for determining a threshold value for binarizing a SEM image of rubber has been proposed.

[0006] The present disclosure provides a method, a system, and a program for calculating a dispersion evaluation value of silica, which avoid deterioration in the accuracy of the evaluation value due to binarization.

Means for Solving the Problems

[0007] The method for calculating the dispersion evaluation value of silica according to the present disclosure is a method executed by one or more processors. From an SEM image, which is a grayscale image obtained by imaging the rubber surface, a plurality of clusters of pixels having relatively higher intensity than the surroundings are extracted, the centroid of each of the extracted clusters of pixels is identified, and a dispersion evaluation value of silica is calculated based on the plurality of identified centroids.

Brief Description of the Drawings

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Modes for Carrying Out the Invention

[0009] Hereinafter, an embodiment of the present disclosure will be described with reference to the drawings.

[0010] [System] The system 1 (device) of this embodiment calculates a silica dispersion evaluation value from a SEM image obtained by imaging the rubber surface. The SEM image is a grayscale image obtained by imaging with an electron microscope. The grayscale image has intensity for each pixel, and the intensity indicates the shade of black. The gradation of the intensity is 256 if it is 8-bit, for example, and 65536 if it is 16-bit. If the intensity is low, it is black, and if the intensity is high, it is white. A single particle of silica or an aggregate of particles appears as a relatively white pixel in the SEM image.

[0011] As shown in FIG. 1, the system 1 includes a SEM image acquisition unit 10, a pixel block extraction unit 11, a centroid identification unit 12, and an evaluation value calculation unit 13. The pixel block extraction unit 11 includes a first image generation unit 14, a second image generation unit 15, and a center extraction unit 16. Each of these units 10 to 16 is realized by the cooperation of software and hardware when the processor 1a executes the processing routine shown in FIG. 2 stored in advance in a computer equipped with a processor 1a, a memory 1b, various interfaces, etc. In this embodiment, the processor 1a in one device realizes each unit, but it is not limited to this. For example, it may be configured to be distributed using a network and a plurality of processors execute the processing of each unit. That is, one or a plurality of processors execute the processing. The memory 1b stores a SEM image, a first image with increased intensity of the SEM image, a second image with dilated high-intensity pixels of the SEM image, center information of the high-intensity pixels, centroid information, nearest neighbor distance, silica dispersion evaluation value, and the like.

[0012] The SEM image acquisition unit 10 acquires a SEM image that is a grayscale image and captures the surface of the rubber, and stores it in the memory 1b. FIG. 3 shows a SEM image capturing the surface of the rubber, which is a grayscale image representing the intensity in 16-bit gradations. Although the imaging conditions are described at the bottom of FIG. 3, the calculation of the dispersion evaluation value of silica is performed on the image body excluding the bottom. Hereinafter, SEM images other than FIG. 3 show images corresponding to the image body excluding the bottom of FIG. 3. FIG. 4 shows the intensity histogram of the image body portion shown in FIG. 3. The horizontal axis of FIG. 4 represents intensity, and the vertical axis represents the number of pixels. The left end of the horizontal axis of FIG. 4 indicates 0 (black), and the right end of the horizontal axis indicates 65535 (white). As shown in FIG. 4, the intensity distribution of the SEM image capturing the surface of the rubber is uniform, without a distinct peak, and it is difficult to determine an appropriate single threshold value for binarization that applies to the entire image. Therefore, this specification proposes a method for calculating the dispersion evaluation value of silica without binarizing the SEM image.

[0013] The pixel block extraction unit 11 extracts a plurality of blocks of pixels having a relatively higher intensity than the surroundings from the SEM image. Pixels having a relatively higher intensity than the surroundings are white pixels with a high intensity and are likely to be silica. In the present embodiment, the pixel block extraction unit 11 is composed of a first image generation unit 14, a second image generation unit 15, and a center extraction unit 16.

[0014] The first image generation unit 14 generates a first image with increased intensity of the SEM image. In the present embodiment, the intensity of each pixel of the SEM image is multiplied by 1.05, but the numerical value can be changed as appropriate. The lower limit value of the amount of intensity increase is 1.01 times, and the upper limit value of the amount of intensity increase is preferably 1.15 times. This is because if the intensity can be slightly increased, the center of the white pixels can be extracted by the intensity calculation with the second image. FIG. 5 shows the first image generated from the image body portion shown in FIG. 3. The image in FIG. 5 is an image obtained by multiplying the intensity of the image body portion in FIG. 3 by 1.05.

[0015] The second image generation unit 15 generates a second image obtained by performing a dilation process of expanding a region of pixels having a relatively higher intensity than the surroundings on the SEM image. Examples of the dilation process include the dilation process of morphological operations. In the present embodiment, a disk element having a size of 5 pixels is used as a structuring element (also called a kernel), but the size of the disk element can be appropriately changed. Since the dilation process of morphological operations is well known, a detailed description thereof will be omitted, and a simple explanation will be given with reference to FIG. 7. FIG. 7 is an explanatory diagram regarding the dilation process. As schematically shown in FIG. 7, assume that the original image has a rectangular block of white pixels on a black pixel background. As shown in the figure, the following operation is performed while moving the disk element one pixel at a time with respect to the original image. Each time the disk element is moved, the operation of outputting the pixel located at the center within the disk element as the pixel having the highest intensity value within the disk element to the corresponding pixel of the new image is repeated. If the number of pixels in the original image is N, the operation of outputting the pixel located at the center within the disk element as the pixel having the highest intensity value within the disk element to the corresponding pixel of the new image is executed N times. Only the original image is referred to within the disk element, and the new image is not referred to, so the pixels of the new image once output do not affect the dilation process. As a result, the pixels having a relatively high intensity within the original image are expanded in a disk shape. In the example of FIG. 7, the rectangular block of white pixels is changed to a circular block of white pixels. In the present embodiment, a disk element is used as the structuring element (kernel), but the present invention is not limited thereto, and a rectangular element may be used. However, since the silica shape is circular, the disk element is considered to be the most preferable. FIG. 6 shows a second image obtained by performing a dilation process on the image main body portion of the SEM image shown in FIG. 3 using a 5-pixel disk element.

[0016] The central extraction unit 16 extracts the central portion of a cluster of pixels with relatively higher intensity than the surroundings by subtracting the intensity of the second image from the first image. FIG. 9 shows an image obtained by subtracting the intensity of the second image shown in FIG. 6 from the intensity of the first image shown in FIG. 5. It can be seen that the central portion of the white-system pixels is obtained. FIG. 8 is a diagram showing the principle of extracting the center of a cluster of pixels with relatively higher intensity than the surroundings. As shown at the top of FIG. 8, an example will be described where there are white-system pixels on a black-system pixel background as the original image. When the intensity is illustrated along the line A1 - A1 passing through the white-system pixels in the left - right direction, as shown on the upper - right side of FIG. 8, the intensity changes like a mountain. The vicinity of the peak of the mountain is the intensity of the white-system pixels. The first image obtained by performing a process of increasing the intensity on the original image has, as shown on the middle - right side of FIG. 8, the intensity of the original image increased while maintaining the shape. The second image subjected to the dilation process has, as shown on the middle - left side of FIG. 8, the peak intensity of the original image remaining unchanged while the width of the portion with relatively higher intensity expands. If the intensity of the second image is subtracted from the intensity of the first image, the portion indicated by the hatching at the bottom of FIG. 8 remains as the central portion of the white-system pixels. Thereby, a part (central portion) of a cluster of pixels with relatively higher intensity than the surroundings is extracted. From the image of FIG. 9, the clusters of pixels are selected based on the size of the clusters of pixels. Specifically, clusters of pixels in which the number of pixels constituting the cluster of pixels is within a predetermined range determined by a predetermined lower limit value and a predetermined upper limit value are extracted. In the present embodiment, with the predetermined lower limit value being 8 and the predetermined upper limit value being 300, only clusters of pixels with 8 or more and 300 or less pixels are extracted. Thereby, it becomes possible to remove noise where the pixel cluster is too small and things other than silica where the pixel cluster is too large.

[0017] The centroid identification unit 12 identifies the centroid of the cluster of pixels extracted by the central extraction unit 16. FIG. 10 is a diagram showing the identified centroid with a cross mark and superimposed on the original SEM image. The black - framed portion in the upper image of FIG. 10 is shown enlarged in the lower enlarged view of FIG. 10. In the lower enlarged view of FIG. 10, the centroid of the white - system pixels in FIG. 9 is represented by a black cross.

[0018] The evaluation value calculation unit 13 calculates a dispersion evaluation value of silica based on the plurality of centroids specified by the centroid specification unit 12. Specifically, the evaluation value calculation unit 13 specifies a line connecting the closest centroids based on the plurality of centroids. FIG. 11 is a diagram showing in white a line connecting the centroids (black crosses) at the shortest distance. The length of each line is the nearest neighbor distance. When the number of specified lines is N, the nearest neighbor distance, which is the length of each line, is X k represented by. k is a natural number from 1 to N, and is represented by k = 1 to N. X av is the average value of the nearest neighbor distances X k . The evaluation value calculation unit 13 calculates the coefficient of variation CV as the dispersion evaluation value. The coefficient of variation CV (evaluation value) can be calculated based on the standard deviation S of the nearest neighbor distance and the average value X av of the nearest neighbor distance. The coefficient of variation CV is expressed by the following formula. S indicates the standard deviation. [Formula]

[0019] [Method for calculating dispersion evaluation value of silica] A method for calculating a dispersion evaluation value of silica, which is executed by the above system 1, will be described with reference to FIG. 2.

[0020] First, in step ST1, the SEM image acquisition unit 10 acquires an SEM image, which is a grayscale image obtained by imaging the rubber surface. By executing the next steps ST2 to 4, the pixel block extraction unit 11 extracts a plurality of blocks of pixels having a relatively higher intensity than the surroundings from the SEM image. Specifically, in step ST2, the first image generation unit 14 constituting the pixel block extraction unit 11 generates and acquires a first image with increased intensity of the SEM image. In step ST3, the second image generation unit 15 constituting the pixel block extraction unit 11 generates and acquires a second image obtained by performing a dilation process of dilating a region of pixels having a relatively higher intensity than the surroundings with respect to the SEM image. In step ST4, the center extraction unit 16 constituting the pixel block extraction unit 11 subtracts the intensity of the second image from the first image to extract the central portion of the block of pixels having a relatively higher intensity than the surroundings.

[0021] In the next step ST5, the centroid identification unit 12 identifies the centroid of each extracted pixel block. In the next step ST6, the evaluation value calculation unit 13 calculates a silica dispersion evaluation value based on the identified multiple centroids. According to the above, the silica dispersion evaluation value can be calculated without binarizing the SEM image.

[0022] As described above, a method executed by one or more processors, such as the method for calculating the silica dispersion evaluation value of the present embodiment, extracts a plurality of blocks of pixels having a relatively higher intensity than the surroundings from a SEM image, which is a grayscale image obtained by imaging the rubber surface, identifies the centroid of each of the extracted pixel blocks, and calculates a silica dispersion evaluation value based on the identified multiple centroids. In this way, without binarizing the SEM image, which is a grayscale image, blocks of pixels with a high possibility of silica are extracted, and the silica dispersion evaluation value is calculated based on the centroid of the block. Therefore, noise and information loss due to binarization can be avoided, and since the centroid is used instead of the area, the inclusion of noise can be reduced and the accuracy of the evaluation value can be improved.

[0023] Although not particularly limited, as in the present embodiment, the block of pixels to be extracted may be such that the number of pixels constituting the block is within a predetermined range determined by a predetermined lower limit value and a predetermined upper limit value. In this way, the presence of a predetermined lower limit value can remove noise with too few pixels, and the presence of a predetermined upper limit value can avoid the extraction of another substance that is not silica. Since the dispersion of silica is uniform locally, it is considered unnecessary to extract all silica, and an appropriate silica dispersion evaluation value can be calculated.

[0024] Although not particularly limited, as in the present embodiment, a first image with an increased intensity of the SEM image is acquired, a second image obtained by performing a dilation process of expanding a region of pixels having a relatively higher intensity than the surroundings with respect to the SEM image is acquired, and by subtracting the intensity of the second image from the first image, the central portion of the block of pixels having a relatively higher intensity than the surroundings is extracted. Suitable specific examples can be cited.

[0025] Although not particularly limited, as in the present embodiment, based on a plurality of centroids, a plurality of nearest neighbor distances, which are the lengths of lines connecting the nearest centroids, may be specified, and a dispersion evaluation value of silica may be calculated based on the standard deviation of the nearest neighbor distances and the average value of the nearest neighbor distances. Suitable specific examples can be cited.

[0026] The system according to the present embodiment includes one or more processors that execute the above method.

[0027] The program according to the present embodiment is a program that causes one or more computers to execute the above method. By executing these programs, it is also possible to obtain the operational effects exhibited by the above method.

[0028] As described above, the embodiments of the present disclosure have been described with reference to the drawings. However, the specific configuration should be considered not to be limited to these embodiments. The scope of the present disclosure is shown not only by the description of the above embodiments but also by the claims, and further includes all modifications within the meaning and scope equivalent to the claims.

[0029] (1) In the above embodiment, the pixel block extraction unit 11 is composed of the first image generation unit 14, the second image generation unit 15, and the center extraction unit 16. However, as long as pixels with relatively higher intensity than the surroundings can be extracted, it is not limited to this configuration.

[0030] (2) In the above embodiment, the coefficient of variation CV is obtained as the dispersion evaluation value of silica. However, as long as the extracted centroids are used, other evaluation values may be adopted.

[0031] It is possible to adopt the structure employed in each of the above embodiments in any other embodiment. The specific configuration of each part is not limited to only the above-described embodiments, and various modifications are possible without departing from the spirit of the present disclosure.

[0032] For example, in the claims, the specification, and the drawings, the execution order of each process such as operations, procedures, steps, and stages in the apparatus, system, program, and method shown can be realized in any order as long as the output of the previous process is not used in the subsequent process. Regarding the flow in the claims, the specification, and the drawings, even if terms such as "first" and "next" are used for convenience in the description, it does not mean that it is essential to execute in this order.

[0033] Each part shown in FIG. 1 is realized by executing a predetermined program on one or more processors, but each part may be constituted by a dedicated memory or a dedicated circuit. Although each part of the system 1 in the above embodiment is implemented in the processor 1a of one computer, each part may be distributed and implemented in a plurality of computers or in the cloud. That is, the above method may be executed on one or more processors.

[0034] The system 1 includes a processor 1a. For example, the processor 1a can be a central processing unit (CPU), a microprocessor, or any other processing unit capable of executing computer-executable instructions. Also, the system 1 includes a memory 1b for storing the data of the system 1. In one example, the memory 1b includes a computer storage medium, including RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, DVD or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired data and can be accessed by the system 1.

Explanation of Reference Numerals

[0035] 1...System, 10...SEM image acquisition unit, 11...Pixel block extraction unit, 12...Centroid determination unit, 13...Evaluation value calculation unit.

Claims

1. A method executed by one or more processors, comprising: obtaining a first image with increased intensity of a SEM image, which is a grayscale image of a rubber surface; obtaining a second image obtained by performing a dilation process of a morphological operation on the SEM image; extracting a plurality of clusters of white pixels, which are single particles or aggregates of particles of silica, obtained by subtracting the intensity of the second image from the intensity of the first image; identifying the center of gravity of each of the extracted clusters of white pixels; calculating a dispersion evaluation value of silica based on the plurality of identified centers of gravity. A method for calculating a dispersion evaluation value of silica.

2. The method according to claim 1, wherein the cluster of white pixels to be extracted has the number of pixels constituting the cluster of white pixels within a predetermined range determined by a predetermined lower limit value and a predetermined upper limit value.

3. Based on the plurality of centers of gravity, a plurality of nearest neighbor distances, which are the lengths of lines connecting the nearest centers of gravity, are identified, and the dispersion evaluation value of the silica is calculated based on the standard deviation of the nearest neighbor distances and the average value of the nearest neighbor distances. The method according to claim 1 or 2.

4. A system comprising one or more processors configured to execute the method according to any one of claims 1 to 3.

5. A program for causing one or more processors to execute the method according to any one of claims 1 to 3.

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