JPH07234941A - Article identification system - Google Patents

Abstract

PURPOSE:To automatically judge whether or not a selected tablet coincides with a designated tablet and thereby, to always output a correct package in which the designated tablet is housed from an automatic tablet packer when a tablet of designated brand is automatically selected and packed by the automatic tablet packet. CONSTITUTION:The tablet is color-photographed by a camera 1, and inverse (gamma) correction is applied to the image of the tablet, and it is converted into a gray image by using a G component out of R, G and B components, and further, a tablet area in the image is corrected in fixed position and direction, and a feature vector is sampled from the image. As the feature vector, the variable density vector of the image is used. Distance between the feature vector and a reference vector of designated brand stored in a dictionary file 110 is found, and when it is within an allowable value r.sigma decided by scatter (sigma) of designated brand, it is judged that the tablet is the one of designated brand.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an article identification system for identifying an article using image processing / identification technology, and particularly in an automatic packaging machine for pharmaceutical tablets and capsules (hereinafter simply referred to as tablets). The present invention relates to an article identification system suitable for tablet identification for inspecting whether a selected tablet is correct for an input name pattern.

[0002]

2. Description of the Related Art At present, in many general hospitals, an automatic tablet packing machine for automatically packing a plurality of tablets of a designated brand in a packaging paper is used. The automatic tablet packing machine receives a plurality of tablet brand information inputted by the operator from the terminal, carries the corresponding tablets from the doublet case in the apparatus, and performs a process of packing the tablets in the same packaging paper. For general automatic tablet packaging machines, see, for example, “Toshikazu Seino, Takao Orii, et al .: Use of a computer for dispensing operations-Trial of an audit system for automatic dispensing packaging machines, Proc. Of the 8th Medical Information Union Conference. , 565-568, 1988 ".

By introducing a drug system using such an automatic tablet packing machine into a hospital, the tablet packing which has been performed by a pharmacist until now is automated, and the waiting time is greatly shortened. It becomes possible and the quality of medical treatment can be improved.

[0004]

In the above automatic tablet packing machine, a mechanical operation is included in the process of taking out the specified tablet from the tablet case and the process of carrying the taken-out tablet to the packaging position. Therefore, there is a possibility that the tablets may be taken out incorrectly or missing. In addition, although the human beings supply the tablets to the tablet case, there is a possibility that the brand to be supplied at that time may be wrong. However, even if there is such a mechanical or human error, it is necessary for the package to be finally provided to the patient to definitely contain the tablet of the designated name. Therefore, conventionally, when operating an automatic tablet packaging machine, a pharmacist has visually performed an identification test to determine whether the packaged tablet is a designated brand.

Therefore, the labor of the identification test is troublesome,
There was a problem that the inspection took time. Further, since the inspection is performed after the packaging, if the wrong packaging of tablets is found, the packaging has to be broken and the packaging of correct tablets has to be made again.

Therefore, it is an object of the present invention to automatically determine whether or not a selected tablet matches a designated brand in the operation of the automatic tablet packing machine, and thereby, the automatic tablet packing machine always specifies the selected tablet. The goal is to get the correct packaging out of the street tablets.

The more general purpose of the present invention is to
In applications such as selecting items in a specified category from a multi-category item group, an identification test is performed to confirm whether the selected item belongs to the specified category. It is to be able to do it automatically and accurately using technology.

[0008]

The article identification system of the present invention comprises an image input means for inputting an image of an article, a category input means for inputting a category of an article, and a gray value vector of the input image as a feature vector. The feature extraction means for extracting as, the distance between the extracted feature vector and the reference vector prepared in advance is obtained, and whether the distance is within a predetermined allowable value or not It is characterized by comprising an identification means for determining whether or not it is related.

[0009]

In the present invention, an article (for example,
An image of the tablet) and the category of the article (eg, tablet brand) are input to the system. Then,
The feature vector of the article is extracted from the image by image processing. As the feature vector, a gray value vector having gray values in the image as elements is used. The distance between the feature vector and the reference vector prepared in advance is obtained, and it is determined whether or not the input category corresponds to the article depending on whether or not the distance is within a predetermined allowable value.

By carrying out the above-mentioned processing, it becomes possible to automatically carry out the identification inspection of the article without the visual inspection of a person.

In this system, when a learning sample related to a certain category and its category are input to the system, the feature vector of the learning sample is received from the feature extracting means and a reference vector of the input category is calculated. It is possible to further include a learning unit and a dispersion degree calculation unit that calculates a dispersion degree of a distribution of the distance between the calculated reference vector and the feature vector of the learning sample.

In this case, the present system can generate the reference vector used for identification and the degree of dispersion in its own system, so that it is easy to deal with the case where the category group to be identified changes. Is.

Further, the present system can be added with the following various means, whereby the identification accuracy can be improved or the processing amount can be eliminated.

That is, it is possible to add an inverse γ correction means for removing the γ correction inherent in the input image. As a result, the influence of the γ correction applied to the image at the time of shooting can be removed, and the original light and shade of the tablet can be more faithfully expressed in the image.

If the input image is a color image,
RG that generates a gray image from the G component of the color image
B-gray conversion means can be added. As a result, a guilleu image with a wide dynamic range corresponding to a G component with a wide dynamic range in the color image can be obtained, which is advantageous for the subsequent processing, and moreover, the amount of information compressed from RGB 3 components to 1 gray component Therefore, the amount of processing thereafter can be reduced.

It is also possible to add a position / orientation correction means for correcting the article area in the input image to a fixed position and orientation. As a result, the position / orientation of the article in the image can be made constant at all times, and strict adjustment of the position / orientation of the tablet with respect to the camera is not necessary at the time of image capturing.

Further, the feature extracting procedure by the feature extracting means can be performed as follows. That is, first, a rectangular area including the article area is cut out from the input image, and a gray value vector having the gray value sampled from this rectangular area as an element is extracted as a feature vector of the article. This allows
Information other than the article such as the background can be deleted from the image, and significant information that is closely related to the article can be preferentially extracted.

Further, in the learning means, principal component analysis is performed on the extracted feature vector to obtain an eigenvalue / eigenvector, the gray value vector is converted into a significant principal component vector using these, and this It is also possible to use as a reference vector and also in the identifying means to perform a process of converting a grayscale value vector into a principal component vector using the eigenvector, and perform identification from the principal component vector and the reference vector. . This allows
Since the feature vector can be dimensionally compressed and only significant features can be selected and used as the principal component vector, the amount of processing can be further reduced.

Further, the standard deviation σ of the distance distribution of the learning sample with respect to the reference vector can be used as the dispersion degree.

As the allowable amount, a coefficient that can be variably set by the user is applied to the dispersion degree (for example, multiplication).
Values can be used. This allows the user to adjust the strictness of identification by this system according to his / her purpose.

When the system is used for tablet identification in an automatic tablet packaging machine, it is desirable that the system is placed between the tablet selection process and the selected tablet packaging process. In that case, it is desirable to provide a selection mechanism that sends the selected tablets to the packaging process or discharges them or returns them to the tablets according to the identification result of the present system.
As a result, if the identification result is negative, the wrong tablet will not be packed, and only the correct tablet will always be output from the packing machine.

[0022]

Embodiments of the present invention will now be described in detail with reference to the drawings.

FIG. 1 shows a functional configuration of a tablet identification system according to an embodiment of the present invention.

This tablet identification system operates on a computer 100 equipped with the NTSC TV camera 1 as an external device. The computer 100 includes an image input unit 10
1, name pattern input unit 102, inverse γ correction unit 103, RGB gray conversion unit 104, position / orientation correction unit 105, feature extraction unit 106, learning unit 107, dispersion degree calculation unit 108, identification unit 109, and dictionary file 110. Prepare

The operation of this system can be roughly divided into a learning phase and an identification phase. Hereinafter, the functions and operations of each unit will be described separately for (A) learning phase and (B) identification phase.

(A) Learning Phase The system must first go through a learning phase as initialization or preparation before starting its operation. In the learning phase, for each brand of tablets to be identified, multiple samples and accurate brand names are entered,
Based on this, the reference vector and the degree of dispersion of each brand are calculated and stored as the reference vector file 10.

First, the image input section 101 inputs a color analog image obtained by photographing a tablet placed on a plain black background by the NTSC television camera 1, and converts it into an RGB digital image. Further, the brand input unit 102 inputs the brand name of the tablet into the system.

Next, the inverse γ correction unit 103 removes the influence of the γ correction applied to each of the RGB planes, and performs the inverse γ correction so that the input / output characteristic of the camera 1 becomes substantially linear. At this time, for example, 2.2 is used as the value of γ. This is because the NTSC television camera 1 is usually configured to perform γ correction of γ = 2.2 on a captured image. By performing this inverse γ correction, the contrast between the tablet region and the background region (black background) in the image is increased, and the subsequent processing can be effectively performed.

Next, the RGB gray conversion unit 104 causes the RG
Using the grayscale value of the G component of the B component as it is, RGB
Converts the image to a gray image with only grayscale components. Where G
The reason for using a component is that G
This is because the components have a wider dynamic range than the other two components, and thus it is considered that a subtle difference in white can be reflected in the light and shade in a tablet having an overwhelmingly large amount of white. By converting an RGB image into a gray image, the following processing amount can be reduced to 1/3.

Next, the position / orientation correction unit 105 performs processing so that the position and orientation of the tablet in the image will always be the same position and orientation if they have the same name. FIG. 2 shows a detailed processing flow of this position / orientation correction.

As shown in FIG. 2, first, the gray image is binarized by using a predetermined threshold value,
Only the tablet area is extracted (step 201). At this time, for example, Otsu's method based on the discrimination criterion is used as a method of selecting the binarization threshold value. This method is a very effective method for an image that can be clearly divided into two areas, a background area and a tablet area. For details, see “Nobuyuki Otsu: Automatic Threshold Selection Method Based on Discrimination and Least Squares Criterion, IEICE Transactions Vol. J63-DN.
o. 4, pp. 349-356, 1980 ".

Next, the center of gravity of the tablet area in the binarized image is obtained, and the center of gravity is moved to the center of the image (step 202). Then, in the tablet area, the gray image is binarized by using a threshold value different from the above threshold value to extract the area of the identification number code printed or stamped on the tablet surface (step 203). The method of Mr. Otsu based on the above-mentioned discrimination standard can also be used for the binarization threshold selection method at this time.

Next, the second moment of the extracted identification code area is obtained, and the entire image is rotated by affine transformation so that the direction in which the second moment becomes maximum coincides with the X axis of the image (step 204). .

By correcting the position and direction of the tablet area in this manner, the identification accuracy described later is improved. This is because the appearance characteristics of tablets depend on the position and the direction. In addition, since the same tablet always has the same position and direction, the exact position of the tablet with respect to the camera 1 at the time of shooting
No need to adjust orientation.

Referring again to FIG. 1, next, the feature extraction unit 1
06 performs processing for extracting the feature vector of the tablet from the position / orientation-corrected gray image. FIG. 3 shows a detailed processing flow of this feature extraction. Further, FIGS. 4 to 6 show image examples for explaining the feature extraction processing step by step.

First, a rectangular area 500 of a predetermined size including the whole or a part of the tablet area is cut out from the gray image 400 whose position and orientation are corrected as shown in FIG. 4 (FIG. 3, FIG. 3). Step 301). Here, the rectangular area 5
The size of 00 is determined by a statistical method as a size that enables the most accurate feature extraction based on the result of performing feature extraction on tablets of various brands in advance.

Next, as shown in FIG. 6, the rectangular area 5
00 is divided into small sections 600 of arbitrary size N × M (step 302). Here, the size N of the small section 600
× M is determined in proportion to the processing capacity of the computer.

Next, for each small section 600, a representative value is calculated from the gray values of all the pixels (step 303). The median is used as the representative value, for example. Even if the distribution of gray values is obviously biased like the tablet area and the background, the median value is
This is because the entire distribution can be well reflected.
Then, a grayscale value vector having the representative value of each small section 600 as an element is extracted as a feature vector (step 30).
4).

In the learning phase, a large number of learning samples of various brands of tablets to be identified are supplied to this system, and the above-described processing from photographing to feature extraction is performed on each learning sample.

Referring again to FIG. 1, in the learning section 107,
For each issue, principal component analysis is performed on the total gray value vector extracted from many learning samples of that issue to obtain the eigenvalue and the eigenvector. Then, the contribution ratio is calculated based on the eigenvalue, several principal components are selected from the one having the highest contribution ratio, and the above-mentioned eigenvector is used to convert the above all gray value vector into the principal component vector. As described above, by selecting a significant feature based on the result of the principal component analysis, the dimension of the feature vector can be reduced, and the amount of processing thereafter can be reduced. Next, the obtained main component vector is averaged for each issue to be a reference vector, and this reference vector is stored in the dictionary file 110 as a reference vector of the issue input from the issue input unit 102. Further, the eigenvector of each issue is also stored in the dictionary file 110.

The dispersion degree calculation unit 108 obtains the standard deviation σ for each issue when the average is set to 0 from the distribution of the distances between the reference vector and the principal component vectors of many learning samples. Here, the distribution of the distance of the learning sample with respect to the reference vector is as shown in FIG. 7, for example. The standard deviation of such distance distribution is calculated for each stock,
This is stored in the dictionary file 110 as the dispersion degree of the brand input from the brand input unit 102.

Hereinafter, the reference vectors and the degree of dispersion of all the brands stored in the dictionary file 110 are collectively called a dictionary.

(B) Identification Phase Using the dictionary created in the learning phase, an identification test is performed to determine whether the tablets supplied to the system correspond to their brands. Here, the tablet to be identified and the brand name are input to the system.

Referring to FIG. 1, first, with respect to the input tablets, starting from the camera photographing and the input of the brand name as in the learning phase, the image input unit 101 to the feature extraction unit 10 are started.
A series of image processing up to 6 is performed and the gray value vector is extracted.

Next, the identification unit 109 uses the eigenvector obtained in the learning phase to convert the grayscale value vector into a principal component vector. Next, the distance between this principal component vector and the reference vector corresponding to the input brand is calculated. At this time, Euclidean distance, similarity, etc. are used for the distance function. A general distance function is described, for example, in “Noboru Funakubo: Visual Pattern Processing and Recognition, published by Keigaku Shuppan, 1990”.

By the way, in the present embodiment, since the gray value vector is used as the basic feature vector, the distance calculation process is performed by superimposing the reference image and the identification target image to calculate the degree of coincidence. It is none other than. Such a feature vector has hardly been used in the conventional pattern identification system, but it is effective when a large category is to be processed because it can maximize the use of image information.

Further, the identifying unit 109 compares the distance thus obtained with a permissible value r.sigma., Which is determined from the dispersion degree (standard deviation) .sigma.
If it is within σ, it is determined that the tablet is a tablet that corresponds to the input brand, and if not, it is determined that it is a tablet of another brand that does not correspond, and the determination result is output.
It is desirable to utilize this result in the automatic tablet packing machine for selecting whether to pack the identified tablet, discharge it without packing, or return it to the original tablet. Alternatively, all tablets are packaged regardless of the identification result, but the identification result may be printed on the surface of the packaging paper and checked by the pharmacist later.

Here, the coefficient r that determines the allowable value r · σ can be arbitrarily set by the user. For example, r
= 1.0, the identification standard becomes strict and tablets that do not correspond to the brand can be easily repelled. However, even in the case of tablets that actually correspond, scratches on the tablet surface, When the distance from the reference vector is large due to a lack or other reasons, it is determined that the tablet does not correspond and is repelled. On the contrary, when the allowable value is large such as r = 3.0, the result opposite to the above example is produced. In this way, the behavior of this system varies greatly depending on the allowable value. Therefore, it is necessary for the user to set an appropriate allowable value depending on the purpose, for example, to repel tablets that are slightly different while giving priority to safety.

As described above, in the tablet identification system of this embodiment, the brand and tablet identification inspection can be performed without relying on human visual inspection.

The present invention is not limited to the above embodiments, but can be implemented in various different modes without departing from the scope of the invention.

[0051]

According to the present invention, it is possible to automatically identify an article. In particular, when used in the operation of an automatic tablet packaging machine, only correct tablets can be reliably provided to the patient, so that high quality medical treatment can be realized.

[Brief description of drawings]

FIG. 1 is a block diagram showing the configuration of a tablet identification system according to an embodiment of the present invention.

FIG. 2 is a flowchart showing processing of a position / orientation correction unit in the embodiment.

FIG. 3 is a flowchart showing a process of a feature extraction unit in the embodiment.

FIG. 4 is a diagram showing an example of a gray image after applying a position / orientation correction unit in the embodiment.

FIG. 5 is a diagram showing a rectangular area cut out from the image of FIG. 4 in the embodiment.

FIG. 6 is a diagram showing a state in which the image of FIG. 5 in the embodiment is divided into small areas.

FIG. 7 is a diagram showing an example of a distance distribution of learning samples with respect to a reference vector in the same embodiment.

[Explanation of symbols]

 1 NTSC TV camera 100 Computer 101 Image input unit 102 Brand input unit 103 Inverse γ correction unit 104 RGB gray conversion unit 105 Position / orientation correction unit 106 Feature extraction unit 107 Learning unit 108 Scattering degree calculation unit 109 Identification unit

Claims (9)

[Claims] 1. An article identification system for determining whether an article input to the system is related to an input category, and an image input means for inputting an image of the article, and a category input means for inputting the category of the article. A feature extraction means for extracting a gray value vector of the input image as a feature vector, and a distance between the extracted feature vector and a reference vector of the input category prepared in advance, and the distance is predetermined. An article identification system comprising: an identification unit that determines whether or not the input article is related to the input category based on whether or not the article is within a permissible value. 2. The system according to claim 1, wherein when a learning sample relating to a certain category and its category are input to the system, the feature extracting means receives the feature vector of the learning sample and inputs the feature vector. Learning means for calculating a reference vector of a category, scatter degree calculation means for calculating the scatter degree of the distribution of the distance between the calculated reference vector and the feature vector of the learning sample, for determining the allowable value, An article identification system, comprising: a dictionary file that holds the calculated reference vector and the degree of dispersion so that the identification unit can use it. 3. The system according to claim 1, further comprising an inverse gamma correction means for performing an inverse gamma correction on the input image so as to remove the influence of an inherent gamma correction. Identification system. 4. The system according to claim 1, wherein the input image is an RGB color image, and an RGB gray conversion for converting the RGB color image into a gray image by using a G component of the RGB color image. An article identification system further comprising means. 5. The system according to claim 1, further comprising position / orientation correction means for correcting the article region in the input image to a constant position and direction. 6. The system according to claim 1, wherein the feature extracting means cuts out a rectangular area of a predetermined size including an article area from the input image, and sets a plurality of grayscale values sampled from the rectangular area as elements. An article identification system characterized in that a grayscale value vector that is set is the feature vector. 7. The system according to claim 2, wherein the learning means performs a principal component analysis on the feature vector of the learning sample to obtain an eigenvalue and an eigenvector, and uses the eigenvalue and the eigenvector to determine the feature vector. Converting to a significant principal component vector, using this principal component vector as the reference vector, the identifying means transforms the feature vector of the identification target article into a principal component vector using the eigenvector, and the principal component vector and the An article identification system characterized by performing identification from a reference vector. 8. The system according to claim 2, wherein the scatter degree is a standard deviation of a distribution of distances between the reference vector and a large number of training samples prepared in advance in the designated category. Item identification system. 9. The system according to claim 2, wherein the allowable value is a value obtained by applying a coefficient that can be variably set by the user to the dispersion degree.