Information processing device, information processing method, and computer program

Abstract

To easily acquire the scattering loss of an object such as a wall.SOLUTION: An information processing device includes an acquisition part for acquiring an image in which the object is captured, an object photographing distance which is a distance from the position of an imaging device capturing the image to the object which is a subject, and the complex dielectric constant of the object, a surface roughness value acquisition part for obtaining a surface roughness value indicating the roughness of the surface of the object from the image, a correlation distance acquisition part for obtaining a correlation distance corresponding to the object photographing distance and the frequency component of the image, and a scattering loss calculation part for calculating the scattering loss of the object by using the surface roughness value, the complex dielectric constant, and the correlation distance.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, an information processing method, and a computer program.

Background Art

[0002] Conventionally, methods for estimating the propagation loss of electromagnetic waves in the 300 GHz band have been studied (see, for example, Non-Patent Documents 1 and 2). In Non-Patent Document 1, scattering of electromagnetic waves in the 300 GHz band by walls is studied. In Non-Patent Document 2, scattering loss by the ER model (scattering loss model) is studied.

Prior Art Documents

Non-Patent Documents

[0003]

Non-Patent Document 1

Non-Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] Electromagnetic waves in the 300GHz band suffer significant losses due to shielding. Therefore, accurately estimating the loss of scattered wave paths due to walls and other structures surrounding the human body is crucial to understanding the area in which communication devices attached to the human body can communicate. While it is preferable to extract surface roughness on the order of the electromagnetic wave wavelength to estimate this scattered wave path loss, the following challenges existed. (1) Surface roughness is generally measured at a few mm / point using a point cloud scanner, so the particle size is insufficient. (2) Measuring surface roughness used in the metals and chemical materials industries is time-consuming because it requires directly applying needles or lasers to the wall.

[0005] This invention was made in consideration of these circumstances, and its purpose is to easily obtain the scattering loss of an object, such as a wall. [Means for solving the problem]

[0006] One aspect of the present invention is an information processing device comprising: an acquisition unit that acquires an image in which an object is depicted, an object shooting distance which is the distance from the position of the imaging device that captured the image to the object which is the subject, and the complex dielectric constant of the object; a surface roughness value acquisition unit that determines a surface roughness value indicating the surface roughness of the object from the image; a correlation distance acquisition unit that determines a correlation distance corresponding to the object shooting distance and the frequency components of the image; and a scattering loss calculation unit that calculates the scattering loss of the object using the surface roughness value, the complex dielectric constant, and the correlation distance. One aspect of the present invention is an information processing device in which the surface roughness value acquisition unit extracts the contours of objects from the image, determines the brightness distribution from the image, and acquires the number of objects with extracted contours and the surface roughness value corresponding to the determined brightness distribution from the relationship between the number of objects with contours, the brightness distribution, and the surface roughness value. One aspect of the present invention is an information processing device in which the correlation distance acquisition unit obtains a power spectrum from the image and obtains a correlation distance corresponding to the object shooting distance from the obtained power spectrum to the object based on the relationship between the power spectrum of the image obtained in advance, the distance from the position of the imaging device that captured the image to the subject, and the correlation distance. One aspect of the present invention is an information processing device in which the scattering loss calculation unit determines, from the surface roughness value, the complex dielectric constant, and the correlation distance, a first parameter used in the scattering loss model that determines the beam width of the scattered wave for each surface element and a second parameter that determines the intensity of the scattered wave for each surface element.

[0007] One aspect of the present invention is an information processing method performed by an information processing device, comprising: an acquisition step of acquiring an image in which an object is depicted, an object shooting distance which is the distance from the position of the imaging device that captured the image to the object which is the subject, and the complex dielectric constant of the object; a surface roughness value acquisition step of determining a surface roughness value indicating the surface roughness of the object from the image; a correlation distance acquisition step of determining a correlation distance corresponding to the object shooting distance and the frequency components of the image; and a scattering loss calculation step of calculating the scattering loss of the object using the surface roughness value, the complex dielectric constant, and the correlation distance.

[0008] One aspect of the present invention is a computer program that causes a computer to perform the following steps: an acquisition step of acquiring an image of an object, an object shooting distance which is the distance from the position of the imaging device that captured the image to the object which is the subject, and the complex dielectric constant of the object; a surface roughness value acquisition step of determining a surface roughness value indicating the surface roughness of the object from the image; a correlation distance acquisition step of determining a correlation distance corresponding to the object shooting distance and the frequency components of the image; and a scattering loss calculation step of calculating the scattering loss of the object using the surface roughness value, the complex dielectric constant, and the correlation distance. [Effects of the Invention]

[0009] According to the present invention, it is possible to easily obtain the scattering loss of an object, such as a wall. [Brief explanation of the drawing]

[0010] [Figure 1] This block diagram shows an example configuration of an information processing device according to one embodiment. [Figure 2] This figure shows an overview of a method for estimating the transmission path of electromagnetic waves according to one embodiment. [Figure 3] This figure shows an example of an information processing method according to one embodiment. [Figure 4] This is a diagram illustrating the surface roughness value according to one embodiment. [Figure 5] This figure illustrates an example of a method for calculating surface roughness value and correlation distance according to one embodiment. [Figure 6] This figure illustrates an example of a method for calculating surface roughness value and correlation distance according to one embodiment. [Figure 7] This figure illustrates an example of a method for calculating surface roughness value and correlation distance according to one embodiment. [Figure 8] This figure illustrates an example of a method for calculating surface roughness value and correlation distance according to one embodiment. [Figure 9] This figure illustrates an example of a method for calculating ER model parameters according to one embodiment. [Figure 10] This figure illustrates an example of a method for calculating ER model parameters according to one embodiment. [Figure 11] This figure illustrates an example of a method for calculating ER model parameters according to one embodiment. [Figure 12] This figure illustrates an example of a method for calculating ER model parameters according to one embodiment. [Modes for carrying out the invention]

[0011] Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 is a block diagram showing a configuration example of an information processing apparatus according to an embodiment. In FIG. 1, the information processing apparatus 1 includes an acquisition unit 11, a surface roughness value acquisition unit 12, a correlation distance acquisition unit 13, a scattering loss calculation unit 14, and a storage unit 20.

[0012] [[ID=​​​​​​​The information processing apparatus 1 outputs the processing result stored in the storage unit 20. For example, the information processing apparatus 1 may display the processing result on a predetermined display device. For example, the information processing apparatus 1 may transmit the processing result to a predetermined destination by communication.

[0015] FIG. 2 is a diagram showing an outline of the electromagnetic wave transmission path estimation method according to the present embodiment. In the example of FIG. 2, when a transmitter Tx (e.g., a smartphone or the like) carried by a person and a receiver Rx (e.g., a wireless headset or the like) worn by the person perform wireless communication using electromagnetic waves in the 300 GHz band, the transmission path of the electromagnetic wave when scattering occurs on the wall surface is estimated.

[0016] As shown in FIG. 2, first, the wireless environment (person, transmitter Tx, receiver Rx, wall surface, etc.) is modeled into a three-dimensional model (3D model). Next, the dominant propagation path between the transmitter Tx and the receiver Rx is identified by the 3D model 3DM. Next, the losses of the identified direct wave path, diffraction wave path, and scattered wave path are estimated. Next, the estimated direct wave path, diffraction wave path, and scattered wave path are synthesized, and the propagation loss between the transmitter Tx and the receiver Rx is calculated by the synthesized transmission path. The present embodiment can be applied to the estimation of the loss of the scattered wave path illustrated in FIG. 2.

[0017] Next, the information processing executed by the information processing apparatus 1 according to the present embodiment will be described in detail.

[0018] FIG. 3 is a diagram showing an example of the information processing method according to the present embodiment. In FIG. 3, two cases are illustrated as objects: (A) a wall with a smooth surface (referred to as wall A) and (B) a wall with a rough surface (referred to as wall B).

[0019] The acquisition unit 11 acquires an image in which wall A is captured (referred to as target image A), the distance from the position of the imaging device that captured target image A to wall A, which is the subject (referred to as object imaging distance A), and the complex dielectric constant of wall A (referred to as complex dielectric constant A). Target image A, object imaging distance A, and complex dielectric constant A are prepared in advance and input to the information processing apparatus 1.

[0020] Similarly, the acquisition unit 11 acquires an image of wall B (referred to as target image B), the distance from the position of the imaging device that captured target image B to the subject wall B (referred to as object shooting distance B), and the complex permittivity of wall B (referred to as complex permittivity B). Target image B, object shooting distance B, and complex permittivity B are prepared in advance and input to the information processing device 1.

[0021] The surface roughness value acquisition unit 12 extracts the contours of objects in the target image A by performing contour extraction image analysis processing on the target image A. As a result, 100 object contours are extracted from the target image A as black dots.

[0022] Similarly, the surface roughness value acquisition unit 12 extracts the contours of objects in the target image B by performing contour extraction image analysis processing on the target image B. As a result, 5,000 object contours are extracted from the target image B as black dots.

[0023] The surface roughness value acquisition unit 12 performs RGB image analysis processing on the target image A to determine the luminance distribution of each RGB component from the target image A. As a result, the width of the luminance distribution of the R component "44", the width of the luminance distribution of the G component "38", and the width of the luminance distribution of the B component "31" are obtained from the target image A. The width of the luminance distribution is a width determined by a predetermined standard, and may, for example, correspond to the standard deviation.

[0024] Similarly, the surface roughness value acquisition unit 12 performs RGB image analysis processing on the target image B to obtain the luminance distribution of each RGB component from the target image B. As a result, the width of the luminance distribution of the R component is obtained from the target image B as "152", the width of the luminance distribution of the G component as "171", and the width of the luminance distribution of the B component as "170".

[0025] The surface roughness value acquisition unit 12 acquires the number of objects in the extracted contours "100" and the surface roughness value "σ_h=aaa" for wall A, corresponding to the calculated luminance distributions "width of R component luminance distribution "44", "width of G component luminance distribution "38", and "width of B component luminance distribution "31", based on the relationship between the number of objects in the contours, the luminance distribution, and the surface roughness value, which has been determined in advance.

[0026] Similarly, the surface roughness value acquisition unit 12 acquires the number of objects in the extracted contours "5000" and the surface roughness value "σ_h=bbb" for wall B, corresponding to the calculated luminance distributions "width of R component luminance distribution "152", "width of G component luminance distribution "171", and "width of B component luminance distribution "170", based on the relationship between the number of objects in the contours, the luminance distribution and the surface roughness value, which has been determined in advance.

[0027] The correlation distance acquisition unit 13 calculates the correlation distance "l=eee" for wall A, corresponding to the object shooting distance A and the frequency components of the target image A.

[0028] Similarly, the correlation distance acquisition unit 13 calculates a correlation distance "l=fff" for wall B, corresponding to the object shooting distance B and the frequency components of the target image B.

[0029] The scattering loss calculation unit 14 uses the surface roughness value "σ_h=aaa", the complex dielectric constant A "ε=ccc", and the correlation distance "l=eee" for wall A to determine the first parameter "α=www" and the second parameter "S=xxx" used in the ER model (scattering loss model). The first parameter α is a parameter that determines the beam width of the scattered wave for each face element (polygon) of the 3D model 3DM. The second parameter S is a parameter that determines the intensity of the scattered wave for each face element (polygon) of the 3D model 3DM.

[0030] Similarly, the scattering loss calculation unit 14 uses the surface roughness value "σ_h=bbb", the complex dielectric constant B "ε=ddd", and the correlation distance "l=fff" for wall B to determine the first parameter "α=yyy" and the second parameter "S=zzz" used in the ER model (scattering loss model).

[0031] The scattering loss calculation unit 14 calculates the scattering loss for wall A using equation (1), with respect to the first parameter "α=www" and the second parameter "S=xxx".

[0032]

number

[0033] Similarly, the scattering loss calculation unit 14 calculates the scattering loss for wall B using equation (1), with the first parameter "α=yyy" and the second parameter "S=zzz".

[0034] Figure 4 is a diagram illustrating the surface roughness value according to this embodiment. In Figure 4, the surface roughness value σ_h is the standard deviation of the height h of the unevenness on the surface of an object such as a wall.

[0035] Figures 5-8 illustrate an example of the method for calculating surface roughness value and correlation distance according to this embodiment. The method for calculating surface roughness value and correlation distance according to this embodiment will be explained with reference to Figures 5-8.

[0036] First, a random rough surface, as illustrated in Figure 5, is generated using three-dimensional computer graphics (3DCG) software. In generating this random rough surface, the surface roughness value σ_h and the correlation distance l are specified. Next, as illustrated in Figure 6, the relationship between the number of contour objects K and the luminance distributions "width R of the R component luminance distribution", "width G of the G component luminance distribution", and "width B of the B component luminance distribution", and the specified surface roughness value σ_h, "σ_h=f(K,R,G,B)", is determined.

[0037] Furthermore, as illustrated in Figure 7, the relationship between the strongest frequency P of the power spectrum in the generated random rough surface, the distance d from the imaging point to the object, and the specified correlation distance l, "l=g(P,d)", is determined. The distance d can be arbitrarily specified.

[0038] The above-mentioned relationship between surface roughness value σ_h, "σ_h=f(K,R,G,B)", and the relationship between correlation distance l, "l=g(P,d)", may be modeled using, for example, machine learning.

[0039] Using the relationship formulas for surface roughness value σ_h, "σ_h=f(K,R,G,B)", and correlation distance l, "l=g(P,d)", obtained above, the actual surface roughness value σ_h and correlation distance l are determined. In Figure 8, the object is wall B. In Figure 8, in step S1, a photograph is taken of wall B as the subject, and the acquisition unit 11 acquires the target image B and the object shooting distance B. Note that the image quality of the target image may be set to an acceptable range of image quality according to the wavelength of the electromagnetic wave that is the target of the scattering loss (for example, wavelength in the 300 GHz band).

[0040] In step S2, the surface roughness value acquisition unit 12 performs contour extraction image analysis processing on the target image B to extract the contours of objects depicted in the target image B. As a result, 5000 object contours are extracted from the target image B as black dots.

[0041] In step S3, the surface roughness value acquisition unit 12 performs RGB image analysis processing on the target image B to obtain the luminance distribution of each RGB component from the target image B. As a result, the width of the luminance distribution of the R component is obtained from the target image B as "152", the width of the luminance distribution of the G component as "171", and the width of the luminance distribution of the B component as "170".

[0042] In step S4, the correlation distance acquisition unit 13 performs spectral analysis on the target image B to obtain a power spectrum from the target image B and find the strongest frequency of the power spectrum, "P=300".

[0043] In step S5, the surface roughness value acquisition unit 12 obtains the surface roughness value σ_h = bbb for wall B, corresponding to the number of objects in the extracted contours "K = 5000" and the calculated luminance distributions "width of the R component luminance distribution "R = 152", "width of the G component luminance distribution "G = 171", and "width of the B component luminance distribution "B = 170"", from the surface roughness value σ_h relation formula "σ_h = f(K, R, G, B)".

[0044] The correlation distance acquisition unit 13 obtains the correlation distance l from the relationship formula "l=g(P,d)" and, for wall B, the frequency with the strongest power spectrum "P=300" and the correlation distance "l=fff" corresponding to the object shooting distance B.

[0045] Figures 9-12 illustrate an example of the method for calculating ER model parameters according to this embodiment. The method for calculating ER model parameters according to this embodiment will be explained with reference to Figures 9-12.

[0046] In this embodiment, the first parameter α and the second parameter S used in the ER model are determined using the surface roughness value σ_h, the correlation distance l, and the complex dielectric constant ε.

[0047] First, a random rough surface is generated using 3DCG software. In this random rough surface generation, the surface roughness value σ_h and the correlation distance l are specified. Next, an electromagnetic field simulation is performed on the generated random rough surface using the FDTD (Finite Difference Time Domain) method, as illustrated in Figure 9. In this electromagnetic field simulation, the complex permittivity ε of the random rough surface is specified. The scattering loss is then calculated from the results of the electromagnetic field simulation.

[0048] Next, as illustrated in Figure 10, the first parameter α and the second parameter S are fitted based on the scattering loss of the electromagnetic field simulation results.

[0049] Next, from the fitting results of the first parameter α and the second parameter S, the relationship equation "α=h(σ_h,l,ε)" between the surface roughness value σ_h, correlation distance l, complex dielectric constant ε and the first parameter α is obtained, as illustrated in Figure 11.

[0050] Furthermore, from the fitting results of the first parameter α and the second parameter S, the relationship equation "S=i(σ_h,l,ε)" between the surface roughness value σ_h, correlation distance l, complex dielectric constant ε and the second parameter S is obtained, as illustrated in Figure 12.

[0051] Using the relationship between the first parameter α, "α=h(σ_h,l,ε)", and the relationship between the second parameter S, "S=i(σ_h,l,ε)", obtained above, we will determine the actual first parameter α and second parameter S.

[0052] The scattering loss calculation unit 14 obtains the first parameter α from the relational expression "α=h(σ_h,l,ε)" for the first parameter α, which corresponds to the surface roughness value σ_h of the object obtained by the surface roughness value acquisition unit 12, the correlation distance l of the object obtained by the correlation distance acquisition unit 13, and the complex dielectric constant ε of the object. The scattering loss calculation unit 14 obtains the second parameter S from the relational expression "S=i(σ_h,l,ε)" for the second parameter S, which corresponds to the surface roughness value σ_h of the object obtained by the surface roughness value acquisition unit 12, the correlation distance l of the object obtained by the correlation distance acquisition unit 13, and the complex dielectric constant ε of the object. The scattering loss calculation unit 14 calculates the scattering loss for the object using the obtained first parameter α and second parameter S according to equation (1) above.

[0053] The scattering loss calculation method described above uses the ER model, but is not limited to this. For example, a scattering loss calculation method that does not use the ER model may also be used.

[0054] As described above, according to this embodiment, the scattering loss of an object can be obtained from an image that shows an object such as a wall, so the scattering loss of an object can be easily obtained.

[0055] Furthermore, this will enable improvements in overall service quality, for example, in wireless communication applications, thereby contributing to Goal 9 of the United Nations-led Sustainable Development Goals (SDGs): "Build resilient infrastructure, promote sustainable industrialization and foster innovation."

[0056] Although embodiments of the present invention have been described in detail above with reference to the drawings, the specific configuration is not limited to these embodiments, and design modifications and the like are also included within the scope of the gist of the present invention.

[0057] Alternatively, a computer program for realizing the functions of the information processing device described above may be recorded on a computer-readable recording medium, and the program recorded on this recording medium may be loaded into a computer system and executed. The term "computer system" here may include hardware such as an operating system and peripheral devices. Furthermore, "computer-readable recording media" refers to writable non-volatile memory such as flexible disks, magneto-optical disks, ROMs, and flash memory, portable media such as DVDs (Digital Versatile Discs), and storage devices such as hard disks built into computer systems.

[0058] Furthermore, "computer-readable recording media" also includes volatile memory (such as DRAM (Dynamic Random Access Memory)) within computer systems that act as servers or clients when programs are transmitted via networks such as the Internet or communication lines such as telephone lines, which retain programs for a certain period of time. Furthermore, the above program may be transmitted from a computer system that stores the program in a memory device or the like to another computer system via a transmission medium or by transmission waves within the transmission medium. Here, the "transmission medium" used to transmit the program refers to a medium that has the function of transmitting information, such as a network (communication network) like the Internet or a communication line (communication line) like a telephone line. Furthermore, the above program may be intended to implement some of the functions described above. It may also be a so-called differential file (differential program) that can implement the aforementioned functions in combination with programs already recorded in the computer system. [Explanation of symbols]

[0059] 1... Information processing device, 11... Acquisition unit, 12... Surface roughness value acquisition unit, 13... Correlation distance acquisition unit, 14... Scattering loss calculation unit, 20... Storage unit

Claims

1. An acquisition unit that acquires an image of the object, the object shooting distance which is the distance from the position of the imaging device that captured the image to the object which is the subject, and the complex dielectric constant of the object. A surface roughness value acquisition unit that obtains a surface roughness value indicating the surface roughness of the object from the aforementioned image, A correlation distance acquisition unit that determines a correlation distance corresponding to the object shooting distance and the frequency components of the image, A scattering loss calculation unit calculates the scattering loss of the object using the surface roughness value, the complex dielectric constant, and the correlation distance. An information processing device equipped with the following features.

2. The surface roughness value acquisition unit is, Extract the outline of the object from the aforementioned image, The brightness distribution is determined from the aforementioned image. Based on the relationship between the number of objects in a pre-determined contour, their brightness distribution, and their surface roughness values, the number of objects in the extracted contour and the surface roughness values ​​corresponding to the determined brightness distribution are obtained. The information processing apparatus according to claim 1.

3. The correlation distance acquisition unit, From the aforementioned image, the power spectrum was obtained. From the relationship between the power spectrum of a pre-determined image, the distance from the position of the imaging device that captured the image to the subject, and the correlation distance, the correlation distance corresponding to the obtained power spectrum and the object's shooting distance is obtained. The information processing apparatus according to claim 1.

4. The scattering loss calculation unit obtains, from the surface roughness value, the complex dielectric constant, and the correlation distance, a first parameter used in the scattering loss model that determines the beam width of the scattered wave for each surface element, and a second parameter that determines the intensity of the scattered wave for each surface element. The information processing apparatus according to claim 1.

5. An information processing method performed by an information processing device, An acquisition step to acquire an image showing the object, the object shooting distance which is the distance from the position of the imaging device that captured the image to the object which is the subject, and the complex permittivity of the object, A surface roughness value acquisition step, which involves obtaining a surface roughness value indicating the surface roughness of the object from the aforementioned image, A correlation distance acquisition step that determines the correlation distance corresponding to the object shooting distance and the frequency components of the image, A scattering loss calculation step in which the scattering loss of the object is calculated using the surface roughness value, the complex dielectric constant, and the correlation distance, Information processing methods including

6. On the computer, An acquisition step to acquire an image showing the object, the object shooting distance which is the distance from the position of the imaging device that captured the image to the object which is the subject, and the complex permittivity of the object, A surface roughness value acquisition step, which involves obtaining a surface roughness value indicating the surface roughness of the object from the aforementioned image, A correlation distance acquisition step that determines the correlation distance corresponding to the object shooting distance and the frequency components of the image, A scattering loss calculation step in which the scattering loss of the object is calculated using the surface roughness value, the complex dielectric constant, and the correlation distance, A computer program designed to execute something.

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