EP1437691A1 - Method for recognizing the relief of a coin in a coin operated apparatus - Google Patents

Abstract

The vending machine examines the patterns of various coins fed into the coin slot. It has electronic equipment which performs a variety of tasks, including scanning the edge of the coin and comparing the received picture with one or more stored pictures. Three-color scanned pictures of embossed patterns are produced and may be used to identify the type and country of origin of the coins.

Description

The present invention relates to a method for recognizing an embossed image a coin in a coin operated machine.

Coin operated machines such as coin validators discriminate against a given one Set of coins in a very short time. There are a number of procedures for this known, a large number of which use the coin material as distinguishing criteria starts. The thickness and diameter of the coin are also used to differentiate. However, for the global monetary system it is not excluded that the same or almost the same blanks can be used for different coins. An important distinguishing feature for the coins is therefore the embossed image.

DE 37 39 239 C2 describes a method and an apparatus for processing a embossed card known. The embossed side of the card is made up of two opposite oblique directions alternately illuminated. The card is different at these Illuminations added. The difference in the pictures taken is compared with reference data to recognize the embossed characters. This Process is not for highly reflective, metallic surfaces of coins suitable.

From DE 33 05 509 an optical coin checking device is known, in which one under an angle illuminated surface is observed at different angles. The quotient of the brightness at different angles gives information about the gloss level of the coin enclosed.

From US 5,839,563 a coin validator is known in which for the picture taken a pattern comparison is carried out on a coin. For good lighting to achieve the object field, the coin is illuminated circularly.

DE 100 51 009 describes a method for recognizing an embossed image of a coin known, in which the coin moves past the light source and over two or more lighting sections each illuminated from a different direction becomes. A difference image is determined from the recordings, which indicates whether it is is a photographic reproduction of the embossed image or an embossed image.

The object of the present invention is a method for detection to provide an embossed image of a coin that is suitable for simple Means in a coin-operated machine a real or false signal for the to recognize inserted coin.

According to the invention, the object is characterized by a method with the features Claim 1 solved. Advantageous designs form the subject of the subclaims.

In the method according to the invention, the coin to be recognized is on one Image receiver and a light source moved past. The light source has at least two, preferably three lighting sections that form an object field recognizing coin from different directions, at the same angle compared to the surface normal of the object field, not overlapping with each other Illuminated wavelength ranges. Object field of the coin is preferred the entire embossed image of the coin. The embossed image is illuminated for the Illumination sections at an equal angle. The lighting led to the name "Selective Stereo Gradient Method" (SSGM) because of a central Record the image only light at a certain angle of reflection or gradient is recorded in the embossed image. Of the in the invention Process illuminated surface, an image receiver records a shot on. The one shot turns into images of the individual lighting sections won. For this, the color components of the different Wavelength ranges separated. The images obtained in this way become a maximum image determines the maximum intensity value for each pixel is assigned from the images. The real or false signal for the recorded one The embossed image is determined from the maximum image. In the invention Process is made from an image by separating it into partial images different directions, but with the same angle of inclination (azimuth angle), determines a maximum image that the coin surface for recognition reproduces the embossed image sufficiently well.

For the generation of the real or false signal, it has proven to be advantageous for the shot and / or the maximum image, center point and diameter to determine the coin. One or more are preferred in the maximum image Cut out circular ring segments with predetermined radii.

Surprisingly, it has been found that for the circular ring segments mean gray value and / or its deviation, preferably standard deviation is already a good indicator of the type of coin to be recognized. Especially The advantage of this feature of the embossed image is that it can be compared low computing effort can be determined.

In a further preferred step, the values of the pixels in the maximum image along circular ring profiles with a predetermined radius in a Frequency representation transformed. A Fourier transformation has which is preferably designed as a fast Fourier transformation (FFT) is proven to be suitable. The transformed spectra are with reference spectra compared, the deviation in determining the real or False signal is taken into account. Surprisingly, this step of the process has also been found pointed out that the spectral comparison along Circular ring profiles are sufficient to reliably indicate whether they are genuine or false of the embossed image.

In a further preferred embodiment, pairs of images are used the individual lighting sections determined difference images. The difference pictures allow discrimination of photographic reproductions of the Embossed image and on the other hand are particularly suitable for individual cutouts from the difference images with reference patterns for a match to compare, so-called template matching. In this process Excerpts of images of an embossed image compared with reference images.

In the method according to the invention, a false signal is generated when the average gray scale value of a difference image is below a predetermined threshold.

The separation of the images from the recording is done by using filters, which are transparent to the individual wavelength ranges. Prefers Mosaic filters are used when using a CMOS or CCD camera.

In the method according to the invention, a first step is carried out Classification of possible coin types, whereby of the possible coin types First of all, exclude those who start from the maximum image the mean gray value and / or the deviation outside of a predetermined one Interval lie; for the remaining coin types, the transformed ones Spectra with the characteristic frequencies of the reference spectra compared.

In a further development, one or more are made after the comparison of the spectra Difference images determined and excerpts with reference patterns of the still comparing coin types.

A real signal is preferably generated for the coin to be checked whenever reduced the number of possible coin types to a single possible coin type has been. The false signal is preferably always generated when none Coin type is more possible.

Fig. 1

ein Flußdiagramm für einen Münzprüfer,

Fig. 2

Nassie-Schneidermann-Diagramm für eine beispielhafte Anwendung, bei der Euromünzen aus unterschiedlichen Ländern diskriminiert werden, und

Fig. 3

ein Beispiel für ein in einem Unwarp-Bild erfolgreich gefundenes Referenztemplate.

The method according to the invention is described in more detail below using a preferred exemplary embodiment. It shows:

Fig. 1

a flow chart for a coin validator,

Fig. 2

Nassie-Schneidermann diagram for an exemplary application in which euro coins from different countries are discriminated against, and

Fig. 3

an example of a reference template successfully found in a warp image.

In the 3-color SSGM (3-color selective stereo gradient method) form three special drawing images of the coin the basis for the evaluation of the topography the embossed images. The drawing files are extracted from a single image.

Here, an LED color lighting ring with five LEDs each of the colors red and blue and green arranged by color in three 120-degree sectors. During the run A trigger signal is triggered on the coin by a light barrier triggers an LED flash for all three colors at the same time, as well as the camera caused to take a single picture. The one used for recording CMOS or CCD camera is with a mosaic filter, for example one Bayer-Patter equipped the information from the three sectors in the recording separates. After the separation, there are again three drawing files that allow this Illuminate the recognizing embossed image from different directions. In the flow chart 1 shows the image recordings in method step 10 and is separated into three individual images in method step 12. Here, the Colors red, green and blue are used.

In step 14, a difference image is calculated from the partial images, the use of which ensures that the photos are counterfeit-proof. In the method according to the invention, the difference image is used only for the verification of the embossed image; the classification is subsequently carried out on the maximum image 16, which has a much stronger structure. The maximum image M is defined as M (x, y) = max [R (x, y); G (x, y); B (x, y)], whereby for each point (x, y) the value with the greatest intensity is selected from the set of images R (x, y), G (x, y) and B (x, y).

In the subsequent method step 18, the originally recorded Picture the coin searched, cut out and in a picture format with predefined Image size, for example 256 x 256 pixels. This surgery will also the diameter determines. In this step, the images are independent of the Coin size scaled to the same size, which for the one to be discussed below Comparison of the mean gray values is important.

In step 20, the image is segmented, starting from the center of the image, Circular ring regions are considered. It has been found that the Decomposing the picture into an outer ring, a middle ring and a coin center is particularly advantageous with 2 euro coins.

In the subsequent step 24, the mean gray value for the three ring regions and the standard deviation of the gray values in the three ring regions certainly.

In addition, there is another distinguishing feature in the outer ring of the coin compared. To do this, the outer ring is converted into a binary image by using suitable ones Threshold values converted. The binary images are perpendicular to each other on two standing axis projected. A characteristic feature of the embossed image the coin is the distance between the center of gravity (COG) and the geometric Center of the picture. The Center of Gravity (COG) is considered to be with the Distance weighted average of the pixels determined. The picture here is that scaled image in which the outer ring is viewed. The COG is about the Axis projection method determined. Instead of a binary image, a Gray value or color image can be used.

In a further method step 22, which is parallel to that described above Method step 24 can run, but can also be carried out afterwards, are grayscale profiles on circular rings around the center of the scaled images sampled. The radii of the circular rings are predetermined. The values of the pixels Fourier transform (FFT) is performed along the circular ring profiles. The dominant Frequency of each FFT spectrum is determined. The one for the five circular rings The dominant frequency determined forms a further characteristic in its entirety Characteristic for the coin.

In step 26, a preliminary comparison is carried out as to whether those obtained up to that point Measured values for the coins taken in at predetermined admissibility intervals lie. This comparison leads to a classification in step 28. It turns out in the classification step 28 that the pattern does not match any of the given ones If references match, the classification process leads to rejection 30 of the coin. If it turns out that several coins are possible, then in Step 32 carried out a template comparison for these coin types. As shown in Fig. 3, this is the picture taken of the coin developed and on the double angular range to avoid cuts in the Reference sample added. The image 34 thus supplemented is provided with a reference pattern 36 compared. As shown in FIG. 3, the position in for the reference pattern 36 the picture 34 found.

Method step 32 is a verification step, in which it is checked whether a Coin type in the shortlist actually comes into consideration. It is conceivable here also a modification of the process step such that every possible coin type with the reference images of all possible coin types is folded to the coin type with the highest agreement.

In the final step 34, the difference image calculated in step 14 checks whether this is an embossed image or a photo of an embossed image is. This step can also be at the beginning of the comparison.

A variety of different approaches can be chosen for this step become. In practice, two approaches have proven to be particularly advantageous. In a first approach, the size of the template at the point in the picture, in who found the pattern, cut out an image section the size of the template. This section of the image is obtained by applying threshold values in changed a binary image. For example, the Sum of the mean gray value in the sample plus the standard deviation of the gray values be set in the pattern. Other variable or fixed threshold values are conceivable.

Differential images are determined from the three partial images. To be particularly advantageous has proven here:

Determine a first difference image (Dift1) from the images for red (R) and green (G). A second difference image (Diff2) is created from the images for red (R) and blue (B) determined. Finally, a difference image (Diff12) is shown as the difference between the first and the second difference image formed. However, it can also have a maximum picture between the first difference image (Diff1) and the second difference image (Diff2): Diff12 = max [Diff1, Diff2]. From the finished one Difference image (Diff12) becomes an unfolded and double the angular range supplemented picture produced. This picture is congruent with the one before image made for pattern comparison. These unfolded pictures become an image section the size of the reference pattern is taken from the same place. The Extracted image sections are multiplied together and for the product the mean gray value is calculated. If the mean gray value is below one predetermined fixed threshold, it is a photo. It stays in the In the case of a photo after the original image has been multiplied by the difference images not enough information left in the gray values of the product image.

As an alternative to the aforementioned operation, the reference template can also be used a suitable threshold value can be converted into a binary image. For example can again be the sum of the mean gray value in the pattern plus the Standard deviation of the gray values in the pattern can be used. Others too variable or fixed threshold values are possible. As described above, the difference images determined, again the two image sections, the difference value and the binary reference image are multiplied together and for that The average gray value is calculated for the product. Here too, a missing three-dimensional Topology recognized by the fact that the mean gray value below a predetermined source.

There are also other methods for combining the difference images with drawing files possible. In the above two variants, the basic idea is common, to create difference images by combining partial images, which only then have information content in the form of grayscale structures if the too recognizing coin has an embossed image. Should the coin recognition by a photo are deceived, the difference images do not have sufficient information. The key to capturing the information on the coin’s 3D embossed image lies in the lighting. The lighting must be homogeneous and in the sectors have the same intensity.

After successful embossing image recognition, the recognized coin is in step 36 accepted.

2 shows the flow of the process according to the invention in a structured diagram Procedure using the example of a recognition of coins from different countries.

1. Mittlerer Grauwert im Ring 1,

2. Standardabweichung der Grauwerte im Ring 1,

3. Mittlerer Grauwert in Ring 2,

4. Verhältnis der mittleren Grauwerte in Ring 1 und 2,

5. Standardabweichung der Grauwerte in Ring 2,

6. Mittlerer Grauwert in Ring 3,

7. Verhältnis der mittleren Grauwerte von Ring 2 und Ring 3,

8. Standardabweichung der Grauwerte in Ring 3,

9. Verhältnis der mittleren Grauwerte von Ring 1 und 3,

10. Abstand des COG im Ring 1 zum geometrischen Mittelpunkt der Münze bzw. zum Zentrum des Rings,

11. Durchmesser,

12.-16. Dominante Frequenzen der Grauwerte auf fünf Kreisringen.

In step 28, three color partial images (R, G, 13 images) are extracted from the recorded camera image of the coin. In step 40, a maximum image is calculated from the three partial images. In step 42, the coin is segmented from the recorded image and the segmented image is rescaled into a square format for further processing. The two steps described above are carried out in step 44, in which mean gray values and dominant frequencies are compared with reference images. So that not all countries of the class to be checked have been fully checked, loop 46 is repeated in the procedure. Overall, the following characteristic features are available:

1. mean gray value in ring 1,

2. standard deviation of the gray values in ring 1,

3. Average gray value in ring 2,

4. ratio of the mean gray values in rings 1 and 2,

5. Standard deviation of the gray values in ring 2,

6. Average gray value in ring 3,

7. ratio of the mean gray values of ring 2 and ring 3,

8. Standard deviation of the gray values in ring 3,

9. ratio of the mean gray values of rings 1 and 3,

10. distance of the COG in ring 1 to the geometric center of the coin or to the center of the ring,

11.diameter,

12th-16th Dominant frequencies of the gray values on five circular rings.

Using the above-mentioned measured values or a subset of these measured values a comparison is now carried out in step 48. The comparison shows that the Measured values lie in a predetermined admissibility interval, the corresponding comes Shortlisted coin type 50. Only in the frequency comparison no uniform admissibility interval is specified, but instead for the Frequency three possible measured values allowed. Each of the three measured values is with one Permission interval linked. A dominant frequency is recognized when the measured frequency within one of the three pre-determined admissibility intervals lies.

In step 54 it is checked whether at least one type is shortlisted. In this case, two unfolded images are calculated in step 56, one of which the first comes from the image scaled in step 42 and the second the reference image is.

As long as there are still unchecked coin types, the following classification loop executed. In step 60, the coin type still to be checked the corresponding reference template (reference pattern) is loaded. In step 62 compare the pattern to one of the unfolded images generated in step 56. If the template is found in the unfolded image and exceeds the match a minimum value (method step 64), the location is found in step 66 of the template is drawn into the unfolded image. For the cutouts, the Comparison described above, generating the product image in step 68 carried out. The embossed image verification is then carried out. Is the If the value for the embossed image verification is poor, the tested one separates in step 72 Coin also out. Should be more than one coin after loop 58 is completed If there are any left over, the coin with the best comparison values can be used (for example from step 48) can be selected.

Claims (16)

Method for recognizing an embossed image of a coin in a coin-operated machine, with the following method steps: the coin to be recognized is moved past an image receiver and a light source, the light source has at least two illumination sections which illuminate an object field of the coin to be recognized from different directions, at the same angle with respect to the surface normal of the object field, with wavelength regions which do not overlap, the image receiver records a recording of the object field, from which images of the individual lighting sections of the individual wavelength ranges are obtained, a maximum image is determined from the images, in which the maximum intensity value from the images of the individual wavelength ranges is assigned to each pixel, a real or false signal is determined from the maximum image. Method according to Claim 1, characterized in that the center and diameter of the coin are determined for the recording and / or the maximum image. Method according to claim 2, characterized in that one or more circular ring segments with predetermined radii are cut out in the maximum image. Method according to Claim 3, characterized in that the real or false signal is generated using the mean gray value of the circular ring segments and / or a deviation of the gray values from the mean gray value. Method according to one of Claims 1 to 4, characterized in that the values of the pixels in the maximum image are transformed into a frequency representation along circular ring profiles with a predetermined radius. Method according to Claim 5, characterized in that a Fourier transformation, in particular a rapid Fourier transformation (FFT), is carried out as the transformation of the pixel values. Method according to Claim 5 or 6, characterized in that the transformed spectra are compared with reference spectra and the deviations are taken into account when determining the real or false signals. Method according to one of Claims 1 to 7, characterized in that differential images are determined from pairs of images relating to the individual lighting sections. Method according to Claim 8, characterized in that one or more sections from the difference images are compared with reference patterns for their correspondence. Method according to one of claims 1 to 9, characterized in that the separation of the recording in images of the individual lighting sections takes place via filters. Method according to Claim 10, characterized in that a mosaic filter for a CMOS or CCD camera is used to separate the recording into images. Method according to one of claims 1 to 11, characterized in that a false signal is generated if the mean gray value of a difference image is below a predetermined threshold value. Method according to one of the preceding claims, characterized in that In a first step, a possible coin type is initially classified, with the possible coin types initially excluding those in which, based on the maximum image, the mean gray value and / or the deviation lie outside a predetermined interval and / or the transformed spectra with their characteristic frequencies deviate from predetermined reference spectra of the coin types. Method according to claim 13, characterized in that for coin types still remaining after the comparison, one or more difference images are determined from the images relating to the wavelength ranges and are compared in detail with reference patterns of the coin type still to be compared. Method according to claim 13 or 14, characterized in that a real signal is always generated when the number of possible coin types has been reduced to a single coin type. A method according to claim 15, characterized in that a false signal is always generated when the number of possible coin types has been reduced to zero.

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