JPH113427A - Method and device for checking image - Google Patents

Abstract

PROBLEM TO BE SOLVED: To decide whether it is good or bad according to check reference that handles with both the difference of density value due to a failure and the size of an area as one body by deciding whether it is good or bad based morphology operation image. SOLUTION: Binary circuits 9a to 9c binarize an absolute value image of difference that is outputted by a finite difference and absolute value circuit 8. Threshold in this case is set by a binary threshold setting circuit 10. Next, morphology operation circuits 11a to 1c perform operation of an absolute value image of difference. The morphology operation makes an infix operation of an object image and a component that acts upon the object image a basic form and gets various conversions by changing components. A faulty area to be detected is partitioned with both the difference of a normal density value due to failure and the size of an area through the combination of the circuits 9a to 9c and 11a to 11c. A decision circuit 13 outputs a failure difference value that shows an area to which a detected failure belongs.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image inspection method and apparatus for comparing and inspecting a reference image and an image to be inspected.
In particular, the present invention relates to an image inspection method and apparatus capable of detecting a defect having a large difference from a normal density value but a small size and a defect having a large size but a small difference from a normal density value.

[0002] When the density of printing on a printed matter is quantitatively represented, "density value" is generally used. But,
The density of the print is not limited to the “density value” and can be represented by “reflectance”, “transmittance”, or other linear or non-linear quantity. In the present invention, the density of printing expressed quantitatively is described as "density value" in a sense including other numerical expressions such as "reflectance".

[0003]

2. Description of the Related Art There are various defects in printed matter, such as defects such as wrinkles, foreign matters, and stains on printing paper, and ink stains, hickey, doctor streak, poor density, and poor color tone that occur during printing. is there. Conventionally, printed matter inspection is mainly performed by visual inspection performed by humans. For example, in the case of printed matter discharged from a folding unit of a sheet-fed printing press or a rotary printing press, a printing machine operator (operator) ) Was removed at the appropriate time and inspected visually.
If a continuous printout is ejected from the printing press and cannot be extracted, the printout is irradiated with flash light that flashes instantaneously in synchronization with the running speed of the printout, and the printout is made stationary using the afterimage of the human eye. Was visually inspected.
Visual inspection requires concentration of the mind and requires a very large work load on the person to be inspected. Further, since the inspection is performed by a person, the inspection standard is ambiguous and it is inevitable to miss a defect. Therefore, proposals have been made to automatically perform printed matter inspection, and various techniques have been disclosed.

For example, in Japanese Patent Publication No. 1-47823, image data read from a picture of a running printed matter when a normal printed matter is printed is stored as reference image data in an image memory, and read out from the image memory. There is disclosed a technique relating to a printed matter inspection apparatus of a method of comparing the reference image data read from a pattern of a printed matter to be inspected during printing with the image data to be inspected on a pixel-by-pixel basis to determine the quality of printing.

[0005]

However, since such an inspection apparatus is based on comparison in pixel units, a defect having a large difference in density value can be detected regardless of the size of the area. However, there is a problem that a defect having a small difference in density cannot be detected no matter how large its area is. In order to avoid this, by integrating the absolute value of the difference between the density values of pixels in a predetermined area, a small difference in density value, which is difficult to distinguish from noise, is detected as a clear significant difference. Since it is easy to realize the integration processing device, the present invention is applied to detection of a slight change in the density value of the entire print image and detection of a doctor streak or a hitkey extending in the transport direction of the printing paper.

However, originally, it is effective to determine the area to be integrated in accordance with the contents of the printed picture. However, since the patterns in printed matter are various, it is not practical for operation to determine the area to be integrated each time the print item is switched. In addition, it is conceivable to preliminarily diversify and subdivide the region to be integrated so as to be able to cope with various kinds of pictures, but it is not realistic in terms of the scale of the apparatus because the amount of calculation in the integration processing apparatus is enormous. SUMMARY OF THE INVENTION It is an object of the present invention to provide an image inspection method and apparatus for performing a pass / fail judgment based on an inspection criterion that treats both the difference in density value and the size of an area in a defect.

[0007]

The above objects are achieved by the present invention described below. That is, the present invention provides an image inspection method for comparing and inspecting a reference image and an inspection target image, performing a binarization operation on a grayscale image using a predetermined threshold value, and generating a binary image corresponding to the threshold value. Obtaining a binarization operation step, and performing a morphology operation on the binarized image corresponding to the threshold value using a predetermined component corresponding to the threshold value to obtain a morphology operation image corresponding to the threshold value; And a quality judgment step of making a quality judgment based on the morphological operation image. According to the present invention, a binarized image obtained in the binarization process can be an image indicating whether or not the binarized image is included in a specific range defined by a pixel value (density value) according to a threshold value. The morphology operation image obtained in the subsequent morphology operation process can be an image formed by extracting a partial image in a specific range with respect to the number of pixels (area) according to the component from the binarized image. In the pass / fail judgment process, pass / fail judgment is performed based on the binarized image obtained as described above. Therefore, the pass / fail judgment is performed based on the inspection criterion that treats both the difference between the normal density value and the area size of the defect as a unit. An inspection method is provided. Further, since the morphological operation in the present invention is performed on a binarized image, the operation process can be simplified as compared with the case of performing on a grayscale image.

Further, the present invention provides a method for performing a binarizing operation on a grayscale image using a plurality of predetermined threshold values to obtain a binarized image corresponding to each of the threshold values. The morphological operation process includes performing a morphological operation on a binarized image corresponding to each of the thresholds using a predetermined component corresponding to each of the thresholds, and performing a morphological operation on the binary image corresponding to each of the thresholds. And the pass / fail judgment step is a pass / fail judgment step of performing pass / fail judgment based on all of the morphological operation images corresponding to each of the threshold values.
According to the present invention, a binarized image obtained in the binarization process can be an image indicating whether or not the binarized image is included in a specific range defined by a pixel value (density value) according to a threshold value. The morphology operation image obtained in the subsequent morphology operation process can be an image formed by extracting a partial image in a specific range with respect to the number of pixels (area) according to the component from the binarized image. A plurality of binarized images are obtained by a plurality of thresholds and a plurality of components corresponding thereto. In the pass / fail judgment process, pass / fail judgment is performed based on the plurality of binarized images obtained as described above. Therefore, pass / fail judgment is performed based on an inspection criterion that treats both the difference between the normal density value and the area size of the defect as one. Further, an image inspection method is provided which has a high degree of coincidence between the inspection standard and a desired standard.

Further, the present invention is an image inspection method wherein the grayscale image is an image obtained by calculating at least an absolute value of a difference based on a reference image and an image to be inspected. According to the present invention, since the pixel value (density value) and the number of pixels (area) of the grayscale image are directly linked to the difference in density value and the size of the area in the defect, the morphological calculation process for the binarized image based thereon is simplified. Is done. Further, the present invention is "an image inspection method in which the morphological operation is an opening operation". According to the present invention, it is possible to obtain a morphological operation image formed by extracting a partial image of a specific range with respect to the number of pixels (area) from a binarized image according to the above-described components in a form suitable for a simple operation. Can be. Further, the present invention is an "image inspection method in which predetermined components corresponding to each of the thresholds correspond to components having a small area with respect to a threshold having a large value." According to the present invention, the general recognition of the weight of a defect matches the tendency of a detected defect.

Further, the present invention provides an image inspection apparatus for comparing and inspecting a reference image and an image to be inspected, by performing a binarization operation on a grayscale image by using a plurality of predetermined threshold values and corresponding to each of the threshold values. A binarizing operation means for obtaining a binarized image to be processed, and performing a morphological operation on the binarized image corresponding to each of the threshold values by using a predetermined component corresponding to each of the threshold values. An image inspection apparatus comprising: a morphological operation unit that obtains an operation image corresponding to the threshold value; and a pass / fail determination unit that performs pass / fail determination based on all of the operation images corresponding to each of the threshold values. According to the present invention, in the binarizing unit, the binarized image can be an image indicating whether or not the image is included in a specific range defined by the pixel value (density value). In the subsequent morphology operation means, the operation image can be an image formed by extracting a partial image in a specific range with respect to the number of pixels (area) according to the constituent elements from the binarized image. Since the pass / fail determination unit performs the pass / fail determination based on all of the plurality of binarized images, the pass / fail determination is performed based on an inspection criterion that treats both the difference in density value and the size of the area as a defect. An inspection device is provided.
Further, since the morphology operation in the present invention is performed on a binary image, the morphology operation means can be simplified as compared with the case of performing on a grayscale image.

[0011]

DESCRIPTION OF THE PREFERRED EMBODIMENTS Next, the present invention will be described with reference to embodiments. FIG. 1 is a block diagram showing the configuration of an apparatus (image inspection apparatus) for implementing the image inspection method of the present invention.
In FIG. 1, reference numeral 1 denotes an image input camera for capturing an image of a printing surface;
2 is an image input buffer for temporarily storing an image, 3 is a synchronization circuit for synchronizing image input, 4 is a reference image memory for storing an image of a non-defective printed matter (reference image), and 5 is a dead zone image calculated from the reference image. A dead zone calculation circuit, 6 is a dead zone image memory for storing a dead zone image, 7 is a circuit for instructing updating of a reference image and creation of a dead zone image, and a reference image updating / dead zone image creation command circuit, and 8 is a circuit for controlling an image to be inspected and a reference image. A difference / absolute value conversion circuit for calculating the absolute value of the difference, 9a,
9b and 9c are binarization circuits for performing binarization operations on images (absolute difference value images), and 10 are binarization threshold setting circuits for setting operation parameters (thresholds and the like) in the binarization circuits 9a, 9b and 9c. , 11a, 11b, 11c are morphological operation circuits for performing morphological operation on an image (binary processed image), and 12 is a morphological operation circuit 11a, 11b,
A component setting circuit for setting operation parameters (components and the like) of 11c, 13 is a morphological operation circuit 11a,
A determination circuit 14 determines the quality of the printed matter based on the outputs of 11b and 11c, and a control circuit 14 outputs an operation amount corresponding to the failure type value output by the determination circuit 13.

The image input camera 1 is installed in a printing machine and captures an image of a printing surface of a web (printing paper) discharged from a printing unit. The image input camera 1 is a line sensor camera,
An image sensor camera or the like can be used. An image signal (analog signal) from the image input camera 1 is converted into digital data by an A / D converter, and
Is temporarily stored. When the inspection is performed, the web is transported in the printing press. Therefore, in synchronization with the transport of the web, the image input camera 1 captures an image of a printing surface, and the image input buffer 2 temporarily stores an image of a predetermined print area. . This synchronization is performed based on a synchronization signal. The synchronizing circuit 3 generates a synchronizing signal based on a web transfer signal output from, for example, a rotary encoder that detects rotation of a driving shaft of a printing press, and outputs the synchronizing signal to the image input camera 1 and the input buffer 2. .

The reference image memory 4 receives an image temporarily stored in the image input buffer 2 and stores an image of the entire predetermined printing area, that is, an image of a predetermined inspection area (an image of a printed matter for one cycle). This image stored in the reference image memory 4 is an image of a non-defective printed matter (reference image). When a non-defective product is being printed on the printing press, the circuit reference image update / dead zone image creation command circuit 7 stores the reference image memory 4 in the reference image memory 4 based on logical judgment automatically performed by the control panel of the printing press or manual input by an operator. An output for instructing the updating of the reference image is performed. The updating of the reference image is performed not only before the start of the inspection, but also during the inspection, for example, when the printing conditions are changed, for example, when the printing speed is changed. FIG. 2A shows an example of the reference image. Although the reference image is originally a grayscale image (in this example, the pixel value is 0 to 255), the grayscale image is shown by a line drawing in FIG.

The dead zone calculation circuit 5 calculates a dead zone image based on the reference image stored in the reference image memory 4 and outputs the calculated dead zone image to the dead zone image memory 6. When comparing the reference image and the inspection target image for each pixel, generally, the edge portion (the portion where the density changes rapidly) of the image is greatly affected by the positional deviation between the reference image and the inspection target image, and the positional deviation is within a normal range. Also, the absolute value of the difference becomes large. The dead zone image is data for preventing such misregistration within a normal range from being erroneously determined as a printing failure. That is, when evaluating the difference between the reference image and the inspection target image,
Rather than evaluating only by the absolute value of a simple difference, a value set in a dead zone image is considered. For example, when the absolute value of the difference exceeds a value set in the dead zone image, the exceeded value is regarded as a substantial difference between the reference image and the inspection target image.

The dead zone calculation circuit 5 calculates a dead zone image based on a command output from the circuit reference image update / dead zone image creation command circuit 7. Normally, a dead zone image is calculated each time the reference image is updated. Figure 2 shows an example of the dead zone image
It is shown in (B). The dead zone image is originally a grayscale image,
In FIG. 2B, as in FIG. 2A, a gray image is shown by a line drawing. In FIG. 2B, the dark portion indicates the size of the dead zone (the intensity of the dead zone).

The difference / absolute value conversion circuit 8 inputs the inspection target image output from the image input buffer 2, the reference image stored in the reference image memory 4, and the dead zone image stored in the dead zone image memory 6. Then, the absolute value of the difference between the reference image and the inspection target image is calculated for each pixel to obtain an absolute value image of the difference. As described above, the dead zone image is considered in the calculation. For example, when the absolute value of the difference between the inspection target image and the inspection target image for each pixel exceeds the value set in the dead zone image, the exceeded value is regarded as the absolute value of the difference. The value of each pixel of the value image is used. If the value does not exceed the value set in the dead zone image, there is no difference, and the pixel value is set to “0”.

FIG. 2 (C) shows an example of an image to be inspected, FIG. 2 (D) shows an example of a simple difference absolute value image not considering a dead zone image, and FIG. FIG. 3E shows an example of the absolute value image. Although these are originally grayscale images, FIGS. 2C, 2D and 3
In (E), a grayscale image is shown by a line drawing. The inspection target image shown in FIG. 2C includes a defective portion. FIG. 2 (D)
In the absolute value image of the simple difference shown in (1), an edge portion (not a defect) in the image is recognized. In the absolute value image of the substantial difference shown in FIG. 3E, the edge portion in the image is almost disappeared and disappears, but a difference between the inspection target image which is not a defective portion and the reference image also appears.

The binarization circuits 9a, 9b and 9c binarize the absolute value image of the difference output from the difference / absolute value conversion circuit 8. The threshold value for performing the binarization is set in the binarization circuits 9a, 9b, and 9c by the binarization threshold setting circuit 12. When the binarization circuits 9a, 9b, and 9c binarize the absolute value image of the substantial difference shown in FIG. 3 (E), FIG. 3 (F), FIG. 3 (G), and FIG. The binarized image shown in (H) is obtained. 3 (F), 3 (G), 3 (H)
3 are binarized images obtained by performing binarization with different threshold values, and FIG. 3F shows the smallest threshold value (18/255; threshold value / maximum value). 3 (G) is an intermediate threshold (36/255), and FIG. 3 (H) has the largest threshold (54/255).

The morphological operation circuits 11a, 11b,
11c is a morphology (morp) for the absolute value image of the difference.
hology) Perform the operation. The morphological operation is based on the target image and the components (structuring elem) that operate on the target image.
ent) is a basic form. Various transformations can be obtained by changing the components. The component setting circuit 10 sets the components in the morphological operation circuits 11a, 11b and 11c. Morphological operations are “dilation” “erosion” “opening” “closing”
". The morphological operations will be described later together, and here, the morphological operation circuits 11a, 11b, 1 in FIG.
The role of 1c will be described.

In order to make it easy to understand, the case where the morphological operation circuits 11a, 11b and 11c in FIG. 1 are omitted and the case where they are present will be described in comparison. When the morphology operation circuits 11a, 11b, and 11c are omitted, the determination circuit 13 determines whether or not there is a pixel exceeding a threshold (the presence or absence of a black pixel) in the binarized images output from the plurality of binarization circuits 9a, 9b, and 9c. Pass / fail judgment is made. Although there is a method of making a determination in consideration of the continuity and shape of black pixels, the determination is basically made based on the presence or absence of a black pixel. Therefore, when the three binarized images are examined, the following can be understood. In FIG. 3F, it can be seen that a defect is detected and a non-defective part is erroneously detected as a defect (noise is mixed). Further, in FIG. 3G, the degree of erroneously detecting a defect is small, but not all. Moreover, oversight of defects is recognized. Further, in FIG. 3H, there is no erroneous detection of a defect, but it is extremely difficult to overlook the defect. Therefore, when simple binarization is performed on the absolute value image of the difference as shown in this example, erroneous detection cannot be avoided when trying to detect a defect with low density, and erroneous detection is avoided. In this case, a defect having a low density is overlooked.

FIG. 4 is a diagram showing a general tendency of human recognition of defective density (difference from normal density) and defective area. In FIG. 4, the horizontal axis indicates the defective area and the vertical axis indicates the defective density. Generally, the larger the defect density and the larger the defective area, the more the defect is regarded as a heavy defect, and conversely, the smaller the defect density and the smaller the defective area, the lighter the defect. In FIG. 4, the farther away from the origin in the upper right direction, the heavier the defect, and the closer to the origin in the lower left direction, the lighter the defect. Therefore, as shown in FIG. 4, the area can be classified into a heavy defect area, a medium defect area, a light defect area, and a normal area.

The morphology operation circuits 11a, 11b,
11c is omitted, the aforementioned binarization circuit 9
Binarization by a, 9b, and 9c is equivalent to dividing the region in FIG. 4 into two by a straight line parallel to the horizontal axis (defective area) in FIG. Then, a defect included in an area above the straight line is detected, and a defect included in an area below the straight line is not detected. This corresponds to a conventional method of detecting a defect only by a level difference. The area above the straight line includes the above-described normal area, which indicates that erroneous detection of a defect is inevitable even though the area is normal. Also, the area below the straight line includes not only the normal area but also the area of heavy defect, the area of medium defect, and the area of light defect, showing that it is inevitable to overlook particularly low density defects. I have.

Changing the threshold for binarization corresponds to changing the vertical position of a straight line parallel to the horizontal axis (defective area) in FIG. 4, that is, changing the defect level. However, even if such an attempt is made to change the defect level in the example of FIG. 4, erroneous detection or oversight cannot be basically avoided. That is, when simple binarization is performed on the absolute value image of the difference, erroneous detection cannot be avoided when trying to detect a defect with low density, and a defect with low density cannot be avoided when trying to avoid erroneous detection. Will be overlooked.

Next, the case of the present invention in which the morphological operation circuits 11a, 11b and 11c in FIG. 1 exist will be described. Morphological operation circuits 11a and 11b
3 (F) and 3 (G) at (, 11c).
A morphological operation called opening is performed on the binarized image using two components having different areas. As a result, FIG. 5 (I) and FIG.
A morphology operation image shown in (J) is generated. FIG.
The area of the component shown in FIG. 5J is larger than the area of the component shown in FIG. As shown in FIG. 5 (I) and FIG. 5 (J), by performing this morphological operation, an isolated point and an image portion having a small area are removed. Since the area of the components in FIG. 5J is larger than that in FIG.
5 (J) has more image portions to be removed as compared with (I).

FIG. 6 shows an example of the components. FIG. 6A has an area composed of 1 × 1 pixels.
FIG. 6B has an area composed of 3 × 3 pixels.
(B) has an area composed of 5 × 9 pixels. Pixels shown in black are central pixels, and pixels shown in gray are neighboring pixels. As shown in FIG. 6, the components are also a kind of binary image, and each pixel of the components has a pixel value “1”. A morphological operation is performed on the image of FIG. 3 (E) using the components of FIG. 6 (B) to obtain the image of FIG. 5 (I), and the image of FIG. By performing a morphological operation using the constituent elements, an image shown in FIG. 5J is obtained.

In the morphological operation, the central pixel of the component is applied to the target pixel of the image, and the operation is performed within the pixel of the component (the central pixel and the neighboring pixels). The operation is performed on all the target pixels in the region where the morphological operation is performed by changing the target pixel of the image. FIG. 7 is an explanatory diagram of a method for performing this morphological operation. In FIG. 7, gray pixels represent portions where the value of the original image (binary image) is “1”, and white pixels represent values of the original image which are “0”.
Represents the part. In FIG. 7, the bold lines indicate constituent elements (here, a square of 3 × 3 pixels).

FIG. 8 is a block diagram showing an example of an arithmetic unit for performing an opening operation which is one of the morphological operations. The operation of the opening operation device will be described with reference to FIGS. An image to be subjected to an opening operation is temporarily stored in the image buffer 21. The component control unit 22 controls the execution of the calculation by applying the central pixel of the component to the sequentially selected target pixel. Under the control of the component control unit 22, the AND operation unit 23 controls the pixel value (“1”) of the component and the pixel value (“0”) of the target image for each pixel where the component and the target image overlap. Or "1"), and when both pixel values are "1", the pixel value is "1", and when either pixel value is "0", the pixel value is "0". An operation for replacing (density value) is executed (see FIG. 7). The image buffer 24 temporarily stores an image obtained by the calculation.

The component control unit 25 controls the execution of the calculation by applying the central pixel of the component to the target pixel of the image buffer 24 which is sequentially selected (see FIG. 7). Under the control of the component control unit 25, the OR operation unit 26
For each pixel in the portion where the component and the calculation target image overlap, the pixel value (“1”) of the component and the pixel value (“0” or “1”) of the calculation target image are compared, and the pixel values of both are compared. Is performed to replace the pixel value (density value) of the calculation target image with “0” when the pixel value is “0”, and to “1” when any of the pixel values is “1” (see FIG. 7). . The image buffer 27 temporarily stores an image obtained by the calculation. As a result, the opening operation is performed on the image in the image buffer 21, and the image after the opening operation is temporarily stored in the image buffer 27.

FIGS. 3F and 3C, which are the above-described binarized images.
When the above morphological operation is performed on (G), a binary image shown in FIGS. 5 (I) and 5 (J) is obtained.
The threshold for binarization is shown in FIG.
(G) is high. FIG. 5 (I) is obtained by performing a morphological operation on the components shown in FIG. 3 (F) using components having a relatively large area. A defect having a high density and a small area is detected, and a defect having a low density and a large area is not detected. On the other hand, FIG. 5J is obtained by performing a morphological operation on the components shown in FIG. 3G with a relatively small area, and a defect having a low density and a large area is detected.

As described above, the binarization circuits 9a, 9b, 9c
By combining the morphological operation circuits 11a, 11b, and 11c with each other, a defective area to be detected can be classified based on both the difference between the normal density value and the size of the defective area. In FIG. 4, three combinations A, B, and C indicate that a defective area to be detected can be set based on the area of a component in a morphological operation called opening and the size of a threshold in binarization. That is, in each of the combinations A, B, and C in FIG. 4, the area of the defect detected by dividing the area by the two boundaries is the area away from the origin in the upper right direction of the two boundaries in FIG. It is.

Therefore, the area of the logical sum of the defective areas detected by each of the combination A, the combination B, and the combination C is a detectable defective area. In FIG. 4, the detectable defective area substantially includes a light defect area, a medium defect area, and a heavy defect area, and does not include a normal area. If more combinations than the three combinations A, B, and C shown in FIG. 4 are used, the degree of coincidence between the detectable defective area and each defective area can be made more accurate.

In FIG. 4, the threshold for binarization is smaller in combination B than in combination A, and smaller in combination C than in combination B. In addition, the area of the component is larger in the combination B than in the combination A, and larger in the combination C than in the combination B. That is, the failure level at the intersection of the two boundaries is substantially equal to the threshold value for binarization, and the failure area at the intersection of the two boundaries is substantially equal to the area of the component. As shown in FIG. 4, the components corresponding to each of the predetermined thresholds are set so that the components having a large area correspond to the thresholds having a small value. The tendency with the detected defect can be matched.

FIG. 9 is a diagram showing the division of defective areas performed by the determination circuit 13 when three combinations are used. As shown in FIG. 9, the number of defective areas depends on the difference between the normal density value and the area size of the defective area. 0 to
No. It is divided into seven. However, no. The region of 0 is within the allowable range and is not determined to be defective. The detected defect is N
o. 1 to No. Which of the regions No. 7 belongs can be determined based on which of the combination A, the combination B, and the combination C belongs to which region. Further, when the weight of “1” is assigned to the failure of the combination A, “2” is assigned to the failure of the combination B, and the weight of “4” is assigned to the failure of the combination C, and the addition is performed, 1 to No. 7
Matches the category. Whether the defect is a heavy defect or a light defect can be determined based on the magnitude of the numerical value.
In this way, the determination circuit 13 determines the defect type value indicating the area to which the detected defect belongs, for example, No. 0-No.
7 is output.

The control output circuit 14 inputs the failure type value and outputs an operation amount corresponding thereto. For example, if the detected defect is No. 1, No. 2, No. If the defect is in the area of No. 4, it is determined that the defect is a minor defect and an alarm is output. In addition, the detected defect is No. 3, No. 6, No. If the defect is in the area of No. 7, it is determined that the defect is a medium defect or a heavy defect, and a control output for removing the defective printed matter in the discharge unit of the printing press is performed.

Next, the morphological operation will be described. The morphological operation in the present invention is a morphological operation on a subset of E ^N , which is a binary morphological operation. ^Let A and B be subsets of EN, let a be an element of set A, and let b be an element of set A. Usually, set A corresponds to the target image, and set B corresponds to the component.

As described above, the morphological operations are "dilation", "erosion", "opening" and "closing".
Is composed of four basic operations. First, binary dilation
And binary erosion are defined. (Definition 1) Binary dilation: dilation by sets A and B
Is defined by the following equation 1.

(Equation 1) (Definition 2) Binary erosion: erosion by sets A and B
Is defined by the following equation (2).

(Equation 2)

Next, binary opening and binary closing will be defined. (Definition 3) Binary opening: according to component B of set A
opening is defined by the following equation (3).

(Equation 3) (Definition 4) Binary closing: by component B of set A
closing is defined by the following equation (4).

(Equation 4)

[0038]

As described above, according to the present invention, there is provided an image inspection method and apparatus for judging pass / fail based on an inspection criterion which treats both the difference in density value and the size of an area in a defect. Further, since the morphological operation in the present invention is performed on a binarized image, the operation can be simplified as compared with the case of performing on a grayscale image. Further, according to the present invention in which the pass / fail judgment is made based on all the calculation results using a plurality of predetermined threshold values, the degree of coincidence between the inspection reference and the desired reference is high. Further, according to the present invention, the grayscale image is an image obtained by calculating at least the absolute value of the difference based on the reference image and the image to be inspected, and the morphological calculation process for the binarized image based thereon is simplified. In the pass / fail determination step, the failure type is determined based on all of the morphology calculation images corresponding to the respective thresholds and the failure type is determined based on both failure attributes of the difference between the normal density value and the area size of the failure, and the failure type value is obtained. According to the present invention, it is possible to determine whether a defect detected by a defect type value is a heavy defect or a light defect. Further, according to the present invention including the control process of outputting the operation amount corresponding to the defect type value, it is possible to automatically perform an appropriate operation such as an alarm or a print stop according to the defect type value by the control process. Further, according to the present invention, in which the morphological operation is an opening operation, it is possible to obtain an operation image in a form suitable for a simple operation. According to the present invention, a predetermined component corresponding to each of the threshold values is a component having a small area corresponding to a threshold value having a large value. Matches.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a configuration of an apparatus (image inspection apparatus) for implementing an image inspection method of the present invention.

FIG. 2 is a pictorial diagram showing a reference image (A), a dead zone image (B), an inspection target image (C), and a simple difference absolute value image (D).

FIG. 3 shows the absolute difference value (E) of the substantial difference, 2 at different thresholds
It is a pictorial diagram showing the binarized images (F), (G), and (H) obtained by performing the binarization.

FIG. 4 is a diagram showing a general tendency of human recognition of a defective density (difference from a normal density) and a defective area.

FIG. 5 is a diagram showing morphology operation images (I) and (J).

FIG. 6 is a diagram showing an example of a component.

FIG. 7 is an explanatory diagram of a method for performing a morphological operation.

FIG. 8 is a block diagram illustrating an example of an arithmetic device that performs an opening operation that is one of morphological operations.

FIG. 9 is a diagram illustrating a defect area division performed by a determination circuit when three combinations are used.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Image input camera 2 Image input buffer 3 Synchronization circuit 4 Reference image memory 5 Dead zone operation circuit 6 Dead zone image memory 7 Reference image update / dead zone image creation command circuit 8 Difference. Absolute value conversion circuit 9a, 9b, 9c Morphological operation circuit 10 Configuration function setting circuit 11a, 11b, 11c Binarization circuit 12 Binary threshold setting circuit 13 Judgment circuit 14 Control output circuit 21, 24, 27 Image buffer 22, 25 Component control unit 23 AND operation unit 26 OR operation unit

Claims (8)

[Claims] An image inspection method for inspecting a reference image by comparing a reference image with an image to be inspected, wherein a binarization operation is performed on a grayscale image using a predetermined threshold to obtain a binary image corresponding to the threshold. A binarization operation step, a morphology operation step of performing a morphology operation on the binarized image corresponding to the threshold value using a predetermined component corresponding to the threshold value to obtain a morphology operation image corresponding to the threshold value, A quality determination step of performing a quality determination based on the morphology operation image. 2. The binarizing operation step is a step of performing a binarizing operation on a grayscale image using a plurality of predetermined threshold values to obtain a binarized image corresponding to each of the threshold values. The morphological operation step is a step of performing a morphological operation on a binarized image corresponding to each of the threshold values using a predetermined component corresponding to each of the threshold values to obtain a morphological operation image corresponding to each of the threshold values 2. The image inspection method according to claim 1, wherein the pass / fail determination step is a pass / fail determination step of performing pass / fail determination based on all of the morphology operation images corresponding to each of the threshold values. 3. 3. The image inspection method according to claim 1, wherein the grayscale image is an image obtained by calculating at least an absolute value of a difference based on the reference image and the image to be inspected. 4. The method of claim 1, wherein the defect type is determined based on all the morphological operation images corresponding to each of the threshold values, from a defect attribute of a difference between a normal density value of the defect and an area size. The image inspection method according to claim 1, wherein a defect type value is obtained. 5. The image inspection method according to claim 1, further comprising a control step of outputting an operation amount corresponding to the defect type value. 6. The image inspection method according to claim 1, wherein the morphological operation is an opening operation. 7. An image inspection apparatus according to claim 1, wherein said predetermined component corresponding to each of said thresholds is a component having a small area corresponding to a threshold having a large value. Method. 8. An image inspection apparatus for inspecting a reference image by comparing a reference image with an image to be inspected, comprising: performing a binarization operation on a grayscale image using a plurality of predetermined thresholds; Means for obtaining a binarized image, and performing a morphological operation on the binarized image corresponding to each of the thresholds using predetermined constituent elements corresponding to each of the thresholds, and corresponding to each of the thresholds An image inspection apparatus comprising: a morphology operation unit that obtains an operation image; and a pass / fail determination unit that performs pass / fail judgment based on all of the operation images corresponding to each of the thresholds.