Method, system, and program for calculating silica dispersion evaluation value

Abstract

To provide a method, system, and program for computing a silica dispersion evaluation value, which offer improved evaluation value accuracy by binarizing an SEM image with an appropriate threshold.SOLUTION: A silica dispersion evaluation value computation method is provided, comprising computing a threshold for binarizing an SEM image, a captured grayscale image of a rubber surface, for each of a plurality of first areas defined in the SEM image on the basis of an intensity distribution of the SEM image, binarizing the SEM image according to the computed thresholds, and computing a silica dispersion evaluation value based on the binarized image.SELECTED DRAWING: Figure 2

Description

[Technical Field]

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

[0002] To evaluate the dispersion of silica in rubber, human eyes are used to evaluate the dispersion based on scanning electron microscope (SEM) images, taking advantage of the fact that silica appears strong (white) in the images. However, manual evaluation is subject to variations in experience and individual differences, so a quantitative evaluation method is desired.

[0003] Paragraph 0056 of Patent Document 1 describes that the average aggregate area of ​​vulcanized rubber is determined. Patent Document 1 also describes that the SEM image is binarized using a threshold determined by the Otsu method, and the aggregate area of ​​the silica portion is calculated based on the binarized image. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 5894583 Summary of the Invention [Problem to be solved by the invention]

[0005] However, SEM images of rubber surfaces show different intensity variations depending on the imaging conditions, even when the object being imaged is the same rubber, and it is thought that the accuracy of the evaluation value will be impaired unless appropriate binarization is performed. The Otsu method described in Patent Document 1 can determine a relatively appropriate threshold for binarization if the image has two peaks in its intensity histogram, but SEM images of rubber surfaces do not have a clear intensity peak, and the Otsu method cannot determine an appropriate threshold. No method has been proposed to determine an appropriate threshold for binarizing SEM images of rubber, not just the Otsu method.

[0006] The present disclosure provides a method, system, and program for calculating a silica dispersion evaluation value that can improve the accuracy of the evaluation value by binarizing an SEM image with an appropriate threshold value. [Means for solving the problem]

[0007] The method for calculating a silica dispersion evaluation value of the present disclosure is a method executed by one or more processors, which calculates, for each of a plurality of first regions set in an SEM image, which is a grayscale image of a rubber surface, a threshold value for binarizing the SEM image based on the intensity distribution of the SEM image, binarizes the SEM image based on the calculated threshold value, and calculates a silica dispersion evaluation value based on the binarized image. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a block diagram showing a system according to an embodiment of the present invention. [Figure 2] 1 is a flowchart showing a process executed by the system. [Figure 3] Figure showing an SEM image of a rubber surface. [Figure 4] FIG. 4 is a graph showing an intensity histogram of the image main body portion shown in FIG. 3. [Figure 5] 10A and 10B are diagrams illustrating an example of determining a threshold value for a pixel of interest. [Figure 6] FIG. 1 shows an image binarized using the method of the present application (upper right image) and an image binarized using one threshold calculated by Otsu's method (lower right image). [Figure 7] FIG. 10 is a diagram showing second regions set so as not to overlap each other. [Figure 8] FIG. 10 is a diagram showing the process of setting second regions that are allowed to partially overlap with each other. [Figure 9] A figure showing a histogram of the number of white pixels in the second regions in an example where the second regions shown in Figure 7 do not overlap each other, with a solid line, and a histogram of the number of white pixels in the second regions in an example where the second regions shown in Figure 8 overlap each other, with a dotted line. DETAILED DESCRIPTION OF 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 the silica dispersion evaluation value from an SEM image of a rubber surface. The SEM image is a grayscale image captured with an electron microscope. In a grayscale image, each pixel has an intensity, and the intensity represents shades of black. For example, the intensity gradation is 256 for 8 bits and 65536 for 16 bits. Low intensity is black, and high intensity is white. Single silica particles or particle aggregates appear as relatively white pixels in the SEM image.

[0011] As shown in FIG. 1, the system 1 includes an SEM image acquisition unit 10, a first region setting unit 11, a threshold calculation unit 12, a binarization execution unit 13, a second region setting unit 14, and an evaluation value calculation unit 15. These units 10-15 are realized by software and hardware working together as the processor 1a executes a processing routine shown in FIG. 2, which is pre-stored in a computer equipped with a processor 1a, a memory 1b, various interfaces, etc. In this embodiment, each unit is realized by the processor 1a in a single device, but this is not limited to this. For example, the processing may be distributed over a network, with multiple processors executing the processing of each unit. That is, one or multiple processors execute the processing. The memory 1b stores the SEM image, the threshold value for each pixel of the SEM image, the binarized image, position information of the second region (coordinates, identifier of the second region), the number of white pixels in the second region, the silica dispersion evaluation value, etc.

[0012] The SEM image acquisition unit 10 acquires grayscale SEM images of the rubber surface and stores them in memory 1b. Figure 3 shows an SEM image of the rubber surface, a grayscale image in which intensity is expressed in 16-bit gradations. The imaging conditions are listed at the bottom of Figure 3, but binarization and calculation of the silica dispersion evaluation value are performed on the main image excluding the bottom portion. Hereafter, SEM images other than those in Figure 3 or images obtained by binarizing SEM images refer to images corresponding to the main image excluding the bottom portion of Figure 3. Figure 4 shows an intensity histogram of the main image portion shown in Figure 3. The horizontal axis of Figure 4 represents intensity, and the vertical axis represents the number of pixels. The left end of the horizontal axis of Figure 4 represents 0 (black), and the right end of the horizontal axis represents 65535 (white). As shown in Figure 4, the intensity distribution of the SEM image of the rubber surface is uniform, with no clear peak, making it difficult to determine a single appropriate threshold value that applies to the entire image.

[0013] The first region setting unit 11 sets a plurality of first regions in the SEM image. In this embodiment, the first region setting unit 11 sets one pixel as one first region. In other words, the number of first regions is the number of vertical pixels x the number of horizontal pixels in the SEM image.

[0014] The threshold calculation unit 12 calculates a threshold for each of a plurality of first regions (one pixel in this embodiment) set in the SEM image based on the intensity distribution of the SEM image. The threshold is used to binarize the SEM image. In this embodiment, the threshold calculation unit 12 calculates a threshold for each pixel of the SEM image based on the intensities of a predetermined number of pixels surrounding the pixel. For example, as shown in FIG. 5, for a pixel of interest G ij An example of determining the threshold value of is explained below. i indicates the position of the pixel on the horizontal axis (for example, it means the i-th pixel on the horizontal axis with the lower left as the origin). j indicates the position of the pixel on the vertical axis (for example, it means the j-th pixel on the vertical axis with the lower left as the origin). In the example of FIG. 5, the target pixel G ij The predetermined number of surrounding pixels for determining the threshold value of the pixel of interest G ijThe pixels are those within a range (frame indicated by a dashed line in FIG. 5) of 1 / 8 of the size of the entire SEM image with the target pixel G at the center. In this embodiment, the threshold calculation unit 12 determines the range of a predetermined number of pixels around the target pixel G based on the ratio to the size of the entire SEM image, but is not limited to this. For example, the threshold calculation unit 12 may determine the range of a predetermined number of pixels around the target pixel G based on which the threshold is determined. ij The pixel may be a pixel within a range of a predetermined number (for example, 100 pixels x 100 pixels) centered on the pixel G ij Alternatively, the pixels may be within a range of a circle having a predetermined radius with the center at .

[0015] In this embodiment, the threshold calculation unit 12 calculates the threshold value of the target pixel G ij Among the pixels within a predetermined number (predetermined range) of pixels surrounding the pixel G ij The threshold is determined based on the average value of the intensities of pixels other than G. Specifically, the threshold is set to t times the average value (t is a number greater than 0 and less than 1). In this embodiment, t=0.4, which means that 40% of the average value is the threshold, but t can be changed appropriately within the above range. In this way, a threshold is calculated for each pixel. The number of calculated thresholds is the same as the number of pixels. For each pixel G ij and threshold Th ij corresponds to the threshold value Th ij is pixel G ij indicates that the threshold corresponds to

[0016] The binarization execution unit 13 binarizes the SEM image based on the threshold calculated by the threshold calculation unit 12. Specifically, the binarization execution unit 13 determines whether each pixel constituting the SEM image should be binary (black or white) based on the corresponding threshold (determination process), and converts the intensity of each pixel into one of the binary values ​​based on the determination result (conversion process). Specifically, the target pixel G ij The intensity of the target pixel G ij The corresponding threshold Th ij If it is smaller than ij is black. ij The intensity of the target pixel G ij The corresponding threshold Th ij If it is equal to or greater than this, the pixel of interest G ijis set to white. The binarization execution unit 13 executes the threshold-based determination process and the conversion process for all pixels that make up the SEM image. This completes the process of converting the SEM image into a binary image. Figure 6 shows an image binarized by the method of the present application (upper right image) and an image binarized with a single threshold calculated by the Otsu method (lower right image). The upper and lower left images in Figure 6 are the same as the main image shown in Figure 3 and are SEM images.

[0017] The second region setting unit 14 divides the binarized image to set multiple second regions. As shown in FIG. 7, multiple second regions Ar2 may be set for the binarized image so that the second regions Ar2 do not overlap each other, or as shown in FIG. 8, the second regions Ar2 may be set so that some overlap is permitted. In the example of FIG. 7, one binarized image is divided into 64 regions, and 64 second regions Ar2 are set. The second regions Ar2 are smaller than the binarized image and contain multiple pixels. Since the number of second regions Ar2 after setting will be too large to illustrate, FIG. 8 shows the process of setting the second regions Ar2. When overlap is permitted for the second regions shown in FIG. 8, each second region is shifted by sliding it vertically and horizontally by five pixels. The amount of shift (overlap amount) of the second regions Ar2 is not limited to five pixels and can be changed as long as it is uniform within the image. The number of second regions Ar2 when overlapping of the second regions Ar2 shown in Fig. 8 is permitted is greater than the number of second regions Ar2 when overlapping of the second regions Ar2 is not permitted as shown in Fig. 7. The size of the second region Ar2 may be u times the size of the binarized image (u is a number greater than 0 and less than 1).

[0018] The evaluation value calculation unit 15 counts the white pixels for each second area Ar2 set by the second area setting unit 14, and calculates the silica dispersion evaluation value based on the number of white pixels in each second area Ar2. As a specific example, if the number of second areas is N, the white pixels in each of the N second areas are counted. For k=1 to N, the number of white pixels in the k-th second area is calculated as X kThe number of white pixels in each second region is X1, X2, ..., X N-1 ,X N It is expressed as: In this embodiment, the evaluation value calculation unit 15 calculates the coefficient of variation CV as a dispersion evaluation value. The coefficient of variation CV (evaluation value) is calculated by multiplying the number of white pixels (X k The coefficient of variation CV can be calculated based on the following formula: S is the standard deviation, and X is the av indicates the average number of white pixels in all second regions.

number

[0019] Figure 9 shows a histogram of the number of white pixels in the second regions for an example in which the second regions shown in Figure 7 do not overlap, as shown in Figure 7, and a histogram of the number of white pixels in the second regions for an example in which the second regions overlap, as shown in Figure 8, as shown in Figure 7, using a dot-dash line. The horizontal axis represents the number of white pixels in one second region, and the vertical axis represents the number of second regions. Comparing the dot-dash line and the solid line, the distorted distribution shape of the solid line is smoothed out and approaches a symmetrical shape in the dot-dash line. Increasing the number of second regions when performing variance evaluation is thought to increase the statistical amount and improve the accuracy of the variance evaluation value. Comparing the dot-dash line and the solid line reveals that the variance of the white pixels is similar, indicating that accurate evaluation is possible using either method of setting the second regions.

[0020] [Calculation method for silica dispersion evaluation value] The method of calculating the dispersion evaluation value of silica, which is executed by the above system 1, will be described with reference to FIG.

[0021] First, in step ST1, the SEM image acquisition unit 10 acquires an SEM image, which is a grayscale image of a rubber surface. In the next step ST2, the first region setting unit 11 sets multiple first regions in the SEM image. In this embodiment, one pixel represents one first region, so the process of setting the first regions is not separated from the threshold calculation process in step ST3, but is performed integrally.

[0022] In the next step ST3, the threshold calculation unit 12 calculates a threshold for binarizing the SEM image for each of the plurality of first regions in the SEM image based on the intensity distribution of the SEM image. In the next step ST4, the binarization execution unit 13 binarizes the SEM image based on the calculated threshold to obtain a binarized image.

[0023] In the next step ST5, the second region setting unit 14 sets a plurality of second regions in the binarized image. In step ST6, the evaluation value calculation unit 15 calculates a silica dispersion evaluation value based on the number of white pixels in each second region.

[0024] As described above, the method for calculating the silica dispersion evaluation value of this embodiment may be a method executed by one or more processors, which calculates a threshold value for binarizing the SEM image for each of a plurality of first regions set in the SEM image, which is a grayscale image of the rubber surface, based on the intensity distribution of the SEM image, binarizes the SEM image based on the calculated threshold value, and calculates the silica dispersion evaluation value based on the binarized image. In this way, instead of using one threshold for the entire SEM image, thresholds are calculated for each of multiple regions set within the SEM image based on the intensity distribution. Compared to binarizing an image using a single threshold, this makes it possible to take into account the intensity distribution that can change with each image capture and the intensity distribution within the image, thereby improving the accuracy of the silica dispersion evaluation value.

[0025] Although not particularly limited, as in this embodiment, the size of the first region may be one pixel, and the threshold may be calculated for each pixel of the SEM image based on the intensities of a predetermined number of pixels surrounding it. In this way, a threshold is calculated for each pixel based on the intensity distribution around it, and the SEM image is binarized using the threshold calculated for each pixel, making it possible to appropriately binarize the SEM image based on the intensity distribution around it.

[0026] Although not particularly limited, as in this embodiment, the binarized image may be divided into a plurality of second regions, the white pixels may be counted for each second region, and the silica dispersion evaluation value may be calculated based on the number of white pixels in each second region. By dividing the binarized image into multiple second regions and then evaluating the dispersion, the degree of bias in the white regions within the entire image can be evaluated with high accuracy, thereby improving the accuracy of the silica dispersion evaluation value.

[0027] Although not particularly limited, the plurality of second regions may partially overlap each other, as in this embodiment. In this way, the number of second regions increases, and the number of white pixels in each local second region increases, so that the accuracy of the silica dispersion evaluation value can be improved.

[0028] The program according to this embodiment is a program that causes one or more computers to execute the above method. By executing these programs, it is possible to obtain the effects of the above-mentioned methods.

[0029] Although the embodiments of the present disclosure have been described above with reference to the drawings, the specific configurations should not be considered to be limited to these embodiments. The scope of the present disclosure is defined not only by the description of the above embodiments but also by the claims, and further includes all modifications within the meaning and scope of the claims.

[0030] (1) In the above embodiment, the first region setting unit 11 sets one first region for one pixel, but this is not limiting. For example, the first region setting unit 11 may set one first region for a pixel block of two or more pixels.

[0031] (2) In the above embodiment, the threshold calculation unit 12 determines the threshold for one pixel based on the average value of a predetermined number of surrounding pixels (a predetermined range), but the threshold is not limited to the average value as long as it is a statistical value of a plurality of pixels. For example, the threshold may be a mode value, a median value, or the like.

[0032] (3) When the binarization method of the above embodiment is implemented, the method for calculating the silica dispersion evaluation value of the binarized image is not limited to the method described in this specification, and various methods can be adopted.

[0033] The structures employed in the above-described embodiments can be employed in any other embodiment. The specific configurations of the components are not limited to the above-described embodiments, and various modifications are possible within the scope of the present disclosure.

[0034] For example, the order of execution of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings, can be implemented in any order, as long as the output of a previous process is not used in a subsequent process. Even if the flow in the claims, specifications, and drawings is explained using terms such as "first" and "next" for convenience, this does not mean that the processes must be executed in this order.

[0035] 1 are realized by executing a predetermined program on one or more processors, but each unit may also be configured with a dedicated memory or dedicated circuit. In the system 1 of the above embodiment, each unit is implemented in the processor 1a of a single computer, but each unit may be distributed and implemented on multiple computers or in the cloud. In other words, the above method may be executed on one or more processors.

[0036] System 1 includes a processor 1 a. For example, processor 1 a can be a central processing unit (CPU), a microprocessor, or other processing unit capable of executing computer-executable instructions. System 1 also includes memory 1 b for storing data for system 1. In one example, memory 1 b includes computer storage media, such as RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, DVD or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device, or any other medium that can be used to store desired data and that can be accessed by system 1. [Explanation of symbols]

[0037] Ar2...Second area.

Claims

1. 1. A method executed by one or more processors, comprising: In an SEM image, which is a grayscale image of a rubber surface, one pixel is set as one first region; calculating the threshold value for binarizing the SEM image based on an average value of intensities of pixels other than the target pixel among a predetermined number of pixels surrounding the target pixel for which a threshold value is determined, the pixel being in the first region; binarizing the SEM image based on the calculated threshold value; A method for calculating a dispersion evaluation value of silica, which comprises dividing the binarized image to set a plurality of second regions, counting white pixels for each of the set second regions, and calculating a dispersion evaluation value of silica based on the number of white pixels in each of the second regions.

2. The method of claim 1 , wherein the second regions partially overlap each other.

3. A system comprising one or more processors for carrying out the method of claim 1 or 2.

4. A program that causes one or more processors to execute the method according to claim 1 or 2.

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