JP5373593B2 - Image processing method, image processing apparatus, program, and recording medium - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image processing method, an image processing device, a program, and a recording medium capable of stabilizing inspection and increasing accuracy independently of the type of tire, the state of tire surface, and positional relation of illumination. <P>SOLUTION: A control part 12 transforms a density of each pixel of a capture image into an average density on a line including the pixel for each line in a direction perpendicular to the rotation direction of a tire to perform density projection transform. Next, Fourier expansion is performed about the pixels on one line from among the lines in the rotation direction of the tire, for the image subjected to density projection transform. Inverse Fourier expansion of residual frequency components excluding frequency component of a cord exposing band from the frequency components subjected to Fourier expansion is performed, a difference density between the inverse Fourier transform density obtained by an inverse Fourier expansion device and a projection transform density obtained by density projection transform is obtained, and cord exposure defect detection processing is performed by determining cord exposure when there is periodicity in the difference density and determining a stamped character when there is no periodicity in the difference density. <P>COPYRIGHT: (C)2011,JPO&amp;INPIT

Description

  The present invention relates to an image processing method, an image processing apparatus, a program, and a recording medium for inspecting a defect of an inspection object based on an image subjected to image processing, and more specifically, an industrial product typified by an automobile tire. The present invention relates to an image processing method, an image processing apparatus, a program, and a recording medium capable of inspecting a linear defect formed on the surface of the image, particularly a code exposure defect, by performing image processing.

  An image processing apparatus for industrial products represented by automobile tires and rubber products, for example, an inspection apparatus for inspecting industrial products, images an inspection object with an imaging device such as a camera, and is based on the captured image. Perform defect inspection.

  As a method for manufacturing a tire, for example, there is a method of vulcanizing by pressing against a mold. Specifically, bead parts, nylon, textiles, rubber sheets containing steel cords, and rubber fabrics are wound around the tire drum in turn, and they are inflated from the inside into a tire shape with rubber balloons called bladders. Then, press against the mold and vulcanize. In order to remove air between the bladder and the tire during vulcanization, a groove is dug in the bladder, and the shape of the groove is transferred to the tire. The shape of the groove transferred to the tire is a fixed pattern in an oblique direction and is called a “blader groove”. The “blader groove” is not formed in the center portion inside the tire. The center portion is a central portion in a direction orthogonal to the tire rotation direction.

  When manufacturing a tire, a non-defect portion is formed on the inner side of the tire, which is not a defect but causes a component that becomes a noise when performing defect inspection using image processing. The non-defect portion includes, for example, the above-described bladder groove, as well as surface shapes such as an inner surface release agent, a molded stamp, and a stamped character.

  Furthermore, when manufacturing a tire, a defect, for example, a defect having linearity (hereinafter referred to as “linear defect”) may be formed inside the tire. Table 1 classifies linear defects.

  The linear defect includes a code exposure defect and an uneven defect. The cord exposure defect includes a failure in which the cord inside the tire is exposed on the surface, and the unevenness defect includes a failure called an inner surface concave or inner surface convex, which are caused by the causes shown in Table 1, respectively. The cord is a ply cord, and is a nylon material, a polyester material, or a steel material that is attached in a radial shape (radial shape).

  FIG. 14 is a diagram illustrating an example of an image obtained by photographing the inside of the tire on which the code exposure defect is formed. An image 100 shown in FIG. 14A is an image showing a code exposure defect 101 that has little unevenness and spreads over a wide area, and an image 110 shown in FIG. 14B has large unevenness and is locally formed. This is an image showing a code exposure defect 111. The code exposure defect is formed when the rubber is thinned. 14 (a) and 14 (b) show bladder grooves 102 and 112, respectively.

  As a first conventional technique, there is a tire inspection apparatus described in Patent Document 1. This tire inspection apparatus detects a code exposure defect using wavelet transform, which is a type of waveform processing. The wavelet transform is a frequency analysis conversion process that enables analysis of a signal whose characteristics change over time. In other words, the input signal is obtained by scaling the wavelet (small wave) with inherent spread on a plane with time and frequency as the coordinate axes, adding and moving the tire position corresponding to time, and moving and scaling and adding them together. It represents the waveform. The photographic signal is wavelet transformed by an analyzing wavelet that is a numerical filter having a center frequency at a frequency peculiar to the code exposure defect, and the code exposure defect is determined based on the conversion result.

  As a second conventional technique, by performing binarization processing using density projection average as preprocessing, or by binarizing the differential image to remove minute edges, and then performing Hough transform, a straight line of unevenness There is a way to detect sex.

JP 2007-333531 A

  However, the first conventional technique is a matching method for inspecting whether or not there is a defect that matches the captured image by changing the wavelet that is a waveform pattern and the inspection position, so that the calculation cost is very high. There is. In the second conventional technique, the type of tire, for example, the diameter, width and flatness of the tire, the condition of the surface of the tire, and the positional relationship of the illumination affect the image, so the inspection stability is lacking. There is a problem that inspection accuracy is low.

  In addition, since the inspection method is different between the first conventional technique for performing the code exposure defect inspection and the second conventional technique for detecting the linearity of the unevenness, each requires an inspection time, and the tact time is different. Time greatly depends on their inspection time.

  An object of the present invention is to provide an image processing method, an image processing apparatus, a program, and a recording capable of stabilizing inspection and improving inspection accuracy without depending on the type of tire, the state of the tire surface, and the positional relationship of illumination. To provide a medium.

The present invention includes a plurality of pixels arranged at intersections of a plurality of parallel lines extending in a predetermined first direction and a plurality of parallel lines extending in a second direction orthogonal to the first direction. This is an image processing method executed by an image processing apparatus that processes an image that is an image of a target object on which a linear pattern composed of periodic linear concave or convex portions is formed. And
A density conversion step of converting the density of each pixel into an average density obtained by averaging the densities of the pixels on the line in the second direction in which each pixel is included;
A Fourier expansion step of Fourier-expanding the density of each pixel converted in the density conversion step, using the position of each pixel as a variable for one of the lines in the first direction;
A frequency determination step of determining the frequency component Fourier expanded in the Fourier expansion step as a frequency component of the code exposure band and a remaining frequency component excluding the frequency component of the code exposure band;
An inverse Fourier expansion step of performing an inverse Fourier expansion on the remaining frequency components excluding the frequency components of the code exposure band in the frequency determination step;
A difference concentration calculation step for obtaining a difference concentration between the projected conversion concentration obtained in the concentration conversion step and the inverse Fourier expansion concentration obtained in the inverse Fourier expansion step;
A code exposure determination step for determining whether or not the difference concentration obtained in the difference concentration calculation step is a difference concentration corresponding to the code exposure;
The difference density determined in the code exposure determination step includes an engraved character determination step in which it is determined that the code exposure is present if there is periodicity, and is determined as an imprinted character if there is no periodicity. .

In the present invention, the image is an image of the inside of the tire,
The predetermined first direction is a tire rotation direction.

In the present invention, the image is an image of the inside of the tire,
The predetermined first direction is a direction of a plurality of angles within a predetermined angle range with respect to the tire rotation direction.

In the invention, it is preferable that the linear pattern is a bladder groove.
The present invention also provides a plurality of pixels arranged at the intersections of a plurality of parallel lines extending in a predetermined first direction and a plurality of parallel lines extending in a second direction orthogonal to the first direction. A density of each pixel of an image obtained by imaging an object on which a linear pattern consisting of a linear concave or convex portion having a periodicity is formed. Density conversion means for converting the density of pixels on the direction line into an average density, and
Fourier expansion means for Fourier-expanding the density of each pixel converted by the density conversion means, with the position of each pixel as a variable for one of the lines in the first direction;
A frequency determination means for determining a frequency component obtained by removing a frequency component of the code exposure band and a frequency component of the code exposure band from the frequency component Fourier-expanded by the Fourier expansion means;
Inverse Fourier expansion means for performing inverse Fourier expansion on the remaining frequency components determined by the frequency determination means;
A difference density calculation means for obtaining a difference density between the projected transformation density obtained by the density conversion means and the inverse Fourier transform density obtained by the inverse Fourier expansion means;
An image processing apparatus, comprising: a difference density calculated by the difference density calculation means; if there is periodicity, it is determined that the code is exposed;

The present invention also provides a computer,
An image composed of a plurality of pixels arranged at intersections of a plurality of parallel lines extending in a first direction defined in advance and a plurality of parallel lines extending in a second direction orthogonal to the first direction. The density of each pixel of an image obtained by imaging an object on which a linear pattern having a periodic linear concave or convex portion is formed is indicated on the line in the second direction including each pixel. Density conversion means for converting the density of pixels into an average density,
Fourier expansion means for Fourier-expanding the density of each pixel converted by the density conversion means, using the position of each pixel as a variable for one of the lines in the first direction;
A frequency determination means for determining a frequency component obtained by removing a frequency component of the code exposure band and a frequency component of the code exposure band from the frequency component Fourier-expanded by the Fourier expansion means;
Inverse Fourier expansion means for performing inverse Fourier expansion of the remaining frequency components determined by the frequency determination means;
If there is periodicity in the difference density calculation means for obtaining the difference density between the projected transformation density obtained by the density conversion means and the inverse Fourier transform density obtained by the inverse Fourier expansion means, and the difference density obtained by the difference density calculation means, if there is periodicity It is a program for determining that the code is exposed and functioning as a stamped character determining means for determining a stamped character if there is no periodicity.
The present invention is also a computer-readable recording medium on which the program is recorded.

  According to the present invention, a code exposure candidate is extracted from a Fourier expansion histogram, a difference density between the projective transformation density and the inverse Fourier expansion density is obtained, and the code is calculated based on an integrated value of a portion having a certain size or more with respect to the difference density. Since the exposure is judged, whether the cord exposure is continuous or locally present, the tire surface bladder groove, the inner surface release agent, and the surface shape are harmful to the code exposure inspection. Thus, the code exposure defect can be detected with high accuracy.

  In addition, since Fourier expansion processing, inverse Fourier expansion processing, and difference processing are executed in this order, the type of inspection object, the condition of the surface of the inspection object, and the effects of illumination on the inspection object during inspection are eliminated. Thus, the inspection can be stabilized.

  Therefore, by image processing, it is possible to detect a code exposure defect that is characterized by linearity, without depending on the type of inspection object, for example, the type of tire, the condition of the tire surface, and the positional relationship of illumination. And higher accuracy can be achieved.

  According to the invention, the image is an image of the inside of the tire, and the predetermined first direction is the tire rotation direction, so that the linearity direction of the code exposure defect coincides with the tire rotation direction. In this case, the inspection can be stabilized without depending on the type of tire, the state of the tire surface, and the positional relationship of illumination.

  According to the invention, the image is an image of the inside of the tire, and the predetermined first direction is a direction of a plurality of angles within a predetermined angle range with respect to the tire rotation direction. Even if the direction of linearity of the code exposure defect does not coincide with the tire rotation direction, the inspection can be stabilized without depending on the type of tire, the state of the tire surface, and the positional relationship of illumination.

  According to the present invention, since the linear pattern is a bladder groove, it is possible to detect a code exposure defect by removing the bladder groove from the captured image.

  Further, according to the present invention, code exposure candidates are extracted from a Fourier expansion histogram, a difference density between the projective transformation density and the inverse Fourier expansion density is obtained, and the difference density is determined by an integrated value of a portion having a certain size or more. Since the code exposure is determined, whether the cord exposure is continuous or locally present, the tire surface bladder groove, inner surface release agent, surface By removing noise such as shape, it is possible to detect a code exposure defect with high accuracy.

  In addition, since Fourier expansion processing, inverse Fourier expansion processing, and difference processing are executed in this order, the type of inspection object, the condition of the surface of the inspection object, and the effects of illumination on the inspection object during inspection are eliminated. Thus, the inspection can be stabilized. By image processing, for example, a code exposure defect can be detected without depending on the type of tire, the state of the tire surface, and the positional relationship of illumination, and the inspection can be stabilized.

  According to the present invention, the computer can function as a density conversion unit, a Fourier expansion unit, a frequency determination unit, an inverse Fourier expansion unit, a difference density calculation unit, and an imprinted character determination unit according to the program.

  The present invention can also be provided as a computer-readable recording medium on which the program is recorded.

1 is a block diagram illustrating a configuration of an image processing apparatus 1 according to an embodiment of the present invention. It is a figure for demonstrating density projection conversion. It is a figure which shows an example of the capture image 30 inside the tire without a defect, and the conversion image 31 which carried out the density projection conversion of it. It is a figure which shows an example of the captured image 35 inside the tire with a linear defect, and the converted image 36 which carried out the density | projection conversion of it. It is a figure for demonstrating the method of the supplement of Fourier expansion. It is a figure which shows a Fourier frequency histogram. It is a figure which shows the specific example of a frequency determination result. It is a figure for demonstrating an autocorrelation value. It is a figure for demonstrating a correlation difference value. It is a figure for demonstrating a peak position and a peak space | interval. It is a figure for demonstrating rotation projection conversion. It is a flowchart which shows the process sequence of the code exposure defect detection process which the control part 12 performs. It is a flowchart which shows the procedure of a code | cord | chord exposure determination process. It is a figure which shows the example of the image which image | photographed the tire inner side in which the uneven | corrugated defect and the code | cord | chord exposed defect were formed, and the image 100 shown to Fig.14 (a) is an image which shows the code | cord | chord exposed defect 101 which has few unevenness | corrugations and spreads widely. The image 110 shown in FIG. 14 (b) is an image showing large code irregularities and locally formed code exposure defects 111.

  FIG. 1 is a block diagram showing a configuration of an image processing apparatus 1 according to an embodiment of the present invention. The image processing apparatus 1 is an inspection apparatus that inspects a surface inspection of a molded product made of, for example, rubber, metal, or resin by using image processing. explain. The image processing method according to the present invention is executed by, for example, the image processing apparatus 1.

  The image processing apparatus 1 includes an input unit 11, a control unit 12, a storage unit 13, and an output unit 14. The input unit 11 includes, for example, an input device such as a mouse and a keyboard, and an imaging device including an illumination unit and an imaging unit. The input device is a device that inputs information for instructing the start and end of the inspection and information such as the inspection conditions, for example. The input unit 11 sends the information input by the input device to the control unit 12.

  The photographing device illuminates the inside of a tire with an illumination unit such as a light, and photographs the tire surface irradiated with the illumination light with a photographing unit such as a camera. Since the photographing device cannot photograph the inside of the tire at a time, the photographing device sequentially photographs by moving the position of the inner surface of the tire. The input unit 11 sends an image of the inner surface of the tire photographed by the photographing device to the control unit 12.

  The control unit 12 is constituted by, for example, a central processing unit (abbreviated as CPU), and controls the input unit 11 and the output unit 14 by executing a program stored in the storage unit 13, according to the present invention. A plurality of functions to be described later for performing a linear defect detection process are realized. The storage unit 13 is a readable / writable storage device configured by, for example, a semiconductor memory or a hard disk device, and stores a program executed by the control unit 12 and information used by the control unit 12.

  The output unit 14 is configured by a display device such as a display that outputs information or a printing device such as a printer, and outputs information received from the control unit 12. Alternatively, a recording / reproducing apparatus that can read and write information on a detachable recording medium, a communication apparatus that transmits / receives information to / from another image processing apparatus, and the like can be used as an input device / output device. The control unit 12 determines the presence or absence of a linear defect based on the image received from the input unit 11, and outputs an inspection result based on the determination result to the output unit 14.

  The control unit 12 processes a linear defect, particularly a code exposure defect detection process, in eight steps of the first to eighth steps. The control unit 12 performs “density projection transformation” processing in the first step, performs “Fourier expansion” processing in the second step, performs “frequency determination” processing in the third step, and performs “inverse Fourier expansion” in the fourth step. ”Process,“ Density difference ”process in the fifth step,“ Code exposure determination ”process in the sixth step,“ Engraved character determination ”process in the seventh step,“ Inclination fluctuation ”in the eighth step Process.

  An image received from the imaging device of the input unit 11 (hereinafter referred to as “captured image”) is stored in the storage unit 13. The captured image is composed of pixels arranged in a tire rotation direction that is a predetermined first direction and a direction perpendicular to the tire rotation direction. That is, the pixel is arranged at the intersection of a plurality of lines parallel to the tire rotation direction and a plurality of lines parallel to the direction orthogonal to the tire rotation direction. The captured image is an image in which a linear pattern composed of periodic linear concave or convex portions, for example, a bladder groove is formed. The bladder groove is a linear pattern composed of linear concave or convex portions having periodicity.

  The control unit 12 executes a program stored in the storage unit 13 to perform density conversion means, Fourier expansion means, frequency calculation means, inverse Fourier means, density difference calculation means, code exposure determination means, engraved character determination means, Functions such as tilt variation processing means are realized. The first step is executed by the density conversion means, the second step is executed by the Fourier expansion means, the third step is executed by the period calculation means, the fourth step is executed by the inverse Fourier expansion means, The fifth step is executed by the density difference calculating means, the sixth step is executed by the code exposure determining means, the seventh step is executed by the stamped character determining means, and the eighth step is executed by the inclination variation processing means. The

[First step] (Density projection conversion)
In the first step, the control unit 12 performs density projection conversion processing. The density projection conversion is to convert the density of pixels on a line in one direction into an average density obtained by averaging the densities of pixels on the line. Specifically, the control unit 12 converts, for each line in a direction orthogonal to the tire rotation direction, the density of each pixel of the captured image into a density obtained by averaging the density of the pixels on the line including each pixel. . When the captured image has uneven density due to illumination, density projection conversion is performed on the image from which the uneven density is removed from the captured image. The method for removing the density unevenness is not particularly limited.

  FIG. 2 is a diagram for explaining density projection conversion. FIG. 2A is an example of a captured image 20 to be subjected to density projection conversion. Of the pixels constituting the captured image 20, the density of the pixels constituting the white part is higher than the density of the pixels constituting the black part. The horizontal direction of the captured image 20 is the X direction, and the vertical direction is the Y direction. FIG. 2B is a graph 21 showing values obtained by adding pixel densities for each line in the X direction of the captured image 20. That is, the graph 21 shows the value obtained by adding the pixel density of the captured image 20 for each line in the X direction. The horizontal direction of the graph 21 is a total obtained by adding the pixel density for each line in the X direction of the captured image 20, and the vertical direction is the Y direction of the captured image 20. The average density is a value obtained by dividing the density obtained by adding the densities of the pixels on the line including each pixel by the number of pixels on the line including each pixel. The control unit 12 converts the density of each pixel into an average density of a line including each pixel.

  FIG. 3 is a diagram illustrating an example of a captured image 30 inside the tire without a defect and a converted image 31 obtained by subjecting the captured image 30 to density projection conversion. FIG. 3A shows a captured image 30, and a bladder groove 301 is shown in the captured image 30. The horizontal direction of the captured image 30 is the tire rotation direction, and the vertical direction of the captured image 30 is a direction orthogonal to the tire rotation direction. The bladder groove 301 is a plurality of grooves formed in the tire at the time of vulcanization. In the captured image 30, the bladder groove 301 is shown as an oblique groove from the upper right to the lower left. FIG. 3B shows a converted image 31 obtained by converting the density of each pixel of the captured image 20 into an average density obtained by averaging the densities of the pixels on the vertical line.

  FIG. 4 is a diagram illustrating an example of a captured image 35 inside a tire having a linear defect and a converted image 36 obtained by performing density projection conversion on the captured image 35. FIG. 4A shows a captured image 35, and a partial image of a bladder groove 351 and a linear defect 352 is shown in the captured image 35. FIG. 4B is a converted image 36 obtained by converting the density of each pixel of the captured image 35 into an average density obtained by averaging the densities of the pixels on the vertical line. A partial image 362 including a darker part than the surroundings is shown at a position corresponding to the linear defect 352.

  By performing density projection conversion, noise in directions other than the direction in which density projection conversion is performed can be reduced. Since the code exposure defect and the unevenness defect are characterized by linearity in the direction orthogonal to the tire rotation direction, by performing density projection conversion, noise in directions other than the direction orthogonal to the tire rotation direction, for example, the bladder groove and the inner surface Noise such as mold release agent can be reduced.

[Second step] (Fourier expansion)
In the second step, the control unit 12 performs a Fourier expansion process. Specifically, the control unit 12 determines the pixel position for pixels on one line in a predetermined first direction, for example, a line in the tire rotation direction, with respect to the image on which the density projection conversion has been performed. Is the variable x and the density of each pixel is the function f (x), and Fourier expansion is performed. Assuming that a Fourier-expanded function is F (x) and the density function f (x) is obtained by 0 ≦ x ≦ 1, F (x) is calculated by the equation (1).
_{F (x) = a 0/} 2 + Σ {a n × cos (n × π × x / L)
+ B _n × sin (n × π × x / L)} (1)

  Here, Σ is a symbol representing the total sum of a number sequence, and the range of the total Σ is an integer from n = 1 to infinity, but in reality, the value of n needs to be truncated at any value. There is. n is the frequency of defects, that is, the number of defects, and the value of n is determined by how far the data related to the non-defective sample can be expanded and restored.

The coefficient _{a n} and the coefficient _{b n,} respectively formula (2), obtained by (3).
a _n = 1 / l × ∫f (x) × cos (n × π × x / L) dx (2)
b _n = 1 / l × ∫f (x) × sin (n × π × x / L) dx (3)
Here, ∫ is an integral symbol, and the range of the integral ∫ is −1 ≦ x ≦ 1.

  Since the calculation in Expression (1) is performed only in the range of 0 ≦ x ≦ 1, it is necessary to supplement the Fourier expansion in the range of −1 ≦ x <0. The method of replenishment is not limited, and it may be replenished in any way.

FIG. 5 is a diagram for explaining how to replenish Fourier expansion. FIG. 5A is a graph 41 showing a function of 0 ≦ x ≦ 1 and f (x) = cx. c is a coefficient. FIG. 5B is a graph 42 showing the first function f ₁ (x) for replenishment. The function f ₁ (x) is f ₁ (x) = c (x + 1) in the range of −1 ≦ x <0, and 0 ≦ x ≦ 1 and f ₁ (x) = f (x). . FIG. 5C is a graph 43 showing the second function f ₂ (x) for replenishment. The function f ₂ (x) is in the range of −1 ≦ x <0, f ₂ (x) = − cx, 0 ≦ x ≦ 1, and f ₂ (x) = f (x). FIG. 5D is a graph 44 showing the third function f ₃ (x) for replenishment. The function f ₃ (x) is f ₃ (x) = c (x + 1) in the range of −1 ≦ x ≦ 1.

The right side of the equation (1) is the sum of continuous functions and does not converge to the discontinuous first function f ₁ (x) shown in the graph 42. Therefore, the range of x is changed from the range of 0 ≦ x ≦ 1. It is also possible to replenish with the third function f ₃ (x) shown in the graph 44 changed to the range of −1 ≦ x ≦ 1. Which function is used for replenishment is not particularly limited, but the second function f ₂ (x) shown in FIG. 5C is often used.

The control unit 12 obtains the amplitude “2 × √ (a _n ² + b _n ² )” from the coefficients obtained by the equations (2) and (3). √ is a square root symbol.

[Third step] (Frequency judgment)
FIG. 6 shows a Fourier frequency histogram. In the Fourier frequency histogram, the horizontal axis represents frequency, and the vertical axis represents amplitude. FIG. 6A is a Fourier frequency histogram 51 when there is no code exposure defect, and FIG. 6B is a Fourier frequency histogram 52 when there is a code exposure defect.

  The Fourier frequency histogram 51 shows a portion 511 having a high amplitude at the frequency of the bladder groove, and the Fourier frequency histogram 52 shows a portion 521 having a high amplitude at the frequency of the bladder groove and a portion having a high amplitude at the frequency of the code exposure defect. 522 is shown. The portion 522 having a high amplitude at the frequency of the code exposure defect is higher than the portion 521 having a high amplitude at the frequency of the bladder groove. The portion 512 of the Fourier frequency histogram 51 does not have a portion with a high amplitude at the frequency of the code exposure defect.

  In the third step, the control unit 12 performs frequency determination processing. Specifically, the control unit 12 calculates an autocorrelation value from the captured image before performing the density projection conversion, and obtains a fluctuation period of the calculated autocorrelation value (hereinafter also referred to as “fluctuation interval”). Calculate the number of bladder grooves.

  In the case of an image characterized by a periodic change such as a bladder groove, the autocorrelation value also periodically changes. Therefore, the number of bladder grooves can be calculated by obtaining the autocorrelation value fluctuation interval. The number of bladder grooves is calculated by the first to fifth procedures. The control unit 12 calculates the autocorrelation value in the first procedure, calculates the correlation difference value in the second procedure, detects the peak position of the correlation difference value in the third procedure, and correlates in the fourth procedure. The peak interval of the difference value is calculated, and the number of bladder grooves is calculated in the fifth procedure.

  In the first procedure, the control unit 12 sets a predetermined rectangular template region in the captured image, and calculates an autocorrelation value between a pixel in the template region and a pixel in the region where the template region is moved. calculate.

  FIG. 7 is a diagram illustrating a specific example of the frequency determination result. In the case of code exposure, an approximate trend appears in the Fourier frequency histogram as shown in FIG. Using this property, the frequency range 56 (40 to 255 in the example of FIG. 7), the intensity lower limit 57 (030 in the example of FIG. 7), and the cumulative intensity 58 (FIG. 7) are obtained for the target region 55 of the captured image 54. In the example shown in FIG. More specifically, for data in a specified frequency range of the frequency histogram, for example, “40”, the excess from the lower limit of intensity, for example “30”, is accumulated, and the accumulated value, that is, the accumulated intensity is designated threshold, for example “35”. "Is exceeded, it is determined that the code is exposed.

The intensity is determined based on the peak value in the high frequency range, and if there is a code exposure defect (NG), the spectrum value corresponding to the peak value is cumulatively added and determined based on the integrated value of the NG intensity. This intensity is an amplitude (= √ [a _n ² + b _n ² ]). In such frequency determination, a stamped character is detected in addition to the code exposure as described later.

FIG. 8 is a diagram for explaining the autocorrelation value. The target area 61 is a rectangular area of an image for calculating an autocorrelation value in the captured image, the vertical dimension in the Y direction which is the vertical direction in FIG. 8 is “H”, and the horizontal direction in FIG. The lateral dimension in the X direction is “W”. In the template region 66, the vertical dimension in the A direction is “H _T ”, and the horizontal dimension in the B direction is “W _T ”. The upper left position of the target area 61 is set as the origin of the XY coordinates.

The control unit 12 sets the template area 66 at an arbitrary position in the target area 61. The dimension H _T of the template region 66 matches the dimension H of the target region 61, and the horizontal dimension W _T of the template region 66 is smaller than the horizontal dimension W of the target region 61. Next, the control unit 12 sets a correlation value calculation area 67 at a position where the template area 66 is moved in the X direction. The correlation value calculation area 67 has the same dimensions as the template area 66, the vertical dimension is “H _T ”, and the horizontal dimension is “W _T ”. The vertical dimension and the horizontal dimension are represented by the number of pixels.

  The control unit 12 superimposes the template area 66 and the correlation value calculation area 67 to calculate the correlation value of each pixel. The position of the pixel in the template area 66 is expressed in a relative coordinate system with the upper left position of the template area 66 as the origin and is set as coordinates (i, j), and the position of the upper left pixel in the correlation value calculation area 67 is the XY coordinate. If the coordinates are (x, y) in the system, the position of the pixel in the correlation value calculation area 67 is (x + i, y + j) in the XY coordinate system.

  In the present embodiment, the correlation value between the pixel density of the template area 66 and the pixel density of the correlation value calculation area 67 is calculated using a normalized correlation method. Specifically, the density of the pixel at the coordinates (i, j) in the template area 66 is T (i, j), and the density of the pixel at the coordinates (x + i, y + j) in the correlation value calculation area 67 is I (x + i, If y + j), the correlation value R (x, y) is calculated by the equation (4) by the normalized correlation method. The control unit 12 calculates the normalized correlation value by moving the correlation value calculation area 67 one pixel at a time in the X direction in the target area 61.

Here, R (x, y) is a normalized correlation value (hereinafter also simply referred to as “correlation value”), and “I _H ” is an average value of the density of pixels in the correlation value calculation area 67. , “T _H ” is an average value of the densities of the pixels in the template region 66, and x∈ {0, 1, 2,..., W−W _T }, y∈ {0, 1, 2,. _{-H T}, i∈ {0,1,2,} ..., W T -1}, j∈ {0,1,2, ..., a H T -1}.

  As shown in the equation (4), the correlation value R (x, y) is a correlation of a value obtained by subtracting the average value of the density from the density of each pixel, so even if the density of the captured image changes. The correlation value R (x, y) can be calculated stably. When formula (4) is expanded, formula (5) is obtained.

Here, “N” is the size of the template region, and N = W _T × H _T.
The correlation value R (x, y) is a value in the range of −1 ≦ R (x, y) ≦ 1, R (x, y) = “1” indicates perfect match, and R (x, y) = “−1” indicates a complete mismatch, that is, the image in the correlation value calculation area 67 is an inverted image of the image in the template area 66, and R (x, y) = “0” indicates no correlation. Further, since the correlation value R (x, y) is expressed as an integer, a value obtained by multiplying a constant such as “10,000” may be used as the correlation value. That is, in order to convert the decimal value in the range of -1.0 to 1.0 into an integer, it is multiplied by 10,000.

  In the present embodiment, the control unit 12 sets the template region 66 at an arbitrary position in the target region 61. However, in another embodiment, for example, the template region 66 is set at the center in the X direction in the target region 61. It may be configured to set and calculate the correlation value.

In the second procedure, the control unit 12 performs a differential process on the autocorrelation value of the pixel in the template region 66 calculated in the first procedure and the autocorrelation value of the pixel having a predetermined interval in the X direction to perform correlation. The difference value is calculated. Specifically, the control unit 12 calculates the correlation difference value D (x, y) from the autocorrelation value R (x, y) using the equation (6).
D (x, y) = R (x + Δx, y) −R (x, y) (6)

  Here, the difference width Δx is a predetermined interval, for example, “5”. Since the correlation difference value is a flatter value than the autocorrelation value, the number of bladder grooves can be easily and reliably calculated using the calculated correlation difference value. Flat means that when expressed in a graph, the rise and fall are clear and are not easily disturbed by noise.

  FIG. 9 is a diagram for explaining the correlation difference value. FIG. 9A is a diagram illustrating a captured image 70 photographed by the photographing device of the input unit 11. FIG. 9B is a diagram in which a graph 71 in the captured image 70 is extracted. Although the graph 71 is included in the captured image 70, since it is difficult to discriminate, it is extracted and shown in FIG.

  In the captured image 70, a target area 61 and a template area 66 are shown. The graph 71 shows a correlation difference value threshold 73 which is a threshold used for detecting the peak position of the correlation difference value, a waveform 74 indicating the autocorrelation value, and a waveform 75 indicating the correlation difference value. In the graph 71, the horizontal axis indicates the position of the pixel in the X direction, and the vertical axis indicates the value of the autocorrelation value.

  In the third procedure, the control unit 12 detects the peak position of each correlation difference value calculated in the second procedure, that is, the maximum position. The control unit 12 dynamically moves the correlation difference value threshold 73 in the graph 71 shown in FIG. 8B in the vertical direction so that the waveform 75 of the correlation difference value exceeds the line of the correlation difference value threshold 73. Is determined as the peak position of the correlation difference value. In the fourth procedure, the control unit 12 calculates the average value of the intervals between adjacent peak positions among the peak positions in the template region detected in the third procedure.

  FIG. 10 is a diagram for explaining peak positions and peak intervals. FIG. 10A is a diagram illustrating a graph 81 that is an example of a waveform of a correlation difference value. FIG. 10B is an enlarged view showing a region 82 in the graph 81.

  A graph 81 shows a waveform 75 of a correlation difference value, a correlation difference value threshold 73, a maximum value 83 of the waveform 75, a peak position 84, and a peak interval T that is an interval between adjacent peak positions. The horizontal axis of the graph 81 is the pixel position X at which the correlation difference value is calculated, and the vertical axis is the correlation difference value D. The correlation difference value threshold 73 is calculated by “(correlation difference value threshold 73) = (waveform maximum 83) × (threshold ratio)” by setting a threshold ratio as a ratio of the threshold to the maximum value 83 of the waveform, for example. Is done.

  10B, the peak position 84, the rising position 86 where the waveform 75 exceeds the correlation difference value threshold 73, and the falling position 87 where the waveform 75 falls below the correlation difference value threshold 73 are shown. Is shown. The control unit 12 calculates the peak position 84 by “(peak position 84) = {(rise position 86) + (fall position 87)} / 2”. Further, the control unit 12 calculates the interval between the peak positions 84 adjacent to each other among the plurality of peak positions 84 to obtain the peak interval T. The control unit 12 calculates an average value of the plurality of peak intervals T.

  In the fifth procedure, the control unit 12 calculates the total number of bladder grooves in the captured image based on the average value of the peak intervals calculated in the fourth procedure, and takes the calculated number from the total number of bladder grooves. Calculate the period of the bladder groove in the embedded image.

  In another embodiment, the control unit 12 returns to the first procedure after the processing in the fifth procedure is completed, moves the template region 66 in the X direction, sets a new template region 66, and again You may perform the process of a 1st-5th procedure. Since the correlation value is calculated a plurality of times by moving the template region 66, even if foreign matter such as dust adheres to the template region 66 set first, if no foreign matter adheres to the new template region 66, The number of bladder grooves can be accurately calculated. Therefore, the control unit 12 can reliably calculate the number of bladder grooves.

  Table 2 is a table showing classification of objects by Fourier frequency. The defect portion is an uneven defect having a frequency of “1 to (z−1)” Hz and a coat exposure defect having a frequency of “40 to 150” Hz. The non-defect portion has a joint (convex line) and an internal release agent droop with a frequency of “1 to (z−1)” Hz, a bladder groove with a frequency of “z to 39” Hz, and a frequency of “40 to 40”. Scattered inner mold release agent and stamped characters that are 150 "Hz. The frequency z is a value determined by the number of bladder groups. Since the number of bladder grooves varies depending on the type of tire and the angle of the camera, the number calculated in the bladder groove number calculation process is taken as the value of z.

  The color of the defect is black, but the color of the internal release agent dripping and spraying internal release agent is white. Therefore, the internal release agent dripping and spraying internal release agent are separate processes, and the defect is determined by the color. Done. Further, defect determination is separately performed for joints (convex lines) and stamped characters. The non-defect portion other than the bladder groove is subjected to defect inspection before the control unit 12 performs the processing from the first step, and the tire having the non-defect portion other than the bladder groove has already been excluded.

  In this embodiment, the frequency determination by calculating the number of bladder grooves is performed after the second step, but may be performed before the second step or may be performed before the first step.

[Fourth step] (Inverse Fourier expansion)
In the fourth step, the control unit 12 performs an inverse Fourier expansion process. Specifically, the control unit 12 uses a frequency component representing the period of the bladder groove calculated by the frequency determination process from the frequency component in the middle frequency to high frequency range (= 40 to 150) Fourier-expanded by the Fourier expansion process. , And inverse Fourier expansion is performed on the remaining frequency components to generate a projective transformation density again. That is, inverse Fourier expansion is performed by removing the term of the frequency z to 39 Hz from the term of the function F (x) obtained by the equation (1). By this inverse Fourier expansion, the bladder groove is removed, and an image in which a defective portion is left can be obtained.

[Fifth step] (Density difference)
In the fifth step, the control unit 12 performs defect detection processing. Specifically, the control unit 12 obtains a difference density between the projective transformation density obtained in the first step and the inverse Fourier expansion density obtained in the fourth step. This is the projected conversion density of the defective part.

[Sixth Step] (Cord exposure judgment)
In the sixth step, an average difference value is obtained by Equation (7) for the difference density obtained in the fifth step.
Difference average value = total difference density / number of difference density data (7)

  Next, if the density of each pixel is equal to or lower than the upper limit threshold obtained by adding the upper limit allowable value to the difference average value calculated by Expression (7) and equal to or higher than the lower limit threshold obtained by subtracting the lower limit allowable value from the difference average value, If it is determined not to be defective (OK) and exceeds the upper threshold and is lower than the lower threshold, it is determined that there is a possibility of code exposure defect (NG), and the next process is executed. For example, 40 is selected as the upper limit allowable value and the lower limit allowable value for the difference average value.

  That is, an absolute value (difference absolute value) is obtained for the difference density obtained in the fifth step, and the maximum position of the difference absolute value is obtained.

  Next, for the absolute difference value, the sum of absolute difference values that are equal to or lower than the upper limit maximum position obtained by adding the upper limit allowable value to the maximum position and within the range equal to or higher than the lower limit maximum position obtained by subtracting the lower limit allowable value from the maximum position. If the sum of the absolute differences is equal to or less than the upper limit threshold value, it is determined that there is no code exposure defect (OK), and if it exceeds the upper limit threshold value, it is determined that there is a code exposure defect (NG). The upper limit allowable value and the lower limit allowable value with respect to the maximum position are each selected as 20, for example. The upper limit allowable value for the maximum position is selected to be 1000, for example.

[Seventh Step] (Determination of stamped characters)
In the above-described sixth step, when it is determined that there is a code exposure defect, a correlation value is obtained in the horizontal direction using the portion determined to have a code exposure defect as a reference template. When periodicity is found in this correlation value, it is determined as a code exposure defect, and when periodicity is not observed, it is determined as a stamped character.

[Eighth step] (Inclination fluctuation)
In the code exposure defect detection process in the first to sixth steps, the control unit 12 executes the gradient for performing density projection conversion as a direction orthogonal to the tire rotation direction, but the direction of the code exposure defect is orthogonal to the tire rotation direction. It does not always coincide with the direction in which it is performed, and may be slightly inclined. If the direction of the code exposure defect does not match the direction in which the density projection conversion is performed, the code exposure defect cannot be detected by the code exposure defect detection process in the first to sixth steps.

  Therefore, the control unit 12 executes the code exposure defect detection process in the first to sixth steps by tilting the direction in which the density projection conversion is performed with respect to the direction orthogonal to the tire rotation direction. In this case, the Fourier expansion is performed by inclining the direction of the line for performing the Fourier expansion by the same angle. The tilt angle is a predetermined angle range, for example, a range of ± 5 degrees. Inclining the density projection conversion by tilting the direction in which the density projection conversion is performed with respect to the direction orthogonal to the tire rotation direction is hereinafter referred to as a rotational projection conversion.

  FIG. 11 is a diagram for explaining the rotational projective transformation. The horizontal direction of the captured image 90 is the tire rotation direction, and the vertical direction of the captured image 90 is a direction orthogonal to the tire rotation direction. A direction orthogonal to the tire rotation direction is a D1 direction, a direction in which density projection conversion is performed is a D2 direction, and an angle of the D2 direction with respect to the D1 direction is K degrees. The shift width W1 is a distance between a pixel in the D1 direction and a pixel in the D2 direction at the upper end and the lower end of the captured image 90, and is expressed in units of one pixel. When the number of pixels in the D1 direction of the captured image 90 is M, tanK = W1 / (M / 2).

  The control unit 12 first executes the code exposure defect detection process in the first to eighth steps with the shift width W1 = 0, and then adds “1” to the shift width W1 to obtain the first to eighth steps. The code exposure defect detection process is executed in steps, and thereafter, “1” is sequentially added to the shift width W1 until K = 5, and the code exposure defect detection process in the first to eighth steps is repeatedly executed. The same calculation is performed for an angle of 0 degrees to an angle of -K degrees. When the line in the D2 direction passes through two pixels adjacent in the horizontal direction in FIG. 10, the rotation projection conversion is performed with the pixel whose center position is closest to the line in the D2 direction as a pixel on the line in the D2 direction. .

  In the embodiment described above, the control unit 12 performs the rotation projection conversion on the entire captured image and repeatedly executes the code exposure defect detection processing in the first to eighth steps. When density projection conversion is performed on the entire captured image, there is a possibility that the defective portion may be erased because the number of pixels to which the density is added is large. Therefore, the captured image is cut in the tire rotation direction, divided into a plurality of blocks in a direction orthogonal to the tire rotation direction, and rotation projection conversion is performed for each block, and the code exposure in the first to eighth steps is performed. It is preferable to repeatedly execute the defect detection process. In this case, each block corresponds to the target area 61.

  FIG. 12 is a flowchart showing a processing procedure of code exposure defect detection processing executed by the control unit 12. When information instructing the start of inspection is input from the input device of the input unit 11 and the control unit 12 receives a captured image from the imaging device of the input unit 11, the process proceeds to step A1.

  In step A1, which is a density conversion process, the control unit 12 performs a density projection conversion process. The density projection conversion process is the process in the first step described above. In step A2, which is a Fourier expansion process, the control unit 12 performs a Fourier expansion process. The Fourier expansion process is a process in the second step described above. In step A3 which is a frequency determination step, the control unit 12 performs a bladder groove number calculation process. The bladder groove number calculation process is the process in the third step described above.

  In step A4, the control unit 12 performs an inverse Fourier expansion process, and a frequency component representing the period of the bladder groove calculated by the frequency determination process from the frequency components in the middle frequency range to the high frequency range Fourier expanded by the Fourier expansion process. , And inverse Fourier expansion is performed on the remaining frequency components to generate a projective transformation density again. By this inverse Fourier expansion, the bladder groove is removed, and an image in which a defective portion is left can be obtained.

  In step A5, the control unit 12 performs defect detection processing, and obtains a difference density between the projective transformation density obtained in the first step and the inverse Fourier expansion density obtained in the fourth step. Next, in step A6, an average difference value is obtained by the above-described equation (7) for the difference density obtained in step A5.

  In step A7, if it is determined in step A6 that there is a code exposure defect, a correlation value is obtained in the horizontal direction using the portion determined to have a code exposure defect as a reference template. When periodicity is found in this correlation value, it is determined as a code exposure defect, and when periodicity is not observed, it is determined as a stamped character.

  In step A8, since the direction of the code exposure defect does not necessarily coincide with the direction orthogonal to the tire rotation direction and may be slightly inclined, the control unit 12 determines the direction in which the density projection conversion is performed as the tire rotation direction. The code exposure defect detection process according to the first to sixth steps is executed with an inclination with respect to the direction orthogonal to the first to sixth steps.

  In step A8, the control unit 12 determines whether or not the tilt range has ended. When the code exposure defect detection processing in the first to eighth steps is not repeatedly executed at an angle in the range of -5 degrees to 5 degrees by performing rotation projection conversion and performing the rotation projection conversion, the inclination range is It is determined that the process has not ended, and the process returns to step A1. When the code exposure defect detection processing in the first to fifth steps is repeatedly performed at an angle K in the range of -5 degrees to 5 degrees by performing rotational projection conversion and performing the rotation projection conversion, the inclination range is completed. It progresses to step A9. In step A9, the control unit 12 determines whether all blocks have been completed. If all blocks have not been completed, the process returns to step A1, and if all blocks have been completed, the code exposure defect detection process ends.

  FIG. 13 is a flowchart illustrating the procedure of the code exposure determination process. In step B1, the difference average value (= total difference density / number of difference density data) is obtained for the difference density obtained in step A5 by the above-described equation (7). Next, in step B2, it is determined whether or not the density of each pixel is equal to or lower than an upper limit threshold obtained by adding the upper limit allowable value to the difference average value and equal to or higher than a lower limit threshold obtained by subtracting the lower limit allowable value from the difference average value. . If the density of each pixel is equal to or lower than the upper limit threshold value obtained by adding the upper limit allowable value to the difference average value and equal to or higher than the lower limit threshold value obtained by subtracting the lower limit allowable value from the difference average value, it is determined that the defect is not defective in step B3. If the upper limit threshold is exceeded and less than the lower limit threshold, it is determined that there is a possibility of code exposure defect (NG), and the process proceeds to the next step B4.

  In Step B4, an absolute value (difference absolute value) is obtained for the difference density obtained in Step B2, and the maximum position of the difference absolute value is obtained. Next, in step B5, the difference absolute value is equal to or less than the upper limit maximum position obtained by adding the upper limit allowable value to the maximum position, and present in the range equal to or greater than the lower limit maximum position obtained by subtracting the lower limit allowable value from the maximum position. Find the sum of values. In step B6, if the sum of the absolute differences obtained in step B5 is equal to or less than the upper threshold, it is determined in step B3 that the code exposure defect is not OK (OK). If the upper limit threshold is exceeded, code exposure is performed in step B7. It is determined that there is a defect (NG).

  In the above-described embodiment, the control unit 12 executes the program stored in the storage unit 13 by a computer, thereby controlling the input unit 11 and the output unit 14 and realizing the above-described functions. The program for realizing is not limited to being stored in the storage unit 13 and may be recorded on a computer-readable recording medium. The recording medium may be a recording medium that can be read by providing a program reading device as an external storage device (not shown) in the image processing apparatus 1 and inserting the recording medium therein, or a storage device of another device. It may be.

  Any recording medium may be used as long as the stored program is accessed from a computer and executed. Alternatively, any recording medium may be configured such that the program is read, the read program is stored in the program storage area of the storage device, and the program is executed.

The recording medium configured to be separable from the image processing apparatus 1 is, for example, a tape recording medium such as a magnetic tape / cassette tape, a magnetic disk such as a flexible disk / hard disk, or a CD-ROM (Compact Disk Read Only Memory) / MO. (Magneto Optical disk) / MD (Mini Disc) / DVD (Digital Versatile Disk) / CD-R (Compact
Disk recordable) / Blu-ray disc and other optical disc recording media, IC (Integrated Circuit) cards (including memory cards) / optical cards and other card recording media, or mask ROM / EPROM (Erasable Programmable Read Only Memory) ) / EEPROM (Electrically Erasable Programmable Read Only Memory) / Recording medium that carries a fixed program including a semiconductor memory such as a flash ROM.

Further, the image processing apparatus 1 may be configured to be connectable to a communication network, and the program may be supplied via the communication network. The communication network is not particularly limited. For example, the Internet, intranet, extranet, LAN (Local Area Network), ISDN (Integrated Services Digital Network), VAN (Value Added Network), CATV (Community Antenna Television) communication network. A communication network such as a virtual private network, a telephone line network, a mobile communication network, or a satellite communication network can be used. In addition, the transmission medium constituting the communication network is not particularly limited, and may be, for example, IEEE1394, USB (Universal Serial Bus), power line carrier, cable TV line, telephone line, ADSL (Asymmetric Digital Subscriber Line) line or the like. , IrDA (Infrared Data Association) or infrared used for remote control, Bluetooth (registered trademark), 802.11 wireless, HDR (High
Data Rate), mobile phone network, satellite link, terrestrial digital network, etc. can also be used. The present invention can also be realized in the form of a computer data signal embedded in a carrier wave in which the program is embodied by electronic transmission.

  As described above, the plurality of pixels arranged at the intersections of the plurality of parallel lines extending in the first direction and the plurality of parallel lines extending in the second direction orthogonal to the first direction. The image processing is performed by the image processing apparatus 1 that performs processing of an image obtained by imaging an inspection target having a linear pattern having a periodic linear concave or convex portion, that is, a code exposure defect. In execution, in step A1 shown in FIG. 12, the density of each pixel is converted into an average density obtained by averaging the densities of the pixels on the line in the second direction in which each pixel is included. In step A2 shown in FIG. 12, the density of each pixel converted in step A1 shown in FIG. 12 is Fourier-expanded for one line in the first direction, using the position of each pixel as a variable. . In step A3 shown in FIG. 12, the period of the linear pattern is calculated. In step A4 shown in FIG. 12, the remaining frequency components obtained by removing the frequency components calculated in step A3 shown in FIG. 12 from the frequency components Fourier-expanded in step A2 shown in FIG. expand. If there is a pixel having a density not included in the predetermined density range in the inverse Fourier developed pixel, it is determined that there is a defect.

  Therefore, by image processing, for example, it is possible to detect defects with linearity, such as code exposure defects, without depending on the type of tire, the state of the tire surface, and the positional relationship of illumination. High accuracy can be achieved.

  Furthermore, the image is an image of the inside of the tire, and the predetermined first direction is the tire rotation direction. Therefore, when the linearity direction of the code exposure defect matches the tire rotation direction, The inspection can be stabilized without depending on the type, the state of the tire surface, and the positional relationship of the illumination.

  In this way, the image is an image of the inside of the tire, and the predetermined first direction is a direction of a plurality of angles within a predetermined angle range with respect to the tire rotation direction. Even if the linearity direction does not coincide with the tire rotation direction, the inspection can be stabilized without depending on the type of tire, the state of the tire surface, and the positional relationship of illumination.

  Further, since the linear pattern is a bladder groove, it is possible to detect a code exposure defect by removing the bladder groove from the captured image.

  Further, the density conversion means is arranged at intersections of a plurality of parallel lines extending in a first direction defined in advance and a plurality of parallel lines extending in a second direction orthogonal to the first direction. Each pixel includes the density of each pixel of an image obtained by imaging an inspection object on which a linear pattern having a periodic linear concave or convex portion is formed. The density of the pixels on the line in the second direction is converted to an average density. The Fourier expansion means Fourier-expands the density of each pixel converted by the density conversion means for one line in the first direction, using the position of each pixel as a variable. The period calculating means calculates the period of the linear pattern. The inverse Fourier expansion means performs inverse Fourier expansion on the remaining frequency component obtained by removing the frequency component of the frequency calculated in the frequency calculation step from the frequency component Fourier expanded by the Fourier expansion means. Then, the defect detection means determines that there is a defect if there is a pixel having a density that is not included in the predetermined density range among the pixels inversely Fourier expanded by the inverse Fourier expansion means.

  Therefore, for example, it is possible to detect defects with linearity such as code exposure defects without depending on the type of tire, the state of the tire surface, and the positional relationship of illumination, and stabilize the inspection. Can be planned.

  Further, the program arranges the computer at intersections of a plurality of parallel lines extending in a predetermined first direction and a plurality of parallel lines extending in a second direction orthogonal to the first direction. An image composed of a plurality of pixels, each pixel including the density of each pixel of an image obtained by imaging an object on which a linear pattern consisting of periodic linear concave or convex portions is formed Density conversion means for converting the density of pixels on the second direction line into an average density, and conversion by the density conversion means for one line of the lines in the first direction using the position of each pixel as a variable Fourier expansion means for Fourier-expanding the density of each pixel obtained, the frequency component of the code exposure band and the frequency of the code exposure band from the frequency components Fourier-expanded by the Fourier expansion means Frequency determination means for determining the frequency components excluding the minutes, inverse Fourier expansion means for inverse Fourier expansion of the remaining frequency components determined by the frequency determination means, projective transformation density obtained by the density conversion means, and inverse Fourier expansion means The difference density calculation means for obtaining the difference density from the inverse Fourier transform density obtained by the above and the difference density obtained by the difference density calculation means are determined as code exposure if there is periodicity, and as a stamped character if there is no periodicity It can be made to function as an imprinted character judging means.

  Further, it can be provided as a computer-readable recording medium in which the program is recorded.

DESCRIPTION OF SYMBOLS 1 Image processing apparatus 11 Input part 12 Control part 13 Storage part 14 Output part

Claims (7)

An image composed of a plurality of pixels arranged at intersections of a plurality of parallel lines extending in a first direction defined in advance and a plurality of parallel lines extending in a second direction orthogonal to the first direction. An image processing method that is executed by an image processing apparatus that performs processing of an image obtained by imaging an object on which a linear pattern composed of periodic linear concave portions or convex portions is formed,
A density conversion step of converting the density of each pixel into an average density obtained by averaging the densities of the pixels on the line in the second direction in which each pixel is included;
A Fourier expansion step of Fourier-expanding the density of each pixel converted in the density conversion step, using the position of each pixel as a variable for one of the lines in the first direction;
A frequency determination step of determining the frequency component Fourier expanded in the Fourier expansion step as a frequency component of the code exposure band and a remaining frequency component excluding the frequency component of the code exposure band;
An inverse Fourier expansion step of performing an inverse Fourier expansion on the remaining frequency components excluding the frequency components of the code exposure band in the frequency determination step;
A difference concentration calculation step for obtaining a difference concentration between the projected conversion concentration obtained in the concentration conversion step and the inverse Fourier expansion concentration obtained in the inverse Fourier expansion step;
A code exposure determination step for determining whether or not the difference concentration obtained in the difference concentration calculation step is a difference concentration corresponding to the code exposure;
An image processing method comprising: determining a code density if the difference density determined in the code exposure determination step is periodic and determining a code exposure if there is periodicity; and determining a stamp character if there is no periodicity. The image is an image of the inside of the tire,
The image processing method according to claim 1, wherein the predetermined first direction is a tire rotation direction. The image is an image of the inside of the tire,
The image processing method according to claim 1, wherein the predetermined first direction is directions of a plurality of angles within a predetermined angle range with respect to a tire rotation direction.   The image processing method according to claim 1, wherein the linear pattern is a bladder groove. An image composed of a plurality of pixels arranged at intersections of a plurality of parallel lines extending in a first direction defined in advance and a plurality of parallel lines extending in a second direction orthogonal to the first direction. The density of each pixel of an image obtained by imaging an object on which a linear pattern having a periodic linear concave or convex portion is formed is indicated on the line in the second direction including each pixel. Density conversion means for converting the density of pixels into an average density, and
Fourier expansion means for Fourier-expanding the density of each pixel converted by the density conversion means, with the position of each pixel as a variable for one of the lines in the first direction;
A frequency determination means for determining a frequency component obtained by removing a frequency component of the code exposure band and a frequency component of the code exposure band from the frequency component Fourier-expanded by the Fourier expansion means;
Inverse Fourier expansion means for performing inverse Fourier expansion on the remaining frequency components determined by the frequency determination means;
A difference density calculation means for obtaining a difference density between the projected transformation density obtained by the density conversion means and the inverse Fourier transform density obtained by the inverse Fourier expansion means;
An image processing apparatus, comprising: a difference character calculated by the difference concentration calculation means; if there is periodicity, the code exposure is determined as code exposure; Computer
An image composed of a plurality of pixels arranged at intersections of a plurality of parallel lines extending in a first direction defined in advance and a plurality of parallel lines extending in a second direction orthogonal to the first direction. The density of each pixel of an image obtained by imaging an object on which a linear pattern having a periodic linear concave or convex portion is formed is indicated on the line in the second direction including each pixel. Density conversion means for converting the density of pixels into an average density,
Fourier expansion means for Fourier-expanding the density of each pixel converted by the density conversion means, using the position of each pixel as a variable for one of the lines in the first direction;
A frequency determination means for determining a frequency component obtained by removing a frequency component of the code exposure band and a frequency component of the code exposure band from the frequency component Fourier-expanded by the Fourier expansion means;
Inverse Fourier expansion means for performing inverse Fourier expansion of the remaining frequency components determined by the frequency determination means;
If there is periodicity in the difference density calculation means for obtaining the difference density between the projected transformation density obtained by the density conversion means and the inverse Fourier transform density obtained by the inverse Fourier expansion means, and the difference density obtained by the difference density calculation means, if there is periodicity A program for determining that the code is exposed and, if there is no periodicity, determining that the character is an imprinted character, means a stamped character determining means.   A computer-readable recording medium on which the program according to claim 6 is recorded.