JPH07200770A - Tablet inspection system - Google Patents

Abstract

PURPOSE:To provide a tablet inspection system which is capable of automatically and exactly detecting the number of tablet enclosed within an envelope CONSTITUTION:The envelope 15 is carried to a prescribed inspection location so as to detect the number of plural tablets 9 enclosed within a transparent envelope 15 and the envelope is photographed by a CCD camera 2 at the inspection location. The image obtained from the CCD camera 2 is delivered to an image recognition processor 3 and an image recognition processing for counting the number of tablet is executed. In the image recognition processing, the image of plural tablets within the envelope is divided into each piece and the number of the connection part contained in the image obtained by this is counted by detecting the contact part of two images of tablets in contact with each other and cutting off these two images of tablets by the contact part.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a tablet inspection system for detecting the number of tablets enclosed in a medicine bag.

[0002]

2. Description of the Related Art Conventionally, a tablet packing apparatus (1) as shown in FIG. 1 has been developed in order to automate tablet packing operations in hospitals and pharmacies. The tablet packaging device (1) includes a tablet supply unit (11) in which a plurality of types of tablets are stored according to type,
Various tablets (9) discharged from the tablet supply unit (11)
Automatically into the transparent packing paper (14) via the belt conveyors (12) (13), and the packing mechanism (17) seals the packing paper (14) for each person. Is. In addition, a printing mechanism (not shown) for automatically printing the patient name, usage, etc. on the surface of the packaging paper (14) is provided in the transport path of the packaging paper (14). By separating the packaging paper (14) thus obtained at the sealing portion for each person, the medicine bag (15) for each patient shown in FIG. 18 is obtained. The medicine bag (15) is provided with a printing section (16) for patient name, usage, etc.
In addition, since a necessary number of tablets (9) of a plurality of types to be taken at once by the patient are enclosed therein, it is convenient and there is no risk of mistakes in taking.

[0003]

However, in the conventional packaging work using the above-mentioned tablet packaging apparatus, the inspector visually confirms that a plurality of tablets to be packaged are accurately enclosed in one medicine bag. However, there is a problem that the efficiency of the whole packaging work is lowered by this complicated inspection work.

By the way, widely in the field of image recognition,
Various methods have been proposed for extracting the contour shape and the characteristic part of each object based on the images of a plurality of target objects (for example, Japanese Patent Publication 1-30183 [G06F15-70], Japanese Patent Publication 2-4948 [G06F15-7].
0], Japanese Patent Publication No. 3-57501 [G06F15-70], etc.). However, in the inspection of the tablets (9) in the medicine bag (15) as shown in FIG. 18, the tablets are often overlapped with each other, and the overlapping states are also different. In such a case, in the detection of the number of tablets using the conventional image recognition method, there is a possibility that two tablets overlapping each other may be counted as one tablet,
It is difficult to detect the correct number. Therefore, an object of the present invention is to provide a tablet inspection system capable of automatically and accurately detecting the number of tablets enclosed in a medicine bag.

[0005]

A tablet inspection system according to the present invention comprises a conveying means for conveying a medicine bag to a predetermined inspection position,
The image pickup means for photographing the surface of the medicine bag at the inspection position and the image recognition processing means for recognizing the image obtained from the image pickup means and counting the number of tablets in the medicine bag. Here, the image recognition processing means detects the contact portions of the images of the two tablets that are in contact with each other, and separates the images of the two tablets at the contact portions to individually obtain the images of the plurality of tablets in the medicine bag. It divides and counts the number of connected components contained in the image obtained by this.

In a specific configuration, an illuminating means for illuminating the medicine bag at the inspection position from the back side is arranged. The image recognition processing means includes means for binarizing the grayscale image from the imaging means and means for performing noise removal processing including contraction and expansion on the binarized image. Based on the configuration, it is possible to detect the contact portion between the images of the two tablets that are in contact with each other. Further, as another configuration of the image recognition processing means, means for performing contrast conversion on the grayscale image from the image pickup means to enhance the contrast, and means for detecting an edge of the image based on the image in which the contrast is enhanced, And a means for thinning out the detected edges to extract only effective thin lines, and by separating the images of two tablets in contact with each other at the position of the effective thin lines, images of a plurality of tablets in the medicine bag Is divided into
A configuration that counts the number of connected components included in the image obtained in this way is possible.

[0007]

In the above tablet inspection system, the medicine bag is conveyed to a predetermined inspection position, and the medicine bag is CCD at the inspection position.
An image is taken from the surface of the medicine bag by an image pickup means such as a camera. At this time, since the medicine bag is made of a light-transmissive material, the inside tablet is photographed. At this time, if an illuminating means for illuminating the medicine bag from the back side is arranged, the image of the tablet becomes clearer. An image recognition process having the following characteristics is performed on the images of the plurality of tablets obtained by this,
The number of tablets is counted. That is, when two tablets are in contact with each other at one point or overlap each other, the contact portion of the image of these tablets may be, for example, as shown in FIG.
Since a contact having an acute angle θ as shown in (a) is generated, it is possible to recognize that the images of the two tablets are in contact by detecting the presence of such a contact. So 2
By separating the images of one tablet at the contact portion, the images of a plurality of tablets in the medicine bag can be individually separated. Therefore, the number of tablets can be detected by counting the number of connected components contained in the image.

In the image recognition processing means, if the grayscale image from the image pickup means is binarized and the binarized image is subjected to noise removal processing consisting of contraction and expansion, the surface of the medicine bag (15) as shown in FIG. The characters of the printing section (16) and other noises are removed from the image. Therefore, the subsequent tablet counting is accurately performed.

Further, in the configuration for performing the image recognition processing on the grayscale image from the image pickup means, first, the contrast conversion is performed on the grayscale image, so that the contours of the plurality of tablets and the contact portions with each other are different from other image portions. It will be emphasized. Here, if you arrange the lighting means that illuminates the medicine bag from the back,
The light leaks from the contact portion of the plurality of tablets, and the boundary line between the two tablets rises more clearly. Next, the edges of the image are detected based on the contrast-enhanced image to find the outlines of the plurality of tablets and the boundaries between them. If thinning is applied to these edges, beards and noises are removed, and only effective thin lines can be extracted. Therefore, by separating the images of the two tablets that contact each other at the position of the effective thin line, the images of the plurality of tablets in the medicine bag can be individually separated.

[0010]

According to the tablet inspection system of the present invention, since a plurality of tablets that are in contact with each other are divided by image processing, the number of tablets enclosed in the medicine bag is automatically and accurately counted. It is possible to eliminate the need for the conventional visual inspection.

[0011]

1 shows the structure of a tablet inspection system according to the present invention, in which a tablet packaging device (1) has the same structure as a conventional one. The wrapping paper (1
4) is further transported in the horizontal direction, and at the position above the transport path,
A camera (2) for photographing the surface of the medicine bag (15) is arranged. Further, a planar illuminator (21) for illuminating the back surface of the medicine bag (15) is arranged at a position below the transport path. The camera (2) is a monochrome CCD camera having 410,000 pixels.

The image signal obtained from the CCD camera (2) is sent to the image recognition processing device (3) composed of a microcomputer and the like, and the tablet (9) enclosed in the medicine bag (15).
It is used for counting the number of. Also, an image recognition processing device
A monitor (4) is connected to (3) to display the process of image processing.

FIG. 2 shows a series of image recognition processing procedures executed by the image recognition processing device (3). First, in step S1, the original image from the CCD camera (2) is captured.
FIG. 8 is an example of an original image captured by the image recognition processing device (3). In this example, nine tablets are enclosed in the medicine bag, and eight of them are in contact with each other. Therefore,
In the conventional method, it is difficult to count the number. In the original image in FIG. 8, some noise is mixed around the image portion of the tablet.

Next, in step S2 of FIG. 2, the original image is binarized at a predetermined threshold level. As a result, each pixel forming the original image is represented by "0" or "1". FIG. 9 shows a binary image in which "0" is white and "1" is black. In the binary image, the noise included in the original image in FIG. 8 remains around the image portion of the tablet.

Next, in step S3 of FIG. 2, noise removal processing consisting of contraction and expansion is performed on the binary image (for example, "Basic technique of image processing", published by Technical Review Co., 53-56).
(See page). The contraction / expansion process is a process of deleting or multiplying all the boundary pixels of the connected components in the image,
In this embodiment, after the contraction process is performed twice, the expansion process is performed twice. As a result, the printing on the surface of the medicine bag (15) is deleted from the image and the isolated points in the image are removed.
In the above example, the noise included in the original image of FIG. 9 is removed.

Subsequently, in step S4 of FIG. 2, labeling processing is performed on the binary image that has been subjected to the noise removal processing (for example, "basic technique of image processing", published by Technical Review Co.
(See page). The labeling process is a process in which, when adjacent pixels are present in the image data, the image group is regarded as one group and a label is assigned, and adjacent pixels are present in one of upper, lower, left and right. Sometimes 1
There are 4-connected labeling that is regarded as a group, and 8-connected labeling that is regarded as 1 group when adjacent pixels are present in any of up, down, left, right, and diagonal directions. In tablet inspection, 4-connection labeling, which is excellent in terms of figure separation, is effective.

After that, in step S5 of FIG. 2, a chain coding process is performed on the connected components in the image to which the label is assigned (see, for example, "Basic Techniques of Image Processing", Technical Review Company, pages 76-78). Do. The chain coding process is a process of tracing the contour or line segment of the connected component and giving a direction index (chain code) of “0” to “7” in that direction. With respect to each of the images assigned to, the contour (outer contour) formed by the outer shape of the tablet and the contour (inner contour) of a gap formed between a plurality of tablets in contact with each other are tracked. And
Record the chain code for each label.

Next, for each connected component to which a label is assigned, the contact point of the image caused by the overlapping of the tablets is detected based on the recorded chain code of the contour line. FIG. 3 shows a contact detection procedure. First, contact candidate points are selected in step S61. As for the contact point candidate, the chain code is 0 → 6, as shown in FIG.
1 → 7, 2 → 0, 3 → 1, 4 → 2, 5 → 3, 6 → 4, 7
In the case of a combination of chain codes that changes abruptly like → 5 and changes from 0 → 6, 0 → 7 → 6 or 7 →
The change point is also set when one or two chain codes are present between the chain codes such as 7 → 6. Therefore, there are a total of 24 combinations of chain codes that should be changed.

Next, in step S62 of FIG. 3, at the contact point candidate, the angle of change in direction around the candidate point is calculated based on the contour lines of the front and rear 5 pixels. For example, in FIG.
In the case of the contact point candidate point generated by the contact of two tablets as shown in (a), the angle θ of the direction change is an acute angle, but FIG.
As described above, the angle θ is an obtuse angle if it is a contact point candidate point caused by noise generated on the contour line of one tablet. Therefore, in step S63, the angle θ is set to a predetermined threshold value (for example, 1
20 degrees), if it is smaller than the threshold value, it is determined to be a true contact point in step S64, and if it is equal to or larger than the threshold value, it is determined to be noise on the contour line in step S65.

FIGS. 6 (a) and 6 (b) are enlarged views in the vicinity of the direction change points in FIGS. 5 (a) and 5 (b), and the circles represent the pixels forming the figure. Here, a chain code that changes from 5 to 3 is detected as a contact point candidate point, and the opening angle of the fifth pixel before and after the candidate point as the center is detected as the direction change angle. In this case, the contact point candidate of FIG. 6A is determined to be a true contact point, and the contact point candidate of FIG. 6B is determined to be noise on the contour line and ignored.

Regarding the true contact point due to the overlapping of tablets,
As shown in FIG. 7, a pair of contacts (a, b), which are paired with each other,
(c, d) exists. Therefore, in step S7 of FIG. 2, such contact pairs are created for all labeled connected components. To create a contact pair, for example, a method of finding two contacts that satisfy the following conditions 1 to 3 is adopted. (Condition 1) The angle θ ₁ between two contact points (for example, the contact points a and b in FIG. 7, the direction change vectors A and B for the contact points a and b in FIG. 7) between the two contact points to be judged as a pair θ ₁ ( 0 ° ≤ θ ₁ ≤ 180 °) is the predetermined threshold
(For example, 150 °) or more. (Condition 2) The contact point vector of interest and a straight line from that contact point to another contact point (for example, for contact points a and b in FIG. 7,
An angle θ ₂ formed by a straight line E from the contact point a to the contact point b is equal to or larger than a predetermined threshold value (for example, 150 °). (Condition 3) Among the contact pairs that satisfy the above conditions 1 and 2,
The distance between the two points is equal to or less than a predetermined threshold value (for example, in the case of a circular tablet, 0.3 to 1.0 times the diameter), and is the minimum. In the case of FIG. 7, the contacts a and b or c and d are determined to be a pair, but the contacts a and c or a and d are not determined to be a pair.

After making contact pairs for all the labels, in step S8 of FIG. 2, the two contacts judged as a pair are connected by a straight line to form a boundary line, which is connected to each other to form one label. The attached graphic element is separated into the connected components of the two tablet graphics at the boundary line. This processing is performed by replacing the pixel "1" on the boundary with "0". FIG. 10 shows a state in which, in the example of the binary image of FIG. 9, contact pairs are created at the contact portions of two tablets that are in contact with each other, and these contact pairs are interconnected to create a boundary line. FIG. 11 shows a state in which the binary image is divided into images of individual tablets at the boundary line.

Thereafter, in step S9 of FIG. 2, relabeling is performed on the divided binary image of the image of each tablet. Then, the number of tablets is recognized by counting the number of labels in step S10. In the example of FIG. 11, the number of tablets is counted as 9. Then, it is determined whether the recognized quantity of tablets matches the expected quantity, and if they do not match, the medicine bag is automatically discharged as defective, or the medicine bag is marked as defective.

According to the image recognition processing method described above, an accurate contact pair is created especially when the overlapping area of tablets is small, and as a result, the number of tablets can be recognized with extremely high accuracy.

FIG. 12 shows a processing method for efficiently separating the images of two tablets that overlap each other in the contraction processing of step S3 of FIG. That is, in addition to the deletion of the boundary pixel described above, when the binary state of 3 × 3 pixels shown in FIG. 12 has the illustrated pattern, the pixel at the position marked with a double circle is also set to “0” at the same time. As another processing method, when the pattern of FIG. 12 is displayed, the pixel at the position marked with a double circle is set to “0” only when the pixel at the position marked with a square is “1”. A method is also possible. This method
The purpose is to shrink the double circle pixels after determining whether or not the pattern of FIG. 12 is a pattern caused by truly overlapping tablets. By adding this determination, it is possible to prevent erroneous determination that the edge generated in the capsule lid portion of the capsule is the overlapping of the tablets.

Further, in the contraction process, it is possible to add a process for removing isolated points. For example, if all 8 pixels in the vicinity of the pixel of interest are "1", the pixel of interest is set to "0". In addition to the above method, as shown in FIGS.
A method can be adopted in which when two pixels or eight neighboring pixels are all "1", a pixel surrounded by these pixels is "1", and when all are "0". The latter method basically aims to remove the isolated points of two or less connected pixels, but the isolated points as shown in FIG. 13C are judged to be meaningful pixels and are not removed.

The above-described image recognition processing method shown in FIG. 2 is particularly effective when the overlapping area of tablets is small.
4 shows the procedure of the image recognition processing method which is effective when the overlapping area of the tablets is large. Therefore, when the contact pair detection fails in the method of FIG. 2, if the images are separated by the method of FIG. 14, it is possible to surely recognize the quantity.

First, in step S11, a CCD camera
A grayscale image of 512 pixels × 512 lines is captured from (2). Also in this case, as shown in FIG. 1, the illumination by the illuminator (21) is performed from below the medicine bag (15). This is because when the illumination from above the medicine bag (15) is adopted, the image information of the tablet is lost due to the reflection on the surface of the medicine bag, or in the case of tablets or capsules with knurls on the surface, knurls or capsules This is because there is a drawback that the step portion of is detected as an edge.

Next, contrast conversion is performed in step S12 without binarizing the grayscale image. The density conversion formula used in this case is well known (see, for example, the magazine "Electronic Life", February 1989, pages 145-153). This facilitates extraction of the overlapping portion of the tablet.

Subsequently, edge detection filter processing is performed in step S13 of FIG. In the examples, as a result of examining various filter processing methods, as the best method, processing using a sobel filter of the first derivative (see, for example, magazine "Electronic Life", December 1988, pages 148 to 155).
It was adopted. As a result, the outline of the tablet and the overlapping area between the tablets are emphasized.

Further, in step S14, the contour-enhanced image is binarized by a predetermined threshold level to extract only the contour (edge).

Then, in step S15, thinning is performed. The thinning is a process of extracting a core line passing through the center of the widthwise edge image in the width direction.
Adopt ch algorithm (for example, Tamura “Various considerations on thinning method” IEICE Technical Report, 1975 PR
See L75-9966). As a result, the boundary line of the overlapping portion with the contour lines of the plurality of tablets is obtained as a line image.

Then, in step S16, the beard and noise are removed from the line image. The conditions for removal are as follows. (1) 1-pixel isolated point (2) Lines that do not include branch points or intersections and have 20 or fewer connected pixels (3) The number of connected pixels between an end point and a branch point or intersection is 1
Lines less than 0

Further, in step S17, an effective thin line necessary for the difference extraction step (S18) with the next binary image is selected. This is a procedure for removing long whiskers and loop-like pseudo-rays that remain without being removed even in step S16 described above, and FIG. 15 shows a specific procedure thereof. First, in step S21, an inner contour and an outer contour are extracted based on the line image. Then step S2
At 2, inflate these contour lines inward and outward,
Create a search area. For example, in the case of a line image in which two circular tablets overlap as shown in FIG.
The heavy lines M and N are created, and the area between them is used as the search area. The line E outside the search area is excluded from the target and is removed.

Subsequently, in step S23 of FIG. 15, a branch point or an intersection existing on the fine line in the search area is detected. In the example of FIG. 16, a branch point exists at a position surrounded by a circle in the search area. After that, in step S24 of FIG. 15, the line protruding from the search area at a distance of 20 pixels or more from the branch point or the intersection is determined to be an effective thin line. In the example of FIG. 16, A and D in the figure are effective thin lines, and the other lines B and C are removed as ineffective thin lines. The line D is a thin line that should be invalidated originally, but it does not pose a problem in recognizing the number of tablets as described later.

After selecting the effective thin line, step S in FIG.
At 18, the difference between the separately created binary original image and the image of the effective thin line is extracted, thereby separating the images of the two tablets that are connected to each other. FIG. 17 shows the result of the difference extraction in the example of FIG. As shown, the images of the two tablets are separated by an effective thin line. Although the effect of the line D remaining without being removed appears in the binary image of FIG. 17, this does not cause any problem in recognizing the quantity.

After that, labeling is performed in step S19 of FIG. 14, and the number of labels is counted in step S20 to recognize the number of tablets. Then, it is determined whether or not the recognized quantity of tablets matches the expected quantity, and if they do not match, the medicine bag is automatically discharged as defective, or the medicine bag is marked as defective.

According to the image recognition processing method described above, even when two tablets are largely overlapped, the illumination light leaks from the vicinity of the overlapped portion and the boundary line is surely detected by the edge detection. Separation is ensured and accurate quantity recognition is realized. Also, by combining the method of FIG. 2 and the method of FIG. 14, it becomes possible to inspect various medicine bags in the overlapping state of tablets.

In the inspection system of FIG. 1, the CCD
Before the tablet photographing process by the camera (2), if a vibration device that gives vibration to the packaging paper (14) in the horizontal plane is provided, the tablets (9) in the packaging paper (14) will be dispersed. Therefore, the overlap can be reduced. In this case, accurate quantity recognition is possible only by the image recognition processing method of FIG.

In addition to the image recognition processing method of FIG. 2 or FIG. 14, the peripheral length and area of the binary image of the divided tablet are calculated and compared with the data registered in advance for each type of tablet. As a result, the accuracy of quantity recognition can be further improved, or the type of tablet can be discriminated.

The above description of the embodiments is for explaining the present invention, and should not be construed as limiting the invention described in the claims or limiting the scope. The configuration of each part of the present invention is not limited to the above-mentioned embodiment, and it goes without saying that various modifications can be made within the technical scope described in the claims. For example, in the above embodiment, the image recognition processing is realized by software, but it is also possible to replace it with hardware for high-speed processing.

[Brief description of drawings]

FIG. 1 is a perspective view showing a configuration of a tablet inspection system according to the present invention.

FIG. 2 is a flowchart showing a procedure of an image recognition processing method.

FIG. 3 is a flowchart showing a procedure of contact detection.

FIG. 4 is a diagram illustrating a method of selecting contact point candidate points.

FIG. 5 is a diagram illustrating a method of discriminating between a true contact point and noise.

FIG. 6 is a diagram illustrating a method of calculating a direction change angle around a contact point.

FIG. 7 is a diagram illustrating a method of discriminating contact pairs.

FIG. 8 is a diagram showing original images of a plurality of tablets.

9 is a diagram showing a binary image in the example of FIG.

FIG. 10 is a diagram showing a state in which a contact pair is created in the above example.

FIG. 11 is a diagram showing a state in which images of a plurality of tablets are divided in the above example.

FIG. 12 is a diagram illustrating a method of improving the efficiency of figure separation in the contraction processing.

FIG. 13 is a diagram illustrating a method of improving the efficiency of isolated point removal in the contraction processing.

FIG. 14 is a flowchart showing the procedure of another image recognition processing method.

FIG. 15 is a flowchart showing a procedure for selecting an effective thin line.

FIG. 16 is a diagram illustrating a method of removing a beard or a pseudo line generated at a branch point or an intersection.

FIG. 17 is a diagram showing a binary image obtained by extracting a difference from an effective thin line.

FIG. 18 is a perspective view of a medicine bag in which a plurality of tablets are enclosed.

[Explanation of symbols]

 (1) Tablet packing device (11) Tablet supply unit (14) Packaging paper (15) Medicine bag (2) CCD camera (21) Lighting equipment (3) Image recognition processing device (4) Monitor (9) Tablet

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[Procedure amendment]

[Submission date] March 4, 1994

[Procedure Amendment 1]

[Document name to be amended] Statement

[Name of item to be corrected] Claim 5

[Correction method] Change

[Correction content]

[Procedure Amendment 2]

[Document name to be amended] Statement

[Correction target item name] 0006

[Correction method] Change

[Correction content]

In a specific configuration, an illuminating means for illuminating the medicine bag at the inspection position from the back side is arranged. The image recognition processing means includes means for binarizing the grayscale image from the imaging means and means for performing noise removal processing including contraction and expansion on the binarized image. Based on the configuration, it is possible to detect the contact portion between the images of the two tablets that are in contact with each other. Further, as another configuration of the image recognition processing means, means for performing contrast conversion on the grayscale image from the image pickup means to enhance the contrast, and means for detecting an edge of the image based on the image in which the contrast is enhanced, After thinning the detected edges
And a means for extracting only the effective thin line, and by separating the images of the two tablets that are in contact with each other at the position of the effective thin line, the images of the plurality of tablets in the medicine bag are individually divided, thereby obtaining It is possible to count the number of connected components included in the acquired image.

[Procedure 3]

[Document name to be amended] Statement

[Correction target item name] 0009

[Correction method] Change

[Correction content]

Further, in the configuration for performing the image recognition processing on the grayscale image from the image pickup means, first, the contrast conversion is performed on the grayscale image, so that the contours of the plurality of tablets and the contact portions with each other are different from other image portions. It will be emphasized. Here, if you arrange the lighting means that illuminates the medicine bag from the back,
The light leaks from the contact portion of the plurality of tablets, and the boundary line between the two tablets rises more clearly. Next, the edges of the image are detected based on the contrast-enhanced image to find the outlines of the plurality of tablets and the boundaries between them. After facilities thinning to these edges, beard Ya
Remove noise and extract only valid thin lines. Use this
By separating the two tablets image in contact with each other have at the position of the effective fine line can be divided into individual images of a plurality of tablets in the medicine bag.

[Procedure amendment 4]

[Document name to be amended] Statement

[Correction target item name] 0013

[Correction method] Change

[Correction content]

FIG. 2 shows a series of image recognition processing procedures executed by the image recognition processing device (3). First, in step S1, the original image from the CCD camera (2) is captured.
FIG. 8 is an example of an original image captured by the image recognition processing device (3). In this example, nine tablets are enclosed in the medicine bag, and eight of them are in contact with each other. Therefore,
In the conventional method, it is difficult to count the number. It should be noted that in the original image of FIG. 8, some noise due to the printing of the packaging paper is mixed around the image portion of the tablet.

[Procedure Amendment 5]

[Document name to be amended] Statement

[Correction target item name] 0034

[Correction method] Change

[Correction content]

Further, in step S17, an effective thin line necessary for the difference extraction step (S18) with the next binary image is selected. This is a procedure for removing long whiskers and loop-like pseudo-rays that remain without being removed even in step S16 described above, and FIG. 15 shows a specific procedure thereof. First, in step S21, the original image is binarized.
The inner contour and the outer contour are extracted from the image . Next, in step S22, these contour lines are expanded inward and outward to create a search area. For example, in the case of a line image in which two circular tablets are overlapped as shown in FIG. 16, double lines M and N are created by enclosing a thin line L, and a space between them is set as a search area. The line E outside the search area is excluded from the target and is removed.

[Procedure correction 6]

[Document name to be corrected] Drawing

[Correction target item name] Fig. 16

[Correction method] Change

[Correction content]

FIG. 16

Claims (5)

[Claims] 1. A tablet inspection system for detecting the number of a plurality of tablets enclosed in a light-transmissive medicine bag, comprising: an imaging means for photographing the medicine bag at an inspection position; and an image obtained from the imaging means. And an image recognition processing means for counting the number of tablets in the medicine bag. The image recognition processing means detects a contact portion between the images of two tablets which are in contact with each other, and the images of these two tablets are detected in the contact portion. By separating by separating, the images of multiple tablets in the medicine bag are individually divided, and the number of connected components contained in the image obtained by this is counted,
A tablet inspection system characterized by calculating the number of tablets. 2. The tablet inspection system according to claim 1, further comprising illumination means for illuminating the medicine bag at the inspection position from the back surface. 3. The image recognition processing means comprises means for binarizing the grayscale image from the image pickup means, and means for performing noise removal processing consisting of contraction and expansion on the binarized image. The tablet inspection system according to claim 1, wherein a contact portion between images of two tablets that contact each other is detected based on the removed image. 4. The image recognition processing means binarizes the grayscale image from the imaging means, and the binarized image is subjected to labeling processing and chain coding processing so that two tablets come into contact with each other or A means for detecting a pair of contact points located at both ends of the intersecting area, and recognizing a position of a connecting line of the pair of contact points as a contact portion of two tablet images.
Tablet inspection system described in. 5. The image recognition processing means performs a contrast conversion on the grayscale image from the image pickup means to enhance the contrast, a means to detect an edge of the image based on the contrast enhanced image, and a detection means. The tablet inspection system according to claim 1, further comprising means for thinning the formed edges to extract only effective thin lines, and recognizing a position of the effective thin lines as a contact portion between images of two tablets.