Method, device and electronic device for enhancing image representation

Abstract

The invention provides a method, a device and an electronic device for image representation enhancement. In the method, a learnable Gabor filter is used to extract texture features of an image to be processed. The learnable Gabor filter can realize dynamic adjustment of parameters, so that the model can still effectively extract significant texture features under the conditions of large changes in illumination, texture complexity and scene. In addition, the texture features are residually fused with semantic features, so that the model can not only understand the high-level semantic information of the scene, but also capture fine-grained texture features, thereby significantly improving the performance of the model in multiple tasks. The learnable Gabor filter can realize adaptive adjustment of parameters, avoiding the problem of fixed parameters of traditional Gabor filters, and is suitable for a variety of complex scenes, that is, it realizes full utilization of texture features and realizes the fusion of texture features and semantic features, thereby improving the robustness and recognition ability of the model.

Description

Technical Field

[0001] The present invention relates to the technical field of computer vision, and in particular to a method, device and electronic equipment for enhancing image representation. Background Art

[0002] Existing image feature extraction techniques have made significant progress under the deep learning framework, and convolutional neural networks can learn high-level semantic features from large-scale image data. However, these methods often ignore the texture information in the image, which plays an important role in improving the robustness of the model and capturing the saliency of the scene.

[0003] As a classic texture analysis tool, Gabor filter performs well in capturing edge and texture information. However, the parameters of traditional Gabor filter are fixed and cannot adapt to complex and changing scenes, which limits its application in deep learning frameworks. In addition, the existing image feature extraction technology lacks an effective mechanism to organically combine texture features with semantic features, which limits its performance in changing environments.

[0004] In summary, traditional image representation methods make insufficient use of texture features and lack the fusion of texture and semantic features, which ultimately leads to poor robustness and recognition ability of the model. Summary of the invention

[0005] In view of this, an object of the present invention is to provide a method, device and electronic device for enhancing image representation, so as to alleviate the technical problems that traditional image representation methods do not make sufficient use of texture features and lack the fusion of texture and semantic features.

[0006] In a first aspect, an embodiment of the present invention provides a method for enhancing image representation, comprising:

[0007] Acquire semantic features corresponding to the image to be processed, and use a learnable Gabor filter to perform a convolution operation on the semantic features to obtain texture features;

[0008] Performing residual fusion on the texture feature and the semantic feature to obtain an initial semantic texture feature;

[0009] Performing a convolution operation on the initial semantic texture feature to obtain a semantic texture feature;

[0010] The semantic texture features are optimized to obtain target semantic texture features corresponding to the image to be processed.

[0011] Furthermore, the semantic features corresponding to the image to be processed are obtained, including:

[0012] A pre-trained backbone network is used to extract semantic features from the image to be processed to obtain the semantic features.

[0013] Furthermore, the parameters of the learnable Gabor filter include: wavelength, direction, phase shift, standard deviation and aspect ratio.

[0014] Further, performing a convolution operation on the initial semantic texture feature includes:

[0015] A 1×1 convolutional layer is used to perform a convolution operation on the initial semantic texture feature to obtain the semantic texture feature.

[0016] Furthermore, the semantic texture feature is optimized, including:

[0017] The semantic texture features are normalized and dimensionally reduced to obtain target semantic texture features corresponding to the image to be processed.

[0018] Furthermore, the method further comprises:

[0019] The target semantic texture features are aggregated to obtain a low-dimensional target semantic texture feature descriptor, so as to use the target semantic texture feature descriptor for a visual processing task.

[0020] In a second aspect, an embodiment of the present invention provides a device for enhancing image representation, including:

[0021] An acquisition and convolution unit, used for acquiring semantic features corresponding to the image to be processed, and performing a convolution operation on the semantic features using a learnable Gabor filter to obtain texture features;

[0022] A residual fusion unit, used for performing residual fusion on the texture feature and the semantic feature to obtain an initial semantic texture feature;

[0023] A convolution operation unit, used for performing a convolution operation on the initial semantic texture feature to obtain a semantic texture feature;

[0024] The optimization processing unit is used to optimize the semantic texture features to obtain target semantic texture features corresponding to the image to be processed.

[0025] Furthermore, the acquisition and convolution unit is also used for:

[0026] A pre-trained backbone network is used to extract semantic features from the image to be processed to obtain the semantic features.

[0027] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0028] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to execute any method described in the first aspect above.

[0029] In an embodiment of the present invention, a method for image representation enhancement is provided, comprising: obtaining semantic features corresponding to an image to be processed, and performing a convolution operation on the semantic features using a learnable Gabor filter to obtain a texture feature; performing residual fusion of the texture feature and the semantic feature to obtain an initial semantic texture feature; performing a convolution operation on the initial semantic texture feature to obtain a semantic texture feature; and optimizing the semantic texture feature to obtain a target semantic texture feature corresponding to the image to be processed. From the above description, it can be seen that in the method for image representation enhancement of the present invention, a learnable Gabor filter is used to extract texture features of the image to be processed. The learnable Gabor filter can realize dynamic adjustment of parameters, so that the model can still effectively extract significant texture features under the conditions of large changes in illumination, texture complexity and scene. In addition, the texture features are residually fused with semantic features, so that the model can not only understand the high-level semantic information of the scene, but also capture fine-grained texture features, thereby significantly improving the performance of the model in multiple tasks. The learnable Gabor filter can realize adaptive adjustment of parameters, avoiding the problem of fixed parameters of traditional Gabor filters, and is suitable for a variety of complex scenes, that is, it realizes full utilization of texture features and realizes the fusion of texture features and semantic features, thereby improving the robustness and recognition ability of the model, and alleviating the technical problems of insufficient utilization of texture features and lack of fusion of texture and semantic features in traditional image representation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 A flowchart of a method for enhancing image representation provided by an embodiment of the present invention;

[0032] Figure 2 A schematic diagram of image representation enhancement provided by an embodiment of the present invention;

[0033] Figure 3 A schematic diagram of a device for enhancing image representation provided by an embodiment of the present invention;

[0034] Figure 4 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Traditional image representation methods make insufficient use of texture features and lack the fusion of texture and semantic features.

[0037] Based on this, in the image representation enhancement method of the present invention, a learnable Gabor filter is used to extract texture features of the image to be processed. The learnable Gabor filter can realize dynamic adjustment of parameters, so that the model can still effectively extract significant texture features under the conditions of large changes in illumination, texture complexity and scene. In addition, the texture features are residually fused with semantic features, so that the model can not only understand the high-level semantic information of the scene, but also capture fine-grained texture features, thereby significantly improving the performance of the model in multiple tasks. The learnable Gabor filter can realize adaptive adjustment of parameters, avoiding the problem of fixed parameters of traditional Gabor filters, and is suitable for a variety of complex scenes, that is, it realizes full utilization of texture features and realizes the fusion of texture features and semantic features, thereby improving the robustness and recognition ability of the model.

[0038] To facilitate understanding of this embodiment, a method for enhancing image representation disclosed in an embodiment of the present invention is first introduced in detail.

[0039] Embodiment 1:

[0040] According to an embodiment of the present invention, an embodiment of a method for enhancing image representation is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0041] Figure 1is a flow chart of a method for enhancing image representation according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0042] Step S102, obtaining semantic features corresponding to the image to be processed, and performing a convolution operation on the semantic features using a learnable Gabor filter to obtain texture features;

[0043] Specifically, the above texture features are multi-directional and multi-frequency.

[0044] Step S104, performing residual fusion on the texture feature and the semantic feature to obtain an initial semantic texture feature;

[0045] refer to Figure 2 , where the process corresponding to the plus sign is the residual fusion process.

[0046] Step S106, performing a convolution operation on the initial semantic texture feature to obtain a semantic texture feature;

[0047] Specifically, a lightweight convolution operation is used to further convolve the initial semantic texture features to improve the fusion efficiency and representation ability.

[0048] Step S108, optimizing the semantic texture features to obtain target semantic texture features corresponding to the image to be processed.

[0049] In an embodiment of the present invention, a method for image representation enhancement is provided, comprising: obtaining semantic features corresponding to an image to be processed, and performing a convolution operation on the semantic features using a learnable Gabor filter to obtain a texture feature; performing residual fusion of the texture feature and the semantic feature to obtain an initial semantic texture feature; performing a convolution operation on the initial semantic texture feature to obtain a semantic texture feature; and optimizing the semantic texture feature to obtain a target semantic texture feature corresponding to the image to be processed. From the above description, it can be seen that in the method for image representation enhancement of the present invention, a learnable Gabor filter is used to extract texture features of the image to be processed. The learnable Gabor filter can realize dynamic adjustment of parameters, so that the model can still effectively extract significant texture features under the conditions of large changes in illumination, texture complexity and scene. In addition, the texture features are residually fused with semantic features, so that the model can not only understand the high-level semantic information of the scene, but also capture fine-grained texture features, thereby significantly improving the performance of the model in multiple tasks. The learnable Gabor filter can realize adaptive adjustment of parameters, avoiding the problem of fixed parameters of traditional Gabor filters, and is suitable for a variety of complex scenes, that is, it realizes full utilization of texture features and realizes the fusion of texture features and semantic features, thereby improving the robustness and recognition ability of the model, and alleviating the technical problems of insufficient utilization of texture features and lack of fusion of texture and semantic features in traditional image representation methods.

[0050] The above content briefly introduces the method for enhancing image representation of the present invention. The specific contents involved therein are described in detail below.

[0051] In an optional embodiment of the present invention, obtaining semantic features corresponding to the image to be processed specifically includes the following steps:

[0052] A pre-trained backbone network is used to extract semantic features from the image to be processed to obtain semantic features.

[0053] In an optional embodiment of the present invention, the parameters of the learnable Gabor filter include: wavelength, direction, phase shift, standard deviation and aspect ratio.

[0054] Specifically, the above parameters are dynamically optimized through model training so that they can adapt to the specific texture requirements of different scenes.

[0055] In an optional embodiment of the present invention, a convolution operation is performed on the initial semantic texture feature, specifically comprising the following steps:

[0056] refer to Figure 2 , a 1×1 convolutional layer is used to perform convolution operation on the initial semantic texture features to obtain semantic texture features.

[0057] In an optional embodiment of the present invention, the semantic texture feature is optimized, specifically comprising the following steps:

[0058] The semantic texture features are normalized and dimensionally reduced to obtain the target semantic texture features corresponding to the image to be processed.

[0059] Specifically, the normalization layer is used to normalize the semantic texture features ( Figure 2 The normalization and dimensionality reduction process is not shown in the figure) to ensure the stability of the model training, and then the dimensionality reduction process is performed to improve the computational efficiency.

[0060] In an optional embodiment of the present invention, the method further comprises the following steps:

[0061] The target semantic texture features are aggregated to obtain a low-dimensional target semantic texture feature descriptor, so that the target semantic texture feature descriptor can be used for visual processing tasks.

[0062] Specifically, the above-mentioned target semantic texture features are high-dimensional features, which are aggregated to obtain low-dimensional target semantic texture feature descriptors, which can be used for subsequent image classification, target detection, visual positioning and recognition tasks.

[0063] Compared with traditional methods, the present invention has the following significant advantages in image representation:

[0064] 1. Enhanced robustness:

[0065] The learnable Gabor filter is used to dynamically adjust parameters, so that the model can still effectively extract significant texture features when the lighting, texture complexity and scene changes are large.

[0066] 2. Improved characterization capabilities:

[0067] Combining texture features with semantic features enables the model to not only understand the high-level semantic information of the scene, but also capture fine-grained texture features, thereby significantly improving the performance of the model in multiple tasks (e.g., classification and recognition).

[0068] 3. Strong adaptability:

[0069] The parameters of the learnable Gabor filter can be adaptively adjusted according to the training data, avoiding the problem of fixed parameters of traditional Gabor filters and is suitable for a variety of complex scenarios.

[0070] Embodiment 2:

[0071] The embodiment of the present invention further provides a device for enhancing image representation, which is mainly used to execute the method for enhancing image representation provided in the first embodiment of the present invention. The device for enhancing image representation provided in the embodiment of the present invention is specifically introduced below.

[0072] Figure 3 is a schematic diagram of a device for enhancing image representation according to an embodiment of the present invention, such as Figure 3 As shown, the device mainly includes: an acquisition and convolution unit 10, a residual fusion unit 20, a convolution operation unit 30, and an optimization processing unit 40, wherein:

[0073] An acquisition and convolution unit is used to acquire semantic features corresponding to the image to be processed, and to perform convolution operation on the semantic features using a learnable Gabor filter to obtain texture features;

[0074] A residual fusion unit is used to perform residual fusion of texture features and semantic features to obtain initial semantic texture features;

[0075] A convolution operation unit, used for performing a convolution operation on the initial semantic texture feature to obtain a semantic texture feature;

[0076] The optimization processing unit is used to optimize the semantic texture features to obtain the target semantic texture features corresponding to the image to be processed.

[0077] In an embodiment of the present invention, a scene representation device based on multi-scale regional feature fusion is provided, comprising: using a pre-trained backbone network to extract features from an original scene image to obtain a feature map, and dividing the feature map into multiple sub-regions according to a preset division scale to obtain multiple sub-region feature maps; using a feature mixing module corresponding to each sub-region feature map to extract the saliency information of each sub-region feature map, and performing dimensionality reduction aggregation on the saliency information of each sub-region feature map to obtain a region-level feature descriptor corresponding to each sub-region feature map; using a multi-head attention mechanism to dynamically weighted reorganize the region-level feature descriptors to obtain dynamically weighted reorganized region-level feature descriptors corresponding to each sub-region feature map; optimizing and reducing the dimensionality of the dynamically weighted reorganized region-level feature descriptors to obtain a low-dimensional scene feature representation vector. From the above description, it can be seen that in the scene representation device based on multi-scale regional feature fusion of the present invention, multi-scale region division and feature mixing effectively capture the local salient information of the original scene image and improve the network's adaptability to complex scenes; the multi-head attention mechanism significantly improves the modeling ability of contextual relationships between regions, enabling the network to focus on salient areas related to scene recognition, that is, it can capture local salient areas and multi-scale features, thereby improving the adaptability and recognition performance of the scene recognition network in complex environments, and alleviating the technical problem of insufficient capture of local salient areas and multi-scale features by traditional scene representation methods.

[0078] Optionally, the acquisition and convolution unit is further used to: use a pre-trained backbone network to extract semantic features from the image to be processed to obtain semantic features.

[0079] Optionally, the parameters of the learnable Gabor filter include: wavelength, direction, phase shift, standard deviation and aspect ratio.

[0080] Optionally, the convolution operation unit is further used to: use a 1×1 convolution layer to perform a convolution operation on the initial semantic texture feature to obtain the semantic texture feature.

[0081] Optionally, the optimization processing unit is further used to: perform normalization and dimensionality reduction processing on the semantic texture features to obtain target semantic texture features corresponding to the image to be processed.

[0082] Optionally, the device is also used to: aggregate the target semantic texture features to obtain a low-dimensional target semantic texture feature descriptor, so as to use the target semantic texture feature descriptor for a visual processing task.

[0083] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0084] like Figure 4As shown, an electronic device 600 provided in an embodiment of the present application includes: a processor 601, a memory 602 and a bus, wherein the memory 602 stores machine-readable instructions executable by the processor 601. When the electronic device is running, the processor 601 communicates with the memory 602 through the bus, and the processor 601 executes the machine-readable instructions to perform the steps of the method for image representation enhancement as described above.

[0085] Specifically, the memory 602 and the processor 601 can be general-purpose memories and processors, which are not specifically limited here. When the processor 601 runs the computer program stored in the memory 602, the image representation enhancement method can be executed.

[0086] The processor 601 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 601. The above processor 601 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 602, and the processor 601 reads the information in the memory 602 and completes the steps of the above method in combination with its hardware.

[0087] Corresponding to the above-mentioned method for enhancing image representation, an embodiment of the present application also provides a computer-readable storage medium, which stores machine-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to execute the steps of the above-mentioned method for enhancing image representation.

[0088] The device for image characterization enhancement provided in the embodiments of the present application can be specific hardware on the device or software or firmware installed on the device. The device provided in the embodiments of the present application has the same implementation principle and technical effects as the aforementioned method embodiments. For the sake of brief description, the parts not mentioned in the device embodiment can refer to the corresponding contents in the aforementioned method embodiments. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0089] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0090] For another example, the flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the device, method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of the boxes in the block diagram and / or the flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0091] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0092] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0093] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can essentially or partly be embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including a number of instructions for enabling an electronic device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method for image representation enhancement described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0094] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0095] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solution of the present application, rather than to limit it. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the aforementioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solution recorded in the aforementioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solution deviate from the scope of the technical solution of the embodiment of the present application. They should all be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for enhancing image representation, characterized in that: include: Acquire semantic features corresponding to the image to be processed, and use a learnable Gabor filter to perform a convolution operation on the semantic features to obtain texture features; Performing residual fusion on the texture feature and the semantic feature to obtain an initial semantic texture feature; Performing a convolution operation on the initial semantic texture feature to obtain a semantic texture feature; The semantic texture features are optimized to obtain target semantic texture features corresponding to the image to be processed.

2. The method according to claim 1, characterized in that Obtain the semantic features corresponding to the image to be processed, including: A pre-trained backbone network is used to extract semantic features from the image to be processed to obtain the semantic features.

3. The method according to claim 1, characterized in that The parameters of the learnable Gabor filter include: wavelength, direction, phase shift, standard deviation and aspect ratio.

4. The method according to claim 1, characterized in that: Performing a convolution operation on the initial semantic texture feature includes: A 1×1 convolutional layer is used to perform a convolution operation on the initial semantic texture feature to obtain the semantic texture feature.

5. The method according to claim 1, characterized in that The semantic texture feature is optimized, including: The semantic texture features are normalized and dimensionally reduced to obtain target semantic texture features corresponding to the image to be processed.

6. The method according to claim 1, characterized in that The method further comprises: The target semantic texture features are aggregated to obtain a low-dimensional target semantic texture feature descriptor, so as to use the target semantic texture feature descriptor for a visual processing task.

7. A device for enhancing image representation, characterized in that: include: An acquisition and convolution unit, used for acquiring semantic features corresponding to the image to be processed, and performing a convolution operation on the semantic features using a learnable Gabor filter to obtain texture features; A residual fusion unit, used for performing residual fusion on the texture feature and the semantic feature to obtain an initial semantic texture feature; A convolution operation unit, used for performing a convolution operation on the initial semantic texture feature to obtain a semantic texture feature; The optimization processing unit is used to optimize the semantic texture features to obtain target semantic texture features corresponding to the image to be processed.

8. The device according to claim 7, characterized in that The acquisition and convolution unit is also used for: A pre-trained backbone network is used to extract semantic features from the image to be processed to obtain the semantic features.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions cause the processor to execute the method according to any one of claims 1 to 6.

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