Method for detecting the position of containers

Abstract

One aspect of the invention relates to a computer-implemented method (20) for detecting the position of a plurality of containers (14), each container (14) comprising an upper surface (141), the method comprising: - acquiring (21) at least one image (I1) of the plurality of containers (14); - obtaining (23) local bit patterns (I2) in the acquired image (I1) on the basis of a dimension of the upper surface (141) of each container (14) of the plurality of containers (14), the dimension of the upper surface (141) of each container (14) being calculated on the basis of a predefined upper surface size (141) of the containers (14); - applying (24) a colour mask to the acquired image (I1) on the basis of a predefined upper surface colour (141) of the containers (14) to obtain a masked image (I3); - combining (25) the partially masked image (I3) and the local bit patterns (I2) to obtain sets of pixels representing the upper surfaces (141) of the containers (14); - calculating (27) centres of gravity of the sets of pixels representing the upper surfaces (141) of the containers (14) of the plurality of containers (14) to obtain the position of each upper surface (141) of the containers (14).

Description

DESCRIPTION Method for detecting the position of containers TECHNICAL FIELD OF THE INVENTION

[0001] The technical field of the invention is that of container detection, preferably in container packaging lines.

[0002] The present invention relates to a method for detecting the position of containers and in particular for detecting their position via the detection of an upper surface of each container, knowing its size and color. TECHNOLOGICAL BACKGROUND OF THE INVENTION

[0003] In container packaging lines, containers are transported between different processing stations via conveyor belts.

[0004] For the purposes of the present invention, the term "container" includes an individual object intended to contain, but not limited to, a fluid, a liquid, powders or granules, in particular of the food or cosmetic type. Such a container may be a bottle or a flask, or a cardboard carton, or even a can. A container may be made of any type of material, in particular plastic, metal or glass. Depending on its shape and material, a container may be rigid or semi-rigid.

[0005] Furthermore, a container can have any type of shape, symmetrical or not, regular or irregular. In addition, a container can have a rounded section, generally circular or ovoid in shape, or a polygonal section, notably rectangular or square.

[0006] In particular, such a container comprises a bottom which may be flat or substantially flat, or, conversely, having one or more cavities, as is the case, for example, of a can with a concave bottom or a bottle with a so-called “petaloid” bottom.

[0007] As is known, within an industrial line, the containers can receive several different successive treatments, such as the manufacture of the container, for example during a plastic injection operation or stretch-blow molding in the case of a plastic bottle, followed by filling and then closing with a cap and labeling. At the end of these treatments, the containers are said to be "finished".

[0008] For handling purposes, such containers are packaged in batches.

[0009] Furthermore, each batch comprises a group of several containers, assembled in a matrix arrangement, generally of a parallelepiped shape, often square or rectangular, in columns and rows. For example, a typical batch groups six containers in two rows and three columns.

[0010] During these different stages, the containers are transported along the production line, in a direction of movement extending longitudinally, from upstream to downstream, between and within the different stations dedicated to each treatment that the containers must undergo. Such transport can be carried out through at least one conveyor installation.

[0011] Further, the conveying is carried out by moving the containers resting directly at their bottom on the upper face of one or more conveyors. This upper surface is mobile, forming a belt, generally in the form of one or more endless bands wound around at least one motorized winding.

[0012] Along a production line, containers may be transported in bulk, meaning that the containers are positioned in a staggered and disorderly manner, particularly against each other, over all or part of the width of a conveyor. For specific processing of containers, such as packaging, it is necessary to order the bulk flow of containers in several lines, particularly with a view to ensuring their grouping downstream and then batch coating from such a multi-line flow of containers.

[0013] Such an operation is carried out by means of container alignment means, generally taking the form of a funnel positioned above the conveyor, making it possible to receive at the inlet the bulk flow of said containers and to align them in several parallel lines delivered at the outlet. Further, such an alignment means comprises vertical sheets mounted on a frame and spaced so as to form corridors. In addition, the corridors are provided converging from upstream to downstream, narrowing according to the direction of movement of the containers.

[0014] Thus, at the inlet, the flow of bulk containers separates upon contact with the upstream end of each of the sheets, the containers moving laterally on either side to enter each lane. As they advance, the convergence in each lane forms a bottleneck, forcing the bottles to position themselves one behind the other, in particular by friction upon contact with the walls of the sheets and by rolling and friction between the containers as they advance along each lane.

[0015] In particular, the decreasing width of the corridors is configured according to the number of containers to be received at the entrance, to be channeled to obtain an exit flow with lines composed of one or more containers wide, as well as the format of said containers, in particular their diameter.

[0016] A disadvantage of such a funnel aligner is the recurring blockage of containers at the inlet and inside the aisles. Indeed, the conveying speed, combined with the narrowing of the aisle width, often results in two containers being positioned side by side, which causes them to jam, particularly due to the friction of the products between them and against the walls of the sheets. It is then necessary to stop the conveying in order to unblock the products, particularly by manual intervention by an operator: a tedious and precarious operation to reach the containers to be unblocked. This blockage also leads to a loss of production and therefore a loss of efficiency of the line.

[0017] To avoid these container blockages, some solutions propose the detection of the container position by supervised learning. For this, a camera is used and the position of the containers is obtained as output from a machine learning algorithm using a model trained from annotated data. These solutions allow, by obtaining the position of the container, to deduce its speed and therefore to predict future blockages. A disadvantage of such solutions is that it requires manual annotation of the position of each container on a plurality of images. When changing containers, packaging lines and / or cameras, new annotated data must be provided to the model, and therefore new annotations must be made by an operator.

[0018] There is therefore a need for a simple solution to obtain the position of containers in packaging lines that does not require complex annotation operations when changing the format or type of container. SUMMARY OF THE INVENTION

[0019] The invention offers a solution to the problems mentioned above, by proposing a detection of the position of containers by image processing, requiring no annotation and few hardware resources.

[0020] One aspect of the invention relates to a computer-implemented method of detecting the position of a plurality of containers, each container comprising a top face, the method comprising: Acquisition of at least one image of the plurality of containers, Obtaining local binary patterns of the acquired image from a dimension of the upper face of each container of the plurality of containers, the dimension of the upper face of each container being calculated from a predefined upper face size of the containers, Applying a color mask to the acquired image from a predefined top face color of the containers to obtain a partially masked image, Combination of the partially masked image and local binary patterns to obtain sets of pixels representing the upper faces of the containers, Calculating centers of gravity of sets of pixels representing the upper faces of the containers of the plurality of containers to obtain the position of each upper face of the containers.

[0021] By means of the invention, a position of the containers of each processed image is obtained from simple data such as the color of the upper surfaces of the containers and the dimension of the upper surfaces of the containers. The method makes it possible to obtain container positions reliably, not requiring annotation of the position of similar containers on similar images unlike the prior art.

[0022] According to additional, non-limiting characteristics, the image is obtained by at least one camera whose field of vision is directed towards the plurality of containers,

[0023] According to additional, non-limiting characteristics, the method further comprises, after the combination of the masked image and the local binary patterns: At least one dilation of the sets of pixels representing the upper faces of the containers, At least some erosion of the pixel sets representing the upper faces of the containers.

[0024] According to one embodiment, the method comprises: a first expansion of the sets of pixels representing the upper faces of the containers then a first erosion of the sets of pixels representing the upper faces of the containers then a second expansion of the sets of pixels representing the upper faces of the containers.

[0025] According to one embodiment, the position of each upper face obtained comprises coordinates of the center of gravity of the upper face in the image.

[0026] According to one embodiment, the combination of the masked image and the local binary patterns is carried out by multiplying a first matrix with a second matrix, the first matrix representing the masked image, the second matrix representing the local binary patterns.

[0027] According to one embodiment, the predefined top surface size and the predefined top surface color are obtained from a human-machine interface, before obtaining local binary patterns and before applying a color mask to the acquired image.

[0028] According to one embodiment, the predefined top surface size and the predefined top surface color are stored in a database.

[0029] According to one embodiment, before calculating the top surface dimension, the predefined container top surface size is transformed into the container top surface size in pixels of the image acquired from a predefined distance between the camera and the top surface of the containers and a predefined resolution of the camera in pixels.

[0030] Another aspect of the invention relates to a computer program product comprising instructions which, when the program is executed by a computer, cause the latter to implement the method according to the invention.

[0031] Another aspect of the invention relates to a computer-readable recording medium comprising instructions which, when executed by a computer, cause the computer to carry out the method according to the invention.

[0032] The invention and its various applications will be better understood by reading the following description and examining the accompanying figures. BRIEF DESCRIPTION OF THE FIGURES

[0033] The figures are presented for information purposes only and in no way limit the invention. Figure 1 shows a schematic representation of a packaging line in which the method according to the invention is implemented, Figure 2 shows a schematic representation of the method according to the invention, Figure 3 shows a schematic representation of results of the steps of the method according to the invention, Figures 4a and 4b show schematic representations of containers whose position can be obtained by the method according to the invention, Figure 5 shows a schematic representation of a step of the method according to the invention. DETAILED DESCRIPTION

[0034] Unless otherwise specified, the same element appearing in different figures has a single reference.

[0035] Figure 1 shows a schematic representation of a packaging line in which the method according to the invention can be implemented.

[0036] The method according to the invention, shown schematically in Figure 2, makes it possible to detect the position of containers, for example in a packaging line.

[0037] Such a packaging line is for example the packaging line 10 of Figure 1. The packaging line 10 comprises a belt 12, a set of lanes 13 and a plurality of containers 14. The plurality of containers 14 circulates upright, that is to say resting on their bottom and with a free upper face. The belt 11 makes it possible to move the containers 14 out of and into the lanes 13. The invention applies to any packaging line and is not limited to packaging lines of the type of the packaging line shown in Figure 1.

[0038] When the invention is applied to the packaging line 10, the packaging line 10 is equipped with a camera 11. The camera 11 is an image capture device, which can be of any type. Preferably, the camera 11 comprises an image sensor, for example of the CCD or CMOS type, and an optical means capable of redirecting the incident light rays towards the image sensor. The camera 11 thus has a field of vision 111 defined in particular by the dimensions of its sensor and by the dimensions and type of its optical means(s). This field of vision 111 is directed towards the containers 14 circulating in or on the packaging line. By "directed towards the containers" is meant that at least a portion of the containers 14, preferably a majority of the containers 14 circulating at a given position are imaged on the sensor of the camera 11.In other words, at each instant, at least a portion of the containers 14, preferably a majority of the containers 14 circulating at a given position, are present on an image obtained by the camera 11 for the given instant.

[0039] The method 20 according to the invention is shown schematically in Figure 2. The method 20 is a computer-implemented method of detecting the position of a plurality of containers.

[0040] The method 20 may be implemented by a computer or by a processor. By "computer-implemented" is meant that the steps, or at least one step, are executed by at least one computer or processor or any other similar system. Thus, steps are performed by the computer, possibly fully automatically or semi-automatically. In examples, the triggering of at least some of the method steps may be carried out by user-computer interaction. The level of user-computer interaction required may depend on the intended level of automation and balanced against the need to implement the user's wishes. In examples, this level may be user-defined and / or predefined.

[0041] A typical example of a computer implementation of a method is to execute the method with a system adapted for this purpose. The system may comprise a processor coupled to a memory and a graphical user interface ("GUI"), the memory having recorded thereon a computer program comprising instructions for implementing the method. The memory may also store a database. Memory is any hardware adapted for such storage, possibly comprising several distinct physical parts.

[0042] The method 20 comprises a first step 21 of acquiring at least one image of a plurality of containers 14. The position of the plurality of containers 14 obtained at the end of the method is the position of the containers 14 included in the image, that is to say the containers 14 represented in the form of pixels in the image. The image comprises a plurality of pixels. The image can be obtained, by the processor or the computer implementing the method 20, directly from a camera 11, or from a local or remote database not shown. The image can be acquired by the processor or the computer via a wired or wireless connection. The image is preferably acquired in digital form. The image was previously obtained by the camera 11. The containers 14 present in the image must be seen from a low angle, that is to say that at least one predefined upper surface of the containers 14 must be visible.To do this, the camera must be placed at a height relative to the surface on which the containers 14 rest that is greater than the height of the containers 14.

[0043] The acquired image is for example image 11 shown in Figure 3, comprising a plurality of containers 14 which are bottles, each bottle comprising a substantially circular white-colored stopper.

[0044] The term "upper surface of the containers" means at least one end of the containers 14. For example, as shown in Figure 4a, when the container 14 is a bottle, the upper surface of the bottle is the upper surface 141, i.e., the surface of the bottle cap when the bottle is viewed from above. For example, as shown in Figure 4b, when the container 14 is a can, the upper surface of the can is the upper surface 141, i.e., the surface visible when the can is viewed from above. For example, when the container 14 is a brick or a box, the upper surface of the brick or box is the upper surface 141, i.e., the surface visible when the brick or can is viewed from above.

[0045] Similarly, the plurality of containers 14 may be in the form of batches. In such a case, when the containers 14 are in the form of batches 41, the upper surface of the batch 41 is the visible surface when the batch 41 is viewed from above. Alternatively, when the containers 14 are in the form of batches, the upper surface may be the upper surface of each container 14 of the batch 41.

[0046] Generally, the upper surface of a container 14 is the highest surface of the container 14 that is substantially planar or that can be approximated by a substantially planar shape, for example by a solid disc or rectangle. An upper surface is preferably a surface in a horizontal plane, that is to say in a plane substantially parallel to the bottom of the container 14 on which the container 14 rests, and therefore substantially parallel to the surface on which the container 14 rests. The “highest” surface is understood to be the highest surface relative to the bottom of the container 14.

[0047] The method then comprises a step 22 of acquiring a size of the upper surface of the containers 14 and a color of the upper surface of the containers 14. This implies that the position of the containers present on the image acquired in step 21 obtained at the end of the method 20 is the position of the containers present on the image and having an upper surface of the acquired size and the acquired color.

[0048] The top surface size and the top surface color are acquired, by the processor or computer implementing the method 20, from from a remote and / or local database or directly from a human-machine interface.

[0049] The size of the upper surface of the containers 14 is for example defined in centimeters or millimeters. The size of the upper surface is for example a size of a dimension of the upper surface of the containers 14. A dimension of the upper surface of the containers 14 is for example a diameter of the upper surface 141, for example when the upper surface is the upper surface of a bottle or any container of circular, oval, substantially circular or substantially oval shape, for example a cap diameter when the upper surface is the upper surface of a cap of a bottle 14. A dimension of the upper surface of the containers 14 is for example a width or a length of the upper surface 141, for example when the upper surface is the upper surface of a brick or any container of rectangular or substantially rectangular shape.

[0050] The color of the upper surface of the containers 14 is for example defined in RGB, YCbCr, or any other color format. The color of the upper surface is a majority color of the upper surface, the majority of color being defined in terms of surface. For example, for containers 14 which are bottles with blue caps, the acquired color of the upper surface is the color blue. The color can be a range of colors.

[0051] The size and color of the upper surface of the containers whose position is desired to be obtained are predefined, that is, they are defined before the start of the method 20 and / or before their use by the method 20. For example, the size and color of the upper surface of the containers may be defined by an operator using a human-machine interface such as a touch screen, or a computer comprising a screen and a means for entering data into the computer, for example a keyboard and / or a mouse. After definition by an operator, the size and color of the upper surface of the containers 14 may be stored in a database, accessible by the processor and / or the computer implementing the method 20. Alternatively, the human-machine interface may be directly accessible by the processor and / or the computer implementing the method 20 to obtain the upper surface size and color data.

[0052] Thus, thanks to the invention, the change of containers whose position is detected is easy, simple information on color and cap size being then simply entered by an operator and / or automatically retrieved, without requiring long and complex training or data annotation, unlike the prior art.

[0053] Once the size and color of the upper surfaces of the containers have been obtained in step 22, the size of the upper surfaces in pixels must be obtained. When the size of the upper surfaces is already indicated in pixels, the method is continued in step 23. When, on the contrary, the size of the upper surfaces is indicated in a unit other than pixels, the size must be converted into pixels. For this, the conversion is carried out by knowing the definition of the image obtained by the camera 1 1 in pixels (and therefore the resolution of the sensor of the camera 1 1 ) and the distance between the camera 1 1 and the containers 14.

[0054] The method 20 then comprises two steps 23 and 24, which can be performed simultaneously or sequentially, in any order. Steps 23 and 24 identify and extract features from the image. Step 23 identifies and extracts shape features in the image 11, and step 24 identifies and extracts color features in the image 11.

[0055] Step 23 is a step of calculating local binary patterns of the acquired image from a dimension of the upper surface of each container of the plurality of containers, the dimension of the upper surface of each container being calculated from a predefined upper surface size of the containers. When the predefined upper surface size of the containers 14 is equal to the predefined upper surface dimension, the latter is directly used in the calculation of local binary patterns. The predefined upper surface dimension is for example the radius, the diameter, a width or a length of the upper surface.

[0056] The calculation of local binary patterns involves the use of an algorithm called "Local Binary Pattern" or "LBP". The dimension of the top surface obtained from the size acquired in step 22 is used to define the neighborhood of each pixel, in number of pixels. The neighborhood is used to compare the luminance level of a central pixel to the luminance level of its neighboring pixels in the neighborhood. For each pixel, called "central", a value is assigned which corresponds to a sequence of numbers (preferably '0' or '1'), each number in the sequence of numbers corresponding to a luminance level of a pixel in the neighborhood relative to the central pixel. A histogram is then calculated on the neighborhood, of the frequency of appearance of each number or digit (preferably '0' or '1'), that is to say each combination of pixels having a luminance level smaller and larger than the central pixel. This histogram can be considered as a vector of characteristics. The concatenation of the histograms of all the neighborhoods of the image makes it possible to obtain local binary patterns. The invention can be implemented with any algorithm making it possible to obtain local binary patterns. An example of an image with local binary patterns is shown in Figure 3, image I2.

[0057] Step 24 is a step of masking the image obtained in step 21 with a color mask. Such a color mask makes it possible to modify the value of all the pixels having a color equal to or close to the predefined color or included in a color range around the predefined color, or included in the predefined color range, and / or to modify the value of all the pixels having a color different from the predefined color not included in a color range around the predefined color or not included in the predefined color range. For example, if the chosen color is white, all the white pixels can remain white and all the non-white pixels can take a similar value different from the value corresponding to the color white. The invention can use any known color mask. The image obtained is said to be masked or "partially" masked, in that only one color is masked.By "masking" we mean the application of a mask, whether it is a mask that reduces the presence of the predefined color or one that attenuates it. A color mask makes it easy to identify all the pixels having the predefined color. An example of a partially masked image obtained is shown in Figure 3, image I3.

[0058] The images I2 and I3 obtained are then combined at a step 25. For this, at each step 23 and 24, the image obtained is a digital image in the form of a matrix. The combination of the two images is preferably a multiplication of matrices. Thus, the image I4 resulting from the combination of the images I2 and I3 includes the extracted color and shape information from the image 11. This Taking into account two types of features allows for better detection of the upper surfaces of containers, and for removing at least some of the noise. By "noise" we mean the parts of the image extracted as corresponding to a container upper surface but not actually corresponding to it. For example, noise can be created by objects of the same color as the predefined color but not being a container upper surface.

[0059] To further reduce noise, after combining images 12 and 13 in step 25 to obtain image 14, an optional step 26 may be implemented.

[0060] Step 26, optional, is shown in Figure 5 and preferably comprises three sub-steps 261 to 263. Step 26 is applied to sets of pixels from image I4, i.e. from the combination of images I2 and I3.

[0061] In step 26, a succession of erosions and dilations of the sets of pixels from the combined image are carried out. Preferably, the erosions and dilations carried out are as follows: A first erosion 261, A first dilation 262, A second erosion 263.

[0062] This succession of erosions and expansions makes it possible to eliminate small detections, and thus reduce noise. This improves the detection of upper surfaces.

[0063] Erosion means keeping, for an image, only those pixels for which a chosen pattern called a "kernel" is included in the image. Thus, the image is shrunk.

[0064] Dilation means replacing each pixel with a chosen pattern called a kernel. Thus, the image is enlarged according to the kernel.

[0065] By applying erosions and dilation to each set of pixels from the image I4, we remove sets of pixels that are too small, i.e. those with a size smaller than the predefined upper surface size. Indeed, during the first erosion 261 , all the pixels in a set of pixels may have been removed, for example if the core cannot be included in its entirety in the set of pixels. Then, the resulting sets of pixels are dilated in a sub-step 262, to take a larger size but smaller than their original size. Finally, a second erosion comes to remove the sets of pixels of too small size. Indeed, during the second erosion 261, all the pixels of a set of pixels may have been removed, for example if the kernel cannot be included in its entirety in the set of pixels after dilation.

[0066] This results in a plurality of sets of pixels in the image I5, corresponding to upper surfaces of containers 14. For example, the pixels of the image I5 not belonging to the pixels of the plurality of sets of pixels can be assigned a predetermined value different from the pixels belonging to the pixels of the plurality of sets of pixels, for example the value 0.

[0067] To extract the sets of pixels from the I5 image, any algorithm for extracting sets of similar pixels can be used, for example a so-called "label features" algorithm.

[0068] Finally, the method 20 comprises a final step 27 of calculating the centers of gravity of the sets of pixels of the image I5 to obtain the position of each container 14 in the image acquired in step 21. This calculation step makes it possible to obtain coordinates of the containers 14 in the image. To calculate the center of gravity of the sets of pixels, for each set of pixels, the average of the coordinates of the pixels of the set of pixels makes it possible to obtain the center of gravity of the set of pixels.

[0069] The coordinates thus obtained can then be transformed into coordinates relative to the packaging line 10 to position each container 14 relative to the packaging line 10 and not relative to the camera 11. The coordinates relative to the packaging line 10 can then be used to detect and / or predict the formation of blockages in the packaging line, for example by calculating the speed of the containers 14 and their position from successive images at successive times. For this, the method 20 can be repeated every predefined time, for example every 0.2 seconds with a conveyor speed of 300 millimeters per second for a 60 millimeter bottle.

Claims

CLAIMS

1. A computer-implemented method (20) of detecting the position of a plurality of containers (14), each container (14) comprising an upper surface (141), the method comprising: - Acquisition (21) of at least one image (11) of the plurality of containers (14), - Obtaining (23) local binary patterns (I2) of the image (11) acquired from a dimension of the upper surface (141) of each container (14) of the plurality of containers (14), the dimension of the upper surface (141) of each container (14) being calculated from a predefined upper surface size (141) of the containers (14), - Application (24) of a color mask to the image (11) acquired from a predefined upper surface color (141) of the containers (14) to obtain a masked image (I3), - Combination (25) of the partially masked image (I3) and the local binary patterns (I2) to obtain sets of pixels representing the upper surfaces (141) of the containers (14), - Calculation (27) of centers of gravity of the sets of pixels representing the upper surfaces (141) of the containers (14) of the plurality of containers (14) to obtain the position of each upper surface (141) of the containers (14).

2. Method (20) according to claim 1 according to which the image (11) is obtained by at least one camera (11) whose field of vision (111) is directed towards the plurality of containers (14).

3. Method (20) according to one of the preceding claims further comprising, after the combination (25) of the masked image (I3) and the local binary patterns (I2): - At least one expansion (261, 263) of the sets of pixels representing the upper surfaces (141) of the containers (14), - At least one erosion (262) of the sets of pixels representing the upper surfaces (141) of the containers (14).

4. A method (20) according to claim 3 comprising: - a first expansion (261) of the sets of pixels representing the upper surfaces (141) of the containers (14) then - a first erosion (262) of the sets of pixels representing the upper surfaces (141) of the containers (14) then - a second expansion (263) of the sets of pixels representing the upper surfaces (141) of the containers (14).

5. Method (20) according to one of the preceding claims according to which the position of each upper surface (141) obtained comprises coordinates of the center of gravity of the upper surface (141) in the image.

6. Method (20) according to one of the preceding claims, according to which the combination (25) of the masked image (I3) and the local binary patterns (I2) is carried out by multiplication of a first matrix with a second matrix, the first matrix representing the masked image (I3), the second matrix representing the local binary patterns (I2).

7. Method (20) according to one of the preceding claims according to which the predefined upper surface size (141) and the predefined upper surface color (141) are obtained from a human-machine interface, before obtaining (22) local binary patterns and before applying (23) a color mask to the acquired image (11).

8. Method (20) according to one of the preceding claims, wherein the predefined upper surface size (141) and the predefined upper surface color (14) are stored in a database.

9. Method (20) according to one of the preceding claims, wherein, before calculating the dimension of the upper surface (141), the predefined upper surface size (141) of the container (14) is transformed into the upper surface size (141) of the container (14) in pixels of the image acquired from a predefined distance between the camera (11) and the upper surface of the containers (14) and a predefined resolution of the camera (11) in pixels.

10. Computer program product comprising instructions which, when the program is executed by a computer, cause the latter to implement the method (20) according to one of claims 1 to 8.

11. A computer-readable recording medium comprising instructions which, when executed by a computer, cause the computer to carry out the method (20) according to one of claims 1 to 8.

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