Image Processing Method

Abstract

To provide an image processing method in which a contour of a tire cross-sectional image hardly changes even if expansion and contraction processing is executed.SOLUTION: An image processing method of an embodiment is a method for processing a cross-sectional image of a tire including the steps of: acquiring an entire image including a tire cross-sectional image; storing image data of a predetermined range in the entire image as image data before expansion and contraction processing; performing the expansion and contraction processing on the entire image; and replacing the image data of the predetermined range in the image after the expansion and contraction processing with the stored image data before the expansion and contraction processing.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to an image processing method. [Background technology]

[0002] It is known that an expansion / contraction process described in Patent Document 1 is performed to remove noise from an image. The expansion / contraction process consists of an expansion process and an erosion process. The number of times the expansion / contraction process is performed and the order in which the expansion / contraction process is performed are determined based on the characteristics of the noise in the image, the purpose of the expansion / contraction process, etc.

[0003] For example, if an object in an image has many missing parts and you want to remove those parts, an expansion process is performed first, followed by a contraction process. The expansion process expands the image of the object to fill in the missing parts, and the contraction process returns the image of the object to its original size. However, after the contraction process, the original missing parts are gone from the image of the object. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2020-19437 Summary of the Invention [Problem to be solved by the invention]

[0005] However, when an expansion / contraction process is performed on a tire cross-sectional image or the like, the contours of the tire cross-sectional image or the like may change. For example, in a cross-sectional image of a tire mounted on a rim, there is a gap between the rim flange portion of the rim cross-sectional image and the tire cross-sectional image. If this gap is narrow, it can be said to be the same as a chip, and it will be filled by the expansion / contraction process. When this gap is filled in this way, the contours of the rim cross-sectional image and the tire cross-sectional image will change.

[0006] Therefore, an object of the present invention is to provide an image processing method that prevents changes in the contour of a tire cross-sectional image or the like even when inflation / deflation processing is performed. [Means for solving the problem]

[0007] The present invention includes the embodiments shown below.

[0008] [1] A method for processing a cross-sectional image of a tire, comprising the steps of: acquiring an entire image including a cross-sectional image of the tire; saving image data of a predetermined range within the entire image as image data before inflation / deflation processing; performing inflation / deflation processing on the entire image; and replacing the image data of the predetermined range of the image after inflation / deflation processing with the saved image data before inflation / deflation processing.

[0009] [2] The image processing method according to [1], wherein the tire cross-sectional image is a cross-sectional image of a tire attached to a rim, and the specified range is a range including the gap between the flange of the rim and the tire. [Effects of the Invention]

[0010] According to this embodiment, image data within a predetermined range of the image after the expansion / contraction processing is replaced with image data before the expansion / contraction processing, so that the contours of tire cross-section images, etc., are less likely to change even when the expansion / contraction processing is performed. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram showing the configuration of an image processing apparatus. [Figure 2] (a) is the whole image before brightness adjustment. (b) is the whole image after brightness adjustment. [Figure 3] (a) is the whole image before rotation. (b) is the whole image after rotation. [Figure 4]Enlarged view of the bead core area of ​​a cross-sectional image of a tire with rim. (a) is the image before binarization. (b) is the image after binarization and before expansion / contraction. (c) is the image after the first expansion process has been performed on image (b). (d) is the image after the first contraction process has been performed on image (c). [Figure 5] FIG. 2 is a simplified diagram showing the contour lines of a cross-sectional image of a tire with rim. [Figure 6] Figures showing the contour lines before and after inflation / deflation processing. (a) is a cross-sectional image of a tire with a rim before inflation / deflation processing. (b) is a diagram showing the contour lines of the tread after inflation / deflation processing. [Figure 7] 10 is a partial enlarged view of a tire cross-sectional image. [Figure 8] 1 is a flowchart of image processing. [Figure 9] 10 is a flowchart of brightness adjustment. [Figure 10] 10 is a flowchart of a process for rotating an image. [Figure 11] 10 is a flowchart of noise processing. [Figure 12] Flowchart of outer and inner contour classification. [Figure 13] Flowchart for creating a model for length measurement. [Figure 14] 10 is a flowchart for calculating tire thickness. [Figure 15] A cross-sectional image of a tire with a rim, with the tire cross-sectional image on top and the rim cross-sectional image on the bottom. DETAILED DESCRIPTION OF THE INVENTION

[0012] The following description of the embodiments will be given with reference to the accompanying drawings. Note that the embodiments described below are merely examples, and any modifications that do not depart from the spirit of the present invention are included within the scope of the present invention.

[0013] 1 shows an image processing device 10 of this embodiment. The image processing device 10 of this embodiment is a device that automatically measures the dimensions of a predetermined portion in a cross-sectional image of a pneumatic tire (hereinafter referred to as a "tire cross-sectional image").

[0014] The tire cross-sectional image in this embodiment is a cross-sectional image of a pneumatic tire (hereinafter referred to as "tire"), and is an image obtained by taking a plane passing through a line perpendicular to the outer surface of the tire and the tire rotation axis as a cross section. Furthermore, unless otherwise specified, the tire axial direction in the description of the tire cross-sectional image refers to the extension direction of the tire rotation axis. Furthermore, in the tire cross-sectional image, the tire radial direction coincides with the direction perpendicular to the tire axial direction.

[0015] The tire cross-sectional image in this embodiment is an image taken by a CT (Computed Tomography) device that uses X-rays.

[0016] The image processing device 10 is realized by a computer including a processing unit, a storage unit, an input unit, and a display unit. The storage unit includes a RAM (Random Access Memory), a ROM (Read Only Memory), and an HDD (Hard Disk Drive). The storage unit stores a program for executing this embodiment, data of tire cross-sectional images captured by a CT device, image data after various processes described below, and the like.

[0017] The processing unit is composed of a CPU (Central Processing Unit) and the like. The processing unit executes the image processing of this embodiment by reading a program stored in a ROM or the like onto a RAM and executing it. The input unit is, for example, a mouse and keyboard, and accepts input from a user of the image processing device 10 (hereinafter referred to as "user"). The display unit is, for example, a display, and displays various data such as tire cross-sectional images, an input screen by the input unit, and the like.

[0018] The program may be distributed by being stored on a computer-readable storage medium such as a CD-ROM, CD-R, or USB, or by being stored on a computer connected to the Internet and being downloaded via the Internet.

[0019] The image processing device 10 is realized by the processing unit executing the program as described above. As shown in Fig. 1, the image processing device 10 functionally includes a grayscale conversion unit 11, a brightness adjustment unit 12, an image rotation unit 13, a binarization processing unit 14, a noise processing unit 15, a contour detection unit 16, a contour classification unit 17, a length measurement model creation unit 18, and a length calculation unit 19.

[0020] The CT device captures a cross-sectional image of the tire mounted on the rim. The image captured is a cross-sectional image of the tire mounted on the rim at any position in the tire circumferential direction. As a result of the capture, a cross-sectional image of the rim portion on the same cross section (hereinafter referred to as the "rim cross-sectional image") is also acquired along with the tire cross-sectional image. The entire captured image, including the tire cross-sectional image and the rim cross-sectional image, is referred to as the "full image." Furthermore, the tire cross-sectional image and the rim cross-sectional image combined together is referred to as the "rim-attached tire cross-sectional image." The proportion (area ratio) of the rim-attached tire cross-sectional image in the entire image is preferably 5% or more and 15% or less, and more preferably 8% or more and 12% or less.

[0021] In the overall image taken by a CT scanner, areas that absorb X-rays more easily appear brighter, and areas that transmit X-rays more easily appear darker. Therefore, metal parts such as the bead core and rim appear brighter, while rubber parts (hereinafter referred to as "rubber parts") appear darker. The brightness of rubber parts varies depending on the type of tire, as the X-ray transmittance varies depending on the compound. The brightness of metal parts also varies depending on the type of metal. Furthermore, empty areas appear black or dark.

[0022] The grayscale conversion unit 11 shown in Fig. 1 converts the entire image captured by a CT device and input into the image processing device 10 into a grayscale image. Through this conversion, the entire image becomes an image with 256 gradations from 0 to 255, with black being 0 and white being 255. In the following explanation, the numerical value representing the gradation is referred to as brightness. High brightness means closer to white, and low brightness means closer to black.

[0023] The entire grayscale image is made up of point cloud data. The point cloud data includes at least the coordinates and brightness data of each of the numerous points (pixels) that form the image. The size of the entire image is 1000 pixels or more in both length and width. A specific example is 1200 pixels in length and 1800 pixels in width. In the description of this embodiment, the pixels that make up the image may be referred to as points.

[0024] The brightness adjustment unit 12 shown in Fig. 1 adjusts the brightness of the grayscale tire cross-sectional image to a reference brightness (hereinafter referred to as "reference brightness"). As shown in Fig. 1, the brightness adjustment unit 12 includes a tire image extraction unit 12a and a brightness change unit 12b.

[0025] The tire image extraction unit 12a extracts rubber portions of the tire cross-sectional image from the entire image. The rubber portions include tread rubber, sidewall rubber, bead filler, belt topping rubber, etc. As a result of investigating cross-sectional images of various tires, the brightness levels of the rubber portions of the tire cross-sectional image in the entire image are known in advance. Therefore, the tire image extraction unit 12a extracts the rubber portions of the tire cross-sectional image by extracting portions of the entire image that fall within a predetermined brightness range. In this case, the predetermined range is, for example, a brightness (a numerical value representing the gradation when the entire image is expressed in 256 gradations as described above) of 30 or more and 150 or less.

[0026] When the rubber portion of the tire cross-sectional image is extracted, the brightness modification unit 12b first calculates the average brightness of the rubber portion. The average brightness is the sum of the brightness of all pixels that make up the calculation range divided by the number of pixels that make up the calculation range. Therefore, the average brightness of the rubber portion is the sum of the brightness of all pixels that make up the rubber portion divided by the number of pixels that make up the rubber portion. Note that when image processing is to be performed on multiple tire cross-sectional images, the average brightness is calculated for each tire cross-sectional image.

[0027] Next, the brightness modification unit 12b uses the average brightness of the rubber parts to perform a process of bringing the brightness of the tire cross-sectional image closer to the reference brightness. To do this, the brightness modification unit 12b sets a value obtained by dividing the reference brightness by the average brightness of the rubber parts (i.e., (reference brightness) / (average brightness of the rubber parts)) as a scaling coefficient. Then, for each of all pixels in the rubber parts in the overall image, the brightness modification unit 12b performs a calculation (scaling calculation) to multiply the pixel brightness by the scaling coefficient. Through the scaling calculation, the brightness of the rubber parts approaches the reference brightness.

[0028] In addition, there is almost no variation in brightness between tires or between images taken in the rim cross-sectional image and the background, so there is little need to perform scaling calculations on the rim cross-sectional image and the background. Therefore, in this embodiment, scaling calculations are performed only on the rubber portion of the entire image.

[0029] As the brightness of the tire cross-sectional image approaches the reference brightness, if the tire cross-sectional image was originally relatively dark, it will become brighter overall, and if it was originally relatively bright, it will become darker overall. For example, an overall image that was originally dark as shown in Figure 2(a) will change to a bright image as shown in Figure 2(b). In this way, as the brightness of the tire cross-sectional image approaches the reference brightness, all tire cross-sectional images will have approximately the same brightness.

[0030] The image rotation unit 13 shown in Fig. 1 rotates the entire image on the plane so that the tire axial direction on the tire cross-sectional image (hereinafter referred to as the "tire axial direction" and shown by arrow D in Fig. 3) is parallel to the reference line. At this time, the image rotation unit 13 rotates the entire image so that the tire cross-sectional image is on top and the rim cross-sectional image is on the bottom. Here, the reference line is the X coordinate axis.

[0031] The rimmed tire cross-sectional image is represented in an orthogonal coordinate system consisting of an X-coordinate axis and a Y-coordinate axis (shown in Figure 3(a)) that are orthogonal to each other. However, as shown in Figure 3(a), immediately after being imported into the image processing device 10, the axial direction of the rimmed tire cross-sectional image is tilted relative to the X-coordinate axis, which serves as the reference line. Therefore, as described above, the image rotation unit 13 rotates the entire image so that the axial direction of the tire cross-sectional image becomes parallel to the X-coordinate axis, which serves as the reference line.

[0032] As shown in FIG. 1, the image rotation unit 13 includes a bead core detection unit 13a and a rotation unit 13b.

[0033] The bead core detection unit 13a detects bead cores from within the overall image. In the overall image, the bead core is the part with the highest brightness. Therefore, the bead core detection unit 13a detects parts with brightness equal to or greater than a predetermined threshold as bead cores. For example, the threshold is set to 250, which is a gradation value when the overall image is expressed in 256 gradations.

[0034] Since bead cores are located on both the serial side and the anti-serial side of the tire, the bead core detection unit 13a detects two bead cores from the overall image. The serial side refers to the side of the tire in the axial direction where the serial is displayed on the surface. The anti-serial side refers to the side opposite the serial side in the axial direction of the tire.

[0035] Furthermore, the bead core detection unit 13a calculates the coordinates of the center of gravity of each bead core on the serial side and the anti-serial side. The X coordinate of the center of gravity of the bead core is obtained by dividing the sum of the X coordinates of each pixel that forms the bead core by the number of pixels that form the bead core. The Y coordinate of the center of gravity of the bead core is obtained by dividing the sum of the Y coordinates of each pixel that forms the bead core by the number of pixels that form the bead core. Using this calculation method, the bead core detection unit 13a calculates the coordinates of the center of gravity of each bead core on the serial side and the anti-serial side.

[0036] First, the rotation unit 13b of the image rotation unit 13 finds a straight line connecting the centers of gravity of the bead cores on the serial side and anti-serial side. If the coordinates of the center of gravity of the bead core on the serial side are (x1, y1) and the coordinates of the center of gravity of the bead core on the anti-serial side are (x2, y2), the straight line can be expressed by the following equation.

[0037]

number

[0038]

number

[0039] Once the rotation angle θ' is determined in this way, the rotation unit 13b rotates the entire image including the rim-fitted tire cross-sectional image. The rotation angle is the previously determined rotation angle θ'. Because the rotation is performed on the plane of the entire image, the image rotation axis is an axis extending in a direction perpendicular to the planar entire image.

[0040] Due to the rotation, the tire axial direction of the tire cross-sectional image becomes the X coordinate direction, and the tire radial direction of the tire cross-sectional image becomes the Y coordinate direction. In other words, even if the tire axial direction is inclined with respect to the X coordinate direction before rotation as shown in Figure 3(a), after rotation the tire axial direction becomes the X coordinate direction as shown in Figure 3(b). After rotating the entire image, cropping is performed as necessary.

[0041] The binarization processing unit 14 shown in Figure 1 performs binarization processing on the entire image. In the binarization processing, a brightness greater than 0 and less than 255 is set as a threshold value. The method for determining the threshold value can be selected appropriately. Pixels with brightness greater than the threshold value are converted into white pixels with a signal value of 1. Pixels with brightness less than the threshold value are converted into black pixels with a signal value of 0. As a result, the tire cross-sectional image and rim cross-sectional image, which were originally high in brightness, become white, and the background, which was originally low in brightness, becomes black.

[0042] Figure 4(a) is an enlarged view of the bead core area of ​​a cross-sectional image of a tire with rim before binarization. In Figure 4(a), it can be seen that the brightness varies from location to location (coordinate). For example, the bead core and rim areas are bright, the background is dark, and the rubber parts are in the middle of the spectrum. This image is converted into an image consisting of only black and white, as shown in Figure 4(b), by binarization.

[0043] The noise processing unit 15 shown in Figure 1 removes noise from the rimmed tire cross-sectional image after binarization processing. First, the noise processing unit 15 saves the coordinates and signal values ​​of a predetermined range in the rimmed tire cross-sectional image after binarization processing as image data before inflation / deflation processing. This predetermined range is referred to as the "storage range."

[0044] The range to be saved is the range in which the original contour of the rim-attached tire cross-sectional image may be distorted by the subsequent inflation / deflation process. The range to be saved is a range of a predetermined shape, such as a rectangle. The range to be saved may be selected manually by the user or automatically by recognizing the shape. In this embodiment, the range enclosed by the square frame in Figure 4(b), i.e., the range including the gap between the flange and the tire, is selected as the range to be saved.

[0045] After saving the image data in the storage range, the noise processing unit 15 performs an expansion / contraction process on the entire image after the binarization process. In the expansion process, a kernel having a predetermined shape and size is scanned within the image. Then, at each position during the scan, if the kernel centered on the pixel of interest (pixel of interest) contains at least one pixel with a signal value of 1 (white), the signal value of the pixel of interest is set to 1 (i.e., if the signal value of the pixel of interest was originally 1, it remains 1, and if it was 0, it is converted to 1). Therefore, the white area is expanded by the expansion process. The expansion process also eliminates small black noise (chips, etc.).

[0046] In the erosion process, the kernel is scanned within the image. At each position during the scan, if the kernel centered on the pixel of interest contains even one pixel with a signal value of 0 (black), the signal value of the pixel of interest is set to 0 (i.e., if the signal value of the pixel of interest was originally 0, it remains 0, and if it was 1, it is converted to 0). Therefore, the erosion process shrinks white areas. It also eliminates small white noise.

[0047] The noise processing unit 15 first performs an expansion process, followed by an erosion process. The image after the expansion process is shown in Figure 4(c), and the image after the subsequent erosion process is shown in Figure 4(c). Although not shown, by performing erosion after the expansion process, any gaps that existed in the rim-equipped tire cross-sectional image are filled in.

[0048] Next, the noise processing unit 15 performs an erosion process, followed by an expansion process. By performing the expansion process after the erosion process, small white noise that was around the rimmed tire cross-sectional image is removed. In this way, the noise processing unit 15 performs the processes in the order of expansion process, erosion process, erosion process, and expansion process.

[0049] In this embodiment, the shape of the kernel for the expansion / contraction process is, for example, a circle or a diamond, and the size of the kernel (the radius if the kernel is a circle, or the length from the center to the vertex (the vertex farthest from the center) if the kernel is a diamond) is, for example, 10 pixels.

[0050] The above expansion / contraction process removes noise from the entire image. However, in the saved area enclosed by the square frame in Figure 4(b), the outline of the rim-equipped tire cross-sectional image after expansion / contraction is distorted from the outline before expansion / contraction.

[0051] Therefore, the noise processing unit 15 replaces the image data in the above-mentioned saved range within the entire image after the expansion / contraction processing with the saved image data before the expansion / contraction processing. As a result, the signal values ​​of each coordinate in the saved range are returned to the signal values ​​before the expansion / contraction processing. As a result, the image in the saved range becomes the image before the expansion / contraction processing, and the image in other areas becomes the image after the expansion / contraction processing. In the saved range, the outline of the rim-equipped tire cross-sectional image appears correctly, and in other areas, the outline of the rim-equipped tire cross-sectional image appears correctly after noise has been removed. Note that, because the saved range is originally a range with little noise, there is no problem with the image data in the saved range being replaced with image data before the expansion / contraction processing in this way.

[0052] The contour line detection unit 16 shown in Figure 1 detects the contour line of the rim-equipped tire cross-sectional image from the entire image after binarization and noise processing. The detection is performed by a well-known method such as using an edge function. The detected contour line is made up of a group of points. The entire detected contour line is referred to as the "overall contour line."

[0053] Figure 5 shows a simplified representation of the detected overall contour (the overall contour that is actually detected is equivalent to what a person would recognize from an image such as Figure 2, and is smoother than the overall contour in Figure 5). Each square in the figure represents one point (pixel). The gray squares in the figure are points that form the overall contour. The overall contour is basically one pixel thick, but there are some places where it is two pixels thick, such as when B and N101 in the figure are connected vertically.

[0054] The contour line classification unit 17 shown in Fig. 1 classifies the overall contour line into an outer contour line 30 and an inner contour line 31 shown in Figs. 5 and 6. The outer contour line 30 is a contour line that represents the outer surface of the rimmed tire. The inner contour line 31 is a contour line that represents the inner surface of the rimmed tire. The outer contour line 30 and the inner contour line 31 are each annular lines. The inner contour line 31 is located inside the outer contour line 30.

[0055] The contour line classification unit 17 extracts an outer contour line 30 from the point cloud representing the overall contour line. First, the contour line classification unit 17 draws a tire axial center line on the rimmed tire cross-sectional image. The tire axial center line is a straight line that passes through the center coordinate between the maximum and minimum coordinates in the X coordinate direction (tire axial direction) of the overall contour line (however, the overall contour line of the rimmed tire image where the tire axial direction is parallel to the X coordinate axis) and is parallel to the Y coordinate direction (tire radial direction). In Figure 5, the tire axial center line is indicated by a dashed line.

[0056] Next, the contour line classification unit 17 determines the point of intersection between the tire axial centerline and the overall contour line that is the outermost in the tire radial direction (i.e., the point with the largest Y coordinate) as the first outer point. The outer point is a point that forms the outer contour line 30. In Figure 5, the point of intersection between the tire axial centerline and the overall contour line that is the outermost in the tire radial direction is point A.

[0057] Next, the contour classification unit 17 searches for the point (nearest point) that is closest to the first outer point among the points that are included in the point group that forms the entire contour and that have not yet been identified as an outer point, and designates the found nearest point as the second outer point. In Figure 5, such second outer point is point N1.

[0058] Next, the contour classification unit 17 searches for the point (nearest point) that is closest to the second outer point from among the points that are included in the point group that forms the entire contour and that have not yet been identified as outer points, and designates the found nearest point as the third outer point. In Figure 5, such a third outer point is point N3. In this way, the contour classification unit 17 repeatedly designates the nearest point of the previously identified outer point (but a point that has not yet been identified as an outer point) as the next outer point.

[0059] If the coordinates of the previously identified outer point are (x0, y0) and the coordinates of the other points constituting the overall contour are (xn, yn) (n=1, 2, 3, . . .), the distance Ln between the previously identified outer point and the other points is calculated using the following formula: The point with the smallest distance Ln is the closest point to the previously identified outer point.

[0060]

number

[0061] In Figure 5, nearest points N4, N5, ... are identified one after another, and finally point N100 is identified as the nearest point to point N99. Of the points that have not been identified as outer points up until the time point N100 is identified, the closest point to point N100 is point B, but point B is more than two pixels away from point N100 and cannot be selected, so the search for outer points ends when the last nearest point N100 is identified. The outer points identified up to the end of the search are defined as the points that form outer contour 30.

[0062] The contour line classification unit 17 also extracts the inner contour line 31. Of the intersection points between the tire axial center line and the overall contour line, the contour line classification unit 17 determines the second point from the outer side in the tire radial direction as the first inner point. The inner point is a point that forms the inner contour line 31. In Figure 5, of the intersection points between the tire axial center line and the overall contour line, the second point from the outer side in the tire radial direction is point B.

[0063] Next, the contour classification unit 17 searches for the point (nearest point) that is closest to the first inner point among the points that are included in the point group that forms the entire contour and that have not yet been identified as an inner point, and designates the found nearest point as the second inner point. In Figure 5, such second inner point is point N101.

[0064] Next, the contour classification unit 17 searches for the point (nearest point) closest to the second inner point from among the points that are included in the point group that forms the entire contour and that have not yet been identified as inner points, and designates the found nearest point as the third inner point. In FIG. 5, such a third inner point is point N102. In this manner, the contour classification unit 17 repeatedly designates the nearest point (but a point that has not yet been identified as an inner point) of the previously identified inner point as the next inner point. The method for calculating the distance between the previously identified inner point and other points is the same as in Equation 3 above.

[0065] In the process of identifying an interior point, a point that is more than a predetermined distance away from a previously identified interior point is not selected as an interior point. Therefore, even if it is the closest point to a previously identified interior point, a point that is more than a predetermined distance away from the previously identified interior point is not selected as the next interior point. The predetermined distance is, for example, two pixels. This measure prevents points that are far from the actual outer contour 30 (for example, points that form the inner contour 31) from being selected as interior points. Furthermore, this measure causes the contour classification unit 17 to end the search for interior points when the next interior point cannot be identified because the nearest point is more than a predetermined distance (for example, two pixels).

[0066] In Figure 5, nearest points N103, N104, etc. are identified one after another, and finally point N200 is identified as the nearest point to point N199. Of the points that have not been identified as interior points up to this point, the point closest to point N200 is point N100, but point N100 is more than two pixels away from point N200 and cannot be selected, so the search for interior points ends when the last nearest point N200 is identified. The interior points identified up to the end of the search are defined as points that form the interior contour 31. The interior points identified up to the end of the search are defined as points that form the interior contour 31.

[0067] The length measurement model creation unit 18 shown in Figure 1 creates a model (hereinafter referred to as "length measurement model") for measuring the thickness of a tire at a predetermined position. As can be seen from Figure 6(a), in the original tire cross-sectional image, multiple main grooves 34 appear on the outer contour line 30 of the tread portion. When the original tire cross-sectional image is 1200 pixels vertically and 1800 pixels horizontally, the width of the main grooves 34 is, for example, 200 pixels, and the depth of the main grooves 34 is, for example, 50 pixels.

[0068] However, when measuring the tire thickness, the measurement position is generally set to a position on the tire cross section that is a predetermined distance away from a predetermined starting point on the tire outer surface along the tire outer surface. This "predetermined distance" is generally measured while ignoring the main grooves 34 (i.e., assuming that the main grooves 34 do not exist). Therefore, when automatically determining the measurement position for measuring the tire thickness, the presence of the main grooves 34 in the tire cross section image is an obstacle. Therefore, the main grooves 34 are filled in by the length measurement model creation unit 18, and the line representing the tire outer surface in the length measurement model is changed to a single smooth line 35 (see FIG. 6(b)).

[0069] As shown in FIG. 1, the length measurement model creation unit 18 includes a smooth line creation unit 18a and a length calculation unit 18b.

[0070] The smooth line creation unit 18a performs an expansion / contraction process on a selected region in the entire image. Here, the selected region is a sufficiently large region that includes the portion for which the length along the smooth line 35 is to be measured, for example, a region that includes the entire tire tread. This region is selected manually or automatically.

[0071] The shape of the kernel for the expansion / contraction process by the smoothed line creation unit 18a is, for example, a circle or a diamond. The size of the kernel for the expansion / contraction process (the radius if the kernel is a circle, or the length from the center to the vertex (the vertex farthest from the center) if the kernel is a diamond) is a size that can fill the main groove 34 in the tire cross-sectional image in a single expansion / contraction process. Since there is no need to expand or contract beyond the thickness of the tire, the size of the kernel is equal to or less than the thickness from the outer contour line 30 to the inner contour line 31 in the tread. Here, the "thickness" refers to the length in the normal direction of the inner contour line 31. The thickness referred to here is, for example, the thickness on the tire axial centerline. The kernel size corresponds to the amount of expansion in the expansion process and the amount of contraction in the contraction process.

[0072] In this embodiment, the size of the kernel is, for example, 100 pixels. Furthermore, the smooth line creating unit 18a performs the expansion process once and then the contraction process once.

[0073] By this expansion / contraction processing, the main grooves 34 that appeared on the outer contour line 30 in the tire cross-sectional image are filled in, and the outer contour line 30 in the selected region becomes a smooth line 35 as shown in Figure 6(b). Outside the selected region, the outer contour line 30 in the tire cross-sectional image is smooth even without the expansion / contraction processing performed by the smooth line creation unit 18a. Hereinafter, the entire outer contour line 30 in the tire cross-sectional image will be referred to as the smooth line 35.

[0074] The length calculation unit 18b determines the length along the smooth line 35 from the intersection of the smooth line 35 created by the smooth line creation unit 18a and the tire axial center line to each position (point) on the smooth line 35.

[0075] First, as shown in FIGS. 5 and 6, the length calculation unit 18b sets the intersection point of the smooth line 35 and the tire axial center line (dash line) as the starting point A (note that although FIG. 5 is the diagram used in explaining the classification of the outer contour line 30 and the inner contour line 31, for convenience, FIG. 5 will also be used here). Next, the length calculation unit 18b searches for the point closest to the starting point A and identifies point N1 as the closest point. Next, the length calculation unit 18b calculates the distance from the starting point A to point N1 (referred to as the "distance between A and N1").

[0076] Next, the length calculation unit 18b searches for the point closest to point N1 and identifies point N2 as the closest point. Note that the search range does not include the points already identified (point A and point N1). Next, the length calculation unit 18b calculates the distance from point N1 to point N2 (referred to as the "N1-N2 distance"). Next, the length calculation unit 18b adds the N1-N2 distance to the A-N1 distance, and determines the resulting value as the length along the smooth line 35 from the starting point A to point N2.

[0077] In this way, the length calculation unit 18b repeatedly identifies the nearest point of the previously identified point and calculates the distance between those two points. The length calculation unit 18b also adds up the distances between the two points that have been found up to that point, and sets the added value as the length along the smooth line 35 from the starting point A.

[0078] Note that the search range for the nearest point does not include points that have already been identified. The search for the nearest point is performed in one direction along the smooth line 35 (that is, in a clockwise or counterclockwise direction on the annular outer contour line 30). Furthermore, since the smooth line 35 is basically formed by consecutive points (pixels), the search range for the nearest point is a range of two pixels or less. Therefore, the distance between the two points described above is a distance of two pixels or less.

[0079] The lengths along the smooth line 35 from the starting point A to each point thus determined are shown in Table 1. N1, N2··· listed in Table 1 refer to N1, N2··· in FIG.

[0080] [Table 1] Using such a table, it is possible to identify a point that is a predetermined distance away from origin point A along smooth line 35. For example, when attempting to identify a point (pixel) that is four pixels away from origin point A along smooth line 35, the point whose integrated value of the distance between the two points from origin point A is closest to 4 is identified. Based on Table 1, this point is point N4 (see FIG. 5).

[0081] Furthermore, if the length of one pixel on the actual tire is known in advance, it is possible to identify which point on the smooth line 35 corresponds to a position on the actual tire that is a predetermined distance from starting point A. For example, if it is known in advance that one pixel corresponds to 1 mm, and a position 8 mm away from starting point A is to be identified, the point whose integrated value of the distance between the two points from starting point A is closest to 8 mm (= 8 pixels) is identified. Based on Table 1, this point is point N7.

[0082] 1 includes a reference point determination unit 19a and a tire thickness calculation unit 19b. The reference point determination unit 19a determines an outer reference point on the outer contour line 30 and an inner reference point on the inner contour line 31. The tire thickness calculation unit 19b then calculates the length from the outer reference point to the inner reference point, and calculates the tire thickness based on that length.

[0083] The optimal method for determining the outer and inner reference points varies depending on the location where the thickness is measured. There are three methods for determining the outer and inner reference points:

[0084] The first method is used mainly when calculating the tire axial center position and the thickness at the tire maximum width position. In the first method, the reference point determination unit 19a determines the intersection of a line drawn on the tire cross-sectional image with the outer contour line 30 as the outer reference point, and the intersection of the same line with the inner contour line 31 as the inner reference point.

[0085] When calculating the thickness at the tire axial center position, the reference point determination unit 19a determines the intersection of the tire axial center line and the outer contour line 30 as the outer reference point (shown by reference symbol 32 in FIG. 5 for reference), and the intersection of the tire axial center line and the inner contour line 31 as the inner reference point (shown by reference symbol 33 in FIG. 5 for reference). Note that, among the intersections of the tire axial center line and the overall contour line, the point with the largest Y coordinate value may be specified as the outer reference point, and the point with the second largest Y coordinate value may be specified as the inner reference point.

[0086] Furthermore, when calculating the tire thickness at the tire's maximum width position (the position where the tire's axial length is the longest), a virtual line parallel to the tire's axial direction is first assumed. Here, it is assumed that the tire's axial direction is parallel to the X-coordinate axis, and that the virtual line is a straight line parallel to the X-coordinate axis with a constant Y-coordinate value. While changing the Y-coordinate value of such a virtual line, the reference point determination unit 19a calculates the difference between the maximum and minimum X-coordinate values ​​of the intersections between the virtual line and the outer contour line 30. Then, the reference point determination unit 19a identifies the virtual line where this difference is greatest as the straight line passing through the tire's maximum width position.

[0087] Next, the reference point determination unit 19a calculates the coordinates of the intersection between the straight line passing through the tire's maximum width position thus identified and the outer contour line 30, and further calculates the coordinates of the intersection between the straight line passing through the same tire's maximum width position and the inner contour line 31. From these calculated coordinates, the reference point determination unit 19a calculates the tire thickness at the tire's maximum width position.

[0088] The second method is suitable for calculating the thickness of a portion of a tire where tread rubber exists, a portion where sidewall rubber exists, etc. In the second method, first, one point on the smooth line 35 is identified as a point corresponding to the outer reference point. Specifically, the user inputs the distance along the smooth line 35 from the starting point A to be used as the outer reference point.

[0089] The reference point determination unit 19a identifies, from the group of points forming the smooth line 35, the point (pixel) that is closest to a position that is the input distance away from the origin A as the point that corresponds to the outer reference point (referred to as the "corresponding point"). At this time, the reference point determination unit 19a also identifies the coordinates of the corresponding point. Here, the point that is closest to a position that is the input distance away from the origin A is identified based on Table 1. For example, if the input distance is 4 in pixels, point N4, which is the point whose integrated value of the distance between the two points from the origin A is closest to 4, is identified as the point that is closest to a position that is the input distance (4 pixels) away from the origin A.

[0090] Next, the reference point determination unit 19a identifies, as an outer reference point, a point with the same coordinates as the corresponding point in the image before it is used as a length measurement model (the image before the outer contour line 30 is changed to the smooth line 35). Here, the outer reference point is often a point on the outer contour line 30, but may also be a point at a location where the outer contour line 30 is not present, such as the location of the tire's main groove 34.

[0091] The images before and after changing the outer contour line 30 to the smooth line 35 have the same size and coordinates. The images before and after changing the outer contour line 30 to the smooth line 35 differ only in the part of the smooth line 35.

[0092] Next, in the image before being used as a length measurement model, the reference point determination unit 19a draws a straight line (perpendicular line) that passes through the outer reference point and is perpendicular to the inner contour 31, and determines the point closest to the perpendicular line among the points forming the inner contour 31 as the inner reference point. Specifically, the reference point determination unit 19a first extracts multiple points close to the outer reference point from the points forming the inner contour 31. In Figure 7, the outer reference point is indicated by the reference symbol 32, and multiple points close to the outer reference point are indicated by the other black dots. However, the number and arrangement of points are simplified in Figure 7 for ease of explanation.

[0093] Here, "multiple points close to the outer reference point" refers to multiple points within a predetermined range from the outer reference point. When the outer reference point is on the contact surface of the tread, the predetermined range is, for example, within ±10 pixels in the X coordinate. When the outer reference point is on the sidewall surface, the predetermined range is, for example, within ±10 pixels in the Y coordinate. When the outer reference point is on the contact edge of the tread or in its vicinity, the predetermined range is, for example, 30 to 50 pixels in the X coordinate toward the tire axial centerline (i.e., in this case, "multiple points close to the outer reference point" are all located further in the tire axial centerline direction than the outer reference point).

[0094] Next, the reference point determination unit 19a approximates, with a straight line (referred to as an "approximate straight line"), multiple points close to the outer reference point extracted from the group of points forming the inner contour 31. In FIG. 7, the approximate straight line is a dashed dotted line indicated as L1. Next, the reference point determination unit 19a determines a straight line that passes through the outer reference point and intersects perpendicularly with the approximate straight line. This straight line can be considered to be a perpendicular line to the inner contour 31. In FIG. 7, this perpendicular line is a dashed dotted line indicated as L2. Next, the reference point determination unit 19a calculates the distance between the perpendicular line to the inner contour 31 and each point forming the inner contour 31, and determines the point with the shortest distance as the inner reference point. In FIG. 7, the inner reference point identified in this manner is indicated by the reference symbol 33.

[0095] The third method is suitable for calculating the thickness of a portion of a tire where a buttress exists or a portion where a rim strip exists. In the third method, the point closest to a specified position on the tire cross-sectional image is identified as the outer reference point from among the point cloud that forms the outer contour line 30.

[0096] Specifically, first, the user specifies the coordinates of a point on the tire cross-sectional image that the user wants to select as an outer reference point. As a specific example, on the tire cross-sectional image with the outer contour 30, the user uses a mouse to click on a point that the user wants to select as an outer reference point. The coordinates of the clicked position are then specified. Next, the reference point determination unit 19a calculates the distance between the specified coordinates and each point on the outer contour 30. The reference point determination unit 19a then specifies the point on the outer contour 30 that has the shortest distance as the outer reference point.

[0097] The method for identifying the inner reference point in the third method is basically the same as the second method. In detail, the reference point determination unit 19a first extracts multiple points close to the outer reference point from the group of points forming the inner contour 31. The multiple points close to the outer reference point are multiple points within a predetermined range based on the outer reference point. When the outer reference point is located on the buttress, the predetermined range is, for example, an area 20 to 60 pixels away from the outer reference point in the direction of the tire axial center in terms of the X coordinate. When the outer reference point is located on the rim strip, the predetermined range is, for example, an area 20 to 50 pixels away from the outer reference point in the direction of the tire rotation axis (the direction in which the Y coordinate value is 0).

[0098] Next, the reference point determination unit 19a approximates with straight lines (approximation lines) multiple points close to the outer reference points extracted from the point group forming the inner contour 31. Next, the reference point determination unit 19a determines straight lines (perpendicular lines) that pass through the outer reference points and intersect perpendicularly with the approximation lines. Next, the reference point determination unit 19a calculates the distance between the perpendicular lines to the inner contour 31 and each point forming the inner contour 31, and determines the point with the shortest distance as the inner reference point.

[0099] After the outer reference point and the inner reference point are determined by any of the above methods, the tire thickness calculation unit 19b calculates the distance from the outer reference point to the inner reference point (this distance is referred to as the "inter-reference point distance"). The unit of the inter-reference point distance obtained by this calculation is pixels.

[0100] Next, the tire thickness calculation unit 19b converts the distance between the reference points into the actual tire thickness. For the conversion, a coefficient indicating the relationship between the number of pixels in the entire image and the actual tire length is used. Such a coefficient is determined in advance by a known method, for example, from the relationship between the length of a predetermined portion of a reference object and the number of pixels in the predetermined portion in an image of the object photographed and imported into the image processing device 10.

[0101] The image processing device 10 described above calculates the tire thickness based on the flowcharts of FIGS.

[0102] First, a cross-sectional image of the rimmed tire taken by a CT device is input into the image processing device 10 and converted into a 256-level grayscale image by the grayscale conversion unit 11 (S1 in FIG. 8). As mentioned above, it is preferable that the cross-sectional image of the rimmed tire accounts for 5% or more and 15% or less of the entire image.

[0103] Next, the brightness adjustment unit 12 adjusts the brightness of the entire image (S2 in FIG. 8). In more detail, first, in a tire image extraction step, the rubber portion of the tire cross-sectional image is extracted from the entire image (S2-1 in FIG. 9). The rubber portion is extracted by extracting a portion of the entire image whose brightness is within a predetermined range (for example, not less than 30 and not more than 150). Next, in an average brightness calculation step, the average brightness of the rubber portion is calculated (S2-2 in FIG. 9).

[0104] Next, in the brightness adjustment step, the average brightness of the rubber parts of the tire cross-sectional image is used to bring the brightness of the tire cross-sectional image closer to the reference brightness. To do this, a scaling coefficient is first calculated by dividing the reference brightness by the average brightness of the rubber parts (S2-3 in Figure 9). Next, a scaling calculation is performed for each pixel in the rubber parts of the tire cross-sectional image, multiplying the pixel brightness by the scaling coefficient (S2-4 in Figure 9). Through the scaling calculation, the brightness of the rubber parts of the tire cross-sectional image approaches the reference brightness. As a result, all tire cross-sectional images have a similar level of brightness.

[0105] Next, the image rotation unit 13 automatically rotates the entire image so that the axial direction of the tire cross-sectional image is parallel to the X-coordinate axis (S3 in FIG. 8). In detail, first, from within the entire image, portions whose brightness is equal to or greater than a predetermined threshold are detected as bead cores of the tire cross-sectional image (S3-1 in FIG. 10). Two bead cores are detected. Next, the coordinates of the centers of gravity of each of the two bead cores are calculated and identified (S3-2 in FIG. 10). Next, a line connecting the centers of gravity of the two bead cores is identified (S3-3 in FIG. 10), and the slope of that line is calculated (S3-4 in FIG. 10).

[0106] Next, based on the determined tilt, the angle between the line connecting the centers of gravity of the bead cores and the X coordinate axis is determined, and a rotation angle θ' is determined based on this angle (S3-5 in FIG. 10). Next, the entire image is rotated by the determined rotation angle θ' (S3-6 in FIG. 10). With this rotation, the tire axial direction of the tire cross-sectional image becomes the X coordinate direction, and the tire radial direction of the tire cross-sectional image becomes the Y coordinate direction. Furthermore, the tire cross-sectional image is on top and the rim cross-sectional image is on the bottom.

[0107] Next, the binarization processing unit 14 performs binarization processing on the entire image after brightness adjustment and rotation (S4 in FIG. 8). Through the binarization processing, the pixels of the tire cross-sectional image and the rim cross-sectional image become white pixels with a signal value of 1, and the pixels of the background become black pixels with a signal value of 0.

[0108] Next, noise is removed from the binarized rim-equipped tire cross-sectional image by the noise processing unit 15 (S5 in FIG. 8). In more detail, the user selects the range including the gap between the flange and the tire in the entire image after binarization as the range to be saved (S5-1 in FIG. 11). Then, the coordinates and signal values ​​of each pixel in the selected range to be saved are saved as image data before expansion / contraction processing (S5-2 in FIG. 11).

[0109] Next, the entire image after the binarization process is subjected to an expansion / contraction process (S5-3 in FIG. 11). The expansion / contraction process removes noise that was present in the entire image. However, the expansion / contraction process distorts the outline of the part that corresponds to the above-mentioned storage range.

[0110] Next, the image data of the portion of the overall image that corresponds to the storage range after the expansion / contraction processing is replaced with the image data of the storage range before the expansion / contraction processing (S5-4 in FIG. 11). As a result, the image in the storage range becomes the image before the expansion / contraction processing, and the image in other areas becomes the image after the expansion / contraction processing. As a result, the entire outline of the rimmed tire cross-sectional image appears correctly. Furthermore, because there is little noise in the storage range before the expansion / contraction processing and noise has been removed from areas outside the storage range by the expansion / contraction processing, the overall image has little noise.

[0111] Next, the contour line of the rimmed tire cross-sectional image is detected from the entire image after binarization and noise processing by the contour line detection unit 16 (S6 in FIG. 8). The detected entire contour line is made up of a group of points.

[0112] Next, the contour classification unit 17 classifies the overall contour into an annular outer contour 30 representing the outer surface of the rimmed tire and an annular inner contour 31 representing the inner surface of the rimmed tire (S7 in FIG. 8).

[0113] In detail, first, the outer contour 30 is extracted from the point cloud representing the overall contour (S7-1 to S7-4 in FIG. 12). In extracting the outer contour 30, first, the point that is the outermost in the tire radial direction among the intersections between the overall contour and the tire axial centerline (for example, point A in FIG. 5) is identified as the first outer point on the outer contour 30 (S7-1 in FIG. 12).

[0114] Next, it is checked whether there are any other points within a predetermined distance (2 pixels) from the first outer point that have not been identified as outer points (S7-2 in FIG. 12). If there are any points within the predetermined distance that have not been identified as outer points (No in S7-2 in FIG. 12), the point closest to the first outer point (nearest point) is identified as the second outer point (S7-3 in FIG. 12).

[0115] Next, it is checked whether there are any other points within a predetermined distance from the second outer point that have not been identified as outer points (S7-2 in FIG. 12). If there are any points within the predetermined distance that have not been identified as outer points (No in S7-2 in FIG. 12), the point closest to the second outer point (nearest point) is identified as the third outer point (S7-3 in FIG. 12).

[0116] In this way, as long as there is a point within the specified distance that has not been identified as an outer point (No in S7-2 in Figure 12), the point closest to the previously identified outer point (nearest point) is identified as the next outer point (S7-3 in Figure 12) and this process is repeated.

[0117] Then, when there are no points within the specified distance that have not been identified as outer points (Yes in S7-2 of FIG. 12), the search for outer points ends, and the line consisting of the points that have been identified as outer points up to that point is extracted as the outer contour line 30 (S7-4 of FIG. 12).

[0118] Next, an inner contour line 31 is extracted from the point cloud representing the overall contour line (S7-5 to S7-8 in FIG. 12). In extracting the inner contour line 31, first, among the intersection points between the overall contour line and the tire axial center line, the second point from the outer side in the tire radial direction (for example, point B in FIG. 5) is identified as the first inner point (S7-5 in FIG. 12).

[0119] Next, it is checked whether there are any other points within a predetermined distance (2 pixels) from the first interior point that have not been identified as interior points (S7-6 in FIG. 12). If there are any points within the predetermined distance that have not been identified as interior points (No in S7-6 in FIG. 12), the point closest to the first interior point (nearest point) is identified as the second interior point (S7-7 in FIG. 12).

[0120] Next, it is checked whether there are any other points within a predetermined distance from the second interior point that have not been identified as interior points (S7-6 in FIG. 12). If there are any points within the predetermined distance that have not been identified as interior points (No in S7-6 in FIG. 12), the point closest to the second interior point (nearest point) is identified as the third interior point (S7-7 in FIG. 12).

[0121] In this way, as long as there is a point within the specified distance that has not been identified as an interior point (No in S7-6 in Figure 12), the point closest to the previously identified interior point (nearest point) is identified as the next interior point (S7-7 in Figure 12) and this process is repeated.

[0122] Then, when there are no points within the specified distance that have not been identified as interior points (Yes in S7-6 of FIG. 12), the search for interior points ends, and the line consisting of the points identified as interior points up to that point is extracted as the interior contour line 31 (S7-8 of FIG. 12).

[0123] By extracting the outer contour 30 and the inner contour 31 in the manner described above, the entire contour is classified into the outer contour 30 and the inner contour 31.

[0124] Next, the length measurement model creation unit 18 creates a length measurement model in which the main grooves 34 of the tread in the tire cross-sectional image are filled in and the outer contour line 30 is replaced with a smooth line 35. Then, the distances along the smooth line 35 from the starting point A to each point on the smooth line 35, as shown in Table 1, are calculated (S8 in FIG. 8).

[0125] In detail, first, a sufficiently large area (for example, an area including the entire tread) that includes the portion for which the length along the smooth line 35 is to be measured is selected from the entire image, and an inflation / deflation process is performed on the selected area (S8-1 in FIG. 13). By the inflation / deflation process, the main grooves 34 of the tread are filled, and the outer contour line 30 in the tire cross-sectional image becomes a smooth line 35. The amount of inflation and deflation in the inflation / deflation process is set to be equal to or less than the thickness from the outer contour line 30 to the inner contour line 31 in the tire tread.

[0126] Next, the intersection point between the smooth line 35 and the tire axial center line is set as the starting point A (S8-2 in FIG. 13). Next, the point closest to the starting point A is identified from the group of points forming the smooth line 35 (S8-3 in FIG. 13). Next, the distance from the starting point A to the closest point is calculated (S8-4 in FIG. 13).

[0127] Next, from the group of points forming the smooth line 35, the point closest to the previously identified nearest point is identified as the next nearest point (S8-5 in FIG. 13). Next, the distance from the previously identified nearest point to the next previously identified nearest point (the distance between the two points) is calculated (S8-6 in FIG. 13). Note that the distance between the two points is two pixels or less. Next, the distances between the two points found up to that point are added up (S8-7 in FIG. 13). This added value corresponds to the distance along the smooth line 35 from the starting point A to the most recently identified nearest point.

[0128] Until the calculation of the predetermined range is completed (No in S8-8 in FIG. 13), the process of identifying the next nearest point, calculating the distance between the two points, and calculating the integrated value of the distance between the two points is repeated (S8-5 to S8-7 in FIG. 13). Then, when the calculation of the predetermined range is completed (Yes in S8-8 in FIG. 13), the creation of the length measurement model is completed. As a result, the distance from the starting point A to each point forming the smooth line 35 is obtained, as shown in Table 1.

[0129] Next, the length calculation unit 19 calculates the tire thickness at an arbitrary position (S9 in FIG. 8). In detail, the user decides the location for which the thickness is to be calculated, and selects the method of determining the outer reference points and inner reference points appropriate for that location from among the first to third methods described above. Note that the system may be configured so that when the user inputs the location for which the thickness is to be calculated from the input unit, one of the first to third methods is automatically selected. Here, it is assumed that the second method using a length measurement model is selected.

[0130] First, the user inputs the distance along the smooth line 35 from the starting point A that will be used as the outer reference point (S9-1 in FIG. 14). Here, the distance from the starting point A to each of the points forming the smooth line 35 has been determined in step S8. Then, the point on the smooth line 35 whose distance from the starting point A is closest to the distance input in step S9-1 is identified as the point corresponding to the outer reference point (corresponding point) (S9-2 in FIG. 14). At this time, the coordinates of the corresponding point are also identified.

[0131] Next, in the image before the outer contour 30 is converted into the smooth line 35, a point with the same coordinates as the corresponding point is identified as an outer reference point (S9-3 in FIG. 14).

[0132] Next, a straight line (perpendicular line, for example, L2 in FIG. 7) that passes through the outer reference point and is perpendicular to the inner contour 31 is identified. Then, from among the points that form the inner contour 31, the point closest to the perpendicular line (for example, point 31 in FIG. 7) is identified as the inner reference point (S9-4 in FIG. 14).

[0133] When the first method is selected, a straight line such as the tire axial centerline is drawn in the entire image. Then, the intersection of this straight line with the outer contour line 30 is identified as the outer reference point, and the intersection of this straight line with the inner contour line 31 is identified as the inner reference point. The outer reference point and the inner reference point are identified simultaneously.

[0134] When the third method is selected, the user specifies coordinates on the tire cross-sectional image near a point they wish to select as an outer reference point. The point on the outer contour 30 that is closest to the specified coordinates is then specified as the outer reference point. Next, from the group of points forming the inner contour 31, the point that is closest to a straight line (perpendicular line) that passes through the outer reference point and is perpendicular to the inner contour 31 is specified as the inner reference point.

[0135] After the outer reference point and the inner reference point are determined, the distance from the outer reference point to the inner reference point (distance between reference points) is calculated (S9-5 in FIG. 14). Next, the distance between reference points is converted into the actual tire thickness (S9-6 in FIG. 14). The tire is evaluated based on the thickness of each part of the tire calculated using the above method.

[0136] When multiple tires are evaluated, the above steps (S1 to S9 in FIG. 8) are performed for each of the multiple tires.

[0137] As described above, according to this embodiment, in the noise removal step (S5 in FIG. 8), image data in the storage range within the entire image is stored before the expansion / contraction process, and then the entire image is subjected to the expansion / contraction process to remove noise. Then, the image data in the storage range within the entire image after the expansion / contraction process is replaced with the stored image data before the expansion / contraction process.

[0138] Therefore, even if the image in the saved range has a contour that is likely to change due to the expansion / contraction process, the image data in the saved range will ultimately be the image data before the expansion / contraction process. As a result, the contour of the rim-equipped tire cross-sectional image is unlikely to change even when the expansion / contraction process is performed. On the other hand, the image data outside the saved range will ultimately be the image data after the expansion / contraction process, so an image with much of the noise removed from the overall image can be obtained.

[0139] Here, the gap between the rim flange and the tire is likely to be filled by the expansion / contraction process, and is a part of the overall image where the contour is particularly likely to be distorted. However, because the range that includes the gap between the rim flange and the tire is set as the range to be saved, the gap between the rim flange and the tire in the final overall image is the image before the expansion / contraction process. As a result, the contour of the rim-attached tire cross-sectional image after the expansion / contraction process is almost the same as that before the expansion / contraction process.

[0140] Various modifications can be made to the above embodiment. Any one of the modifications described below may be applied to the above embodiment, or two or more of the modifications may be selected and combined and applied to the above embodiment.

[0141] <Change example 1> As tire cross-sectional images, images taken by various non-destructive testing devices such as an MRI (Magnetic Resonance Imaging) device, as well as images taken by an X-ray CT device, can be used.

[0142] Furthermore, an image obtained by actually cutting a tire with a cutter and photographing the cross section with a camera can also be used as the tire cross-sectional image.

[0143] <Change example 2> When adjusting the brightness by the brightness adjustment unit 12, the brightness may be adjusted based on the average brightness of the entire rim-equipped tire cross-sectional image (i.e., the entire tire cross-sectional image and the entire rim cross-sectional image), not just the rubber part as in the above embodiment.

[0144] In this case, first, a cross-sectional image of the tire with rim is extracted from the entire image. In the entire image, the rubber parts of the tire cross-sectional image have a high brightness, and the metal parts (bead cores, etc.) and the rim cross-sectional image of the tire have an even higher brightness. Therefore, by extracting parts of the entire image whose brightness is above a threshold, the entire cross-sectional image of the tire with rim is extracted. For example, the threshold value is set to 30, which is the numerical value of the gray scale when the entire image is expressed in 256 gray scales.

[0145] Next, the average brightness of the entire rimmed tire cross-sectional image is calculated. Next, the reference brightness is divided by the average brightness and the resulting value is set as the scaling factor. Next, for each of all pixels in the entire image or a portion thereof (for example, the rimmed tire cross-sectional image), a calculation is performed in which the brightness of the pixel is multiplied by the scaling factor. This calculation brings the brightness of the rimmed tire cross-sectional image closer to the reference brightness.

[0146] <Change example 3> In adjusting the brightness by the brightness adjusting unit 12, the average brightness of the entire image may be calculated, and the scaling coefficient may be determined from the average brightness and the reference brightness.

[0147] In this case, the proportion (area ratio) of the rim-equipped tire cross-sectional image in the entire image affects the average brightness. Therefore, the image is taken so that the area ratio falls within a predetermined range. The predetermined range is preferably 5% to 15%, and more preferably 8% to 12%.

[0148] In this case, it is preferable that the number of pixels in the white parts of the entire image (for example, parts with gradations above the intermediate gradations between black and white) is between 5% and 10% of the total number of pixels in the entire image. If this condition is met, the average brightness will be appropriate.

[0149] <Change Example 4> When a bead core is used to rotate the rimmed tire cross-sectional image, the method of selecting the bead core and the method of identifying the center of gravity of the bead core are not limited to the methods in the above embodiment.

[0150] For example, the user may manually select a bead core from the cross-sectional image of the rimmed tire, or may manually identify the center of gravity of the bead core.

[0151] <Change Example 5> When rotating the rimmed tire cross-sectional image so that it is parallel to the reference line (X coordinate axis), a straight line indicating the inclination of the rim may be used instead of a straight line connecting the bead cores on both sides of the tire in the axial direction.

[0152] Specifically, first, the flanges on both sides of the rim in the tire axial direction are identified, and then the vertices on the outer sides of each flange in the tire radial direction are identified. After these two vertices are identified, a straight line connecting these vertices is obtained. Here, the method for obtaining a straight line from two points is the same as the method for obtaining a straight line from the respective centers of gravity of the two bead cores in the above embodiment.

[0153] The straight line thus obtained is the straight line that indicates the inclination of the rim. The rotation angle of the tire cross-sectional image is then determined based on the angle between the straight line that indicates the inclination of the rim and the reference line. The method for determining the rotation angle from the straight line is the same as the method used in the above embodiment.

[0154] In this modified example, the method for identifying the flange and its radially outer vertex is not limited. For example, the user may manually identify the flange and its radially outer vertex. Alternatively, the rim may be automatically detected based on brightness, or the radially outer vertex of the flange may be automatically identified from the rim shape.

[0155] In addition, by taking advantage of the fact that the brightness of the rim cross-sectional image is high in the rim-equipped tire cross-sectional image, a direction in which a large number of pixels with high brightness (for example, brightness falling within a predetermined range of 180 or more and 250 or less) are lined up can be identified, and a straight line extending in that direction can be used as a straight line indicating the inclination of the rim.

[0156] The basic shape of the rim on which a tire is mounted is the same regardless of the type of tire, so by rotating the rim-attached tire cross-sectional image using the straight line that indicates the inclination of the rim, as in this modified example, all tires can be oriented in the same direction.

[0157] <Change Example 6> This modified example is a modified example of the method for determining the rotation angle when rotating the rimmed tire cross-sectional image so that it is parallel to the reference line (X coordinate axis).

[0158] In this modified example, small rotations are performed multiple times to rotate the rim cross-sectional image (or a rim-equipped tire cross-sectional image) by an arbitrary small angle. The center of rotation for the small rotation is any point on the rim cross-sectional image, and the rotation occurs on the plane of the entire image. The rotation axis for the small rotation is an axis extending in a direction perpendicular to the planar entire image. The arbitrary angle is, for example, 1°.

[0159] For each small rotation by the aforementioned arbitrary angle, the maximum coordinate in the direction perpendicular to the reference line (Y coordinate direction) is determined for each of the axially one portion and the other portion of the rim. Such maximum coordinates are often the coordinates of the axially one and the other rim flange ends. Furthermore, for each small rotation by the aforementioned arbitrary angle, the absolute value of the difference between the maximum coordinates for the axially one portion and the other portion is determined.

[0160] As small rotations are repeated, the absolute value of the difference between the maximum coordinates on one side of the tire axial direction and the other side will reach a minimum. When this absolute value reaches a minimum, it is when the axial direction of the rim cross-sectional image (note that the axial direction of the rim cross-sectional image coincides with the axial direction of the tire cross-sectional image) becomes parallel to the reference line, or when it is closest to being parallel. At this time, the number of small rotations performed until the absolute value reaches a minimum becomes clear. Also, the cumulative rotation angle (cumulative angle of small rotations) from the start of a small rotation to the time the absolute value reaches a minimum becomes clear.

[0161] The cumulative rotation angle thus determined is set as the rotation angle for rotating the rimmed tire cross-sectional image, and processing is then performed to rotate the rimmed tire cross-sectional image by that rotation angle.

[0162] This processing makes the axial direction of the rim cross-sectional image parallel or approximately parallel to the reference line, and further makes the axial direction of the tire cross-sectional image parallel or approximately parallel to the reference line.

[0163] The above method can be implemented when the tire cross-sectional image is on top (i.e., the one with the larger Y coordinate value) and the rim cross-sectional image is on the bottom in the rim tire cross-sectional image before rotation. If the tire cross-sectional image is on the bottom and the rim cross-sectional image is on top in the rim tire cross-sectional image before rotation, it is necessary to rotate the rim tire cross-sectional image by 180 degrees before implementing the above method.

[0164] <Change Example 7> This modified example is a modified example of the method of rotating the rimmed tire cross-sectional image so that it is parallel to the reference line (X coordinate axis).

[0165] In this modified example, small rotations are performed multiple times to rotate the rim cross-sectional image (or a rim-equipped tire cross-sectional image) by an arbitrary small angle. The center of rotation for the small rotation is any point on the rim-equipped tire cross-sectional image, and the rotation occurs on the surface of the entire image. The rotation axis for the small rotation is an axis extending in a direction perpendicular to the planar entire image. The arbitrary angle is, for example, 1°.

[0166] For each small rotation by the aforementioned arbitrary angle, the maximum coordinate of each of the rim portions in the direction perpendicular to the reference line (Y coordinate direction) is determined. Such maximum coordinates are often the coordinates of the rim flange ends on one side and the other side in the tire axial direction. Furthermore, for each small rotation by the aforementioned arbitrary angle, the absolute value of the difference between the maximum coordinates on one side and the other side in the tire axial direction is determined.

[0167] As small rotations are repeated, the absolute value of the difference between the maximum coordinates on one side of the tire axial direction and the other side may reach a minimum value. When this absolute value reaches a minimum value, the axial direction of the rim cross-sectional image becomes parallel to the reference line, or is closest to being parallel. At this time, the axial direction of the tire cross-sectional image also becomes parallel to the reference line, or is closest to being parallel. Therefore, the small rotation is terminated when the absolute value reaches a minimum value.

[0168] This processing makes the axial direction of the rim cross-sectional image parallel or approximately parallel to the reference line, and further makes the axial direction of the tire cross-sectional image parallel or approximately parallel to the reference line.

[0169] <Change Example 8> As shown in Figure 15, there are cases where the rimmed tire cross-sectional image is not upside down (i.e., the tire cross-sectional image is on top and the rim cross-sectional image is on the bottom) in the overall image at the time it is imported into the image processing device 10. In such cases, in the step of rotating the image (S3 in Figure 8), the rotation angle of the rimmed tire cross-sectional image is set to the angle θ between the line connecting the centers of gravity of the bead cores (or the line indicating the inclination of the rim as explained in the modified example above) and the reference line.

[0170] Whether or not the cross-sectional image of the tire with rim is upside down is automatically determined using the following method. First, the entire image is divided into two regions, one above the other. At this time, the areas of the two regions are equal. Next, the average brightness within each of the upper and lower regions is calculated. Next, it is determined that the rim cross-sectional image exists in the region with the higher average brightness (this is because the rim and the bead core nearby appear particularly bright). Then, if the region where the rim cross-sectional image exists is the upper region, it is determined that the rim cross-sectional image is upside down.

[0171] <Change Example 9> In the noise processing step (S5 in Figure 8), the storage range in which the image data is restored to the state before the expansion / contraction processing after the expansion / contraction processing is not limited to the range including the gap between the flange and the tire (within the frame in Figure 4(b)) as in the above embodiment.

[0172] The range to be saved preferably includes areas where the original contour lines are likely to be distorted by the expansion / contraction process, or areas where there is little noise such as chipping in the original image. Such areas include, for example, any part of the rim.

[0173] <Change Example 10> In the noise processing step (S5 in FIG. 8) in the above embodiment, the processes are performed in the order of expansion, erosion, erosion, and expansion, but the order of the processes is not limited to this. For example, if there are few defects in the rimmed tire cross-sectional image and there is no risk of thin portions of the rimmed tire cross-sectional image being cut off even if erosion is performed first, the processes may be performed in the order of erosion, expansion, expansion, and erosion.

[0174] <Change Example 11> As a method for classifying the entire contour into the outer contour 30 and the inner contour 31, a line consisting of the points remaining after the outer contour 30 is extracted may be extracted as the inner contour 31.

[0175] Specifically, first, the outer contour 30 is extracted using the method of the above embodiment. Then, among the points forming the overall contour, those not included in the points forming the outer contour 30 are identified as points forming the inner contour 31.

[0176] <Change Example 12> In the method for classifying the overall contour line into the outer contour line 30 and the inner contour line 31, in the above embodiment, the intersection points between the tire axial center line and the overall contour line are identified as the first outer point and the first inner point. However, the straight lines used to identify the intersection points are not limited to the tire axial center line.

[0177] If a straight line is parallel to the Y coordinate axis (i.e., a straight line extending in the tire radial direction) and can intersect with both the outer contour line 30 and the inner contour line 31, the intersection points of that line and the overall contour line can be identified as the first outer point and the first inner point. A straight line that is parallel to the Y coordinate axis and can intersect with both the outer contour line 30 and the inner contour line 31 is a straight line that passes through a cavity inside the tire and is parallel to the Y coordinate axis. The tire axial center line is also a straight line that passes through a cavity inside the tire and is parallel to the Y coordinate axis.

[0178] <Change Example 13> In the above embodiment, the intersection of the smooth line 35 created by the smooth line creating unit 18a and the tire axial center line is set as the starting point A, and the lengths from the starting point A to each point are calculated to create Table 1, but the position of the starting point is not limited. Any point on the smooth line 35 can be set as the starting point, and the lengths from the starting point to each point can be calculated to create a table similar to Table 1.

[0179] In the above embodiment, in the second method for identifying the outer reference points and the inner reference points, the intersection of the smooth line 35 and the tire axial center line is set as the starting point A, and the outer reference points are identified based on the distance along the smooth line 35 from the starting point A. However, the starting point for identifying the outer reference points is not limited to the starting point A, which is the intersection of the smooth line 35 and the tire axial center line. The starting point may be any point on the smooth line 35.

[0180] <Change Example 14> The thickness of the tire in the above embodiment is the length in the direction of extension of a perpendicular to the inner contour line 31. However, the length in the direction of extension of a perpendicular to the outer contour line 30 may be the thickness of the tire.

[0181] <Change Example 15> The order of the steps in the above embodiment can be changed. For example, the order of the step of adjusting the brightness of the overall image (S2 in FIG. 8) and the step of rotating the rimmed tire cross-sectional image (S3 in FIG. 8) can be interchanged. Also, the order of the step of classifying the overall contour line into outer contour line 30 and inner contour line 31 (S7 in FIG. 8) and the step of filling in the main groove 34 to form a single smooth line 35 representing the tire outer surface and creating a length measurement model (S8 in FIG. 8) can be interchanged.

[0182] In addition, in the step of classifying the overall contour into the outer contour 30 and the inner contour 31 (S7 in FIG. 8), the outer contour 30 may be extracted after the inner contour 31 is extracted. Alternatively, the extraction of the outer contour 30 and the extraction of the inner contour 31 may be performed simultaneously.

[0183] <Change Example 16> The scaling calculation may be performed only on the entire tire cross-sectional image (i.e., the portion including not only the rubber portion but also the metal portion such as the bead core, but excluding the rim cross-sectional image and the background). Alternatively, the scaling calculation may be performed on the entire image. [Explanation of symbols]

[0184] 10...image processing device, 11...grayscale conversion unit, 12...brightness adjustment unit, 12a...tire image extraction unit, 12b...brightness change unit, 13...image rotation unit, 13a...bead core detection unit, 13b...rotation unit, 14...binarization processing unit, 15...noise processing unit, 16...contour line detection unit, 17...contour line classification unit, 18...length measurement model creation unit, 18a...smooth line creation unit, 18b...length calculation unit, 19...length calculation unit, 19a...reference point determination unit, 19b...tire thickness calculation unit, 30...outer contour line, 31...inner contour line, 32...outer reference point, 33...inner reference point, 34...main groove, 35...smooth line

Claims

1. 1. A tire cross-sectional image processing method, comprising: acquiring an overall image including a tire cross-sectional image; storing image data of a predetermined range within the entire image as image data before expansion / contraction processing; performing an expansion / contraction process on the entire image; replacing the image data of the predetermined range of the image after the expansion / contraction processing with the stored image data before the expansion / contraction processing; An image processing method comprising:

2. the tire cross-sectional image is a cross-sectional image of a tire attached to a rim, 2. The image processing method according to claim 1, wherein the predetermined range is a range that includes a gap between the flange of the rim and the tire.

Images