KR101845972B1 - Measurement method and system of visual fatigue level based depth image and bio signal - Google Patents

Abstract

The present invention relates to a method for measuring a 3D image expected visual fatigue level based on a depth image and a bio-signal considering a user and a system thereof. The method comprises the steps of: (a) obtaining an image expected visual fatigue level parameter associated with an image feature from a 3D image; (b) obtaining a bio-signal obtained visual fatigue level parameter appearing from a bio-signal of a viewer while watching a 3D image; and (c) generating weights by using a fuzzy function generated from the obtained image expected visual fatigue level parameter and the obtained bio-signal visual fatigue level parameter, and selecting one of the generated weights to calculate a user′s visual fatigue level.

Description

TECHNICAL FIELD [0001] The present invention relates to a three-dimensional image visual fatigue measurement method and system based on a user-considered depth image and a bio-

The present invention relates to a method and system for measuring three-dimensional visual fatigue, and more particularly, to a method and system for measuring visual fatigue in real time by analyzing bio-signals varying between a 3D image feature and a user's 3D image viewing .

3D image is a method of imitating the principle that human beings visually feel and implements. The human vision perceives image information with two eyes and acquires the visual information in a way that the brain perceives it comprehensively. At this point, there is an important biologic characteristic that both eyes are about 6.5 cm apart. Therefore, when viewing a particular object, both eyes produce different images due to differences in angle of view. This is called binocular disparity. When two images with a visual difference are synthesized, a stereoscopic effect occurs, and in the process of synthesizing the stereoscopic effect, the image information capable of spatial perception is recognized.

FIG. 1 is a schematic diagram showing the principle of generation of visual fatigue caused by viewing of a 3D image. When viewing a 3D image created in this manner, the distance of the actual image and the convergence distance due to the 3D effect do not coincide with each other as shown in FIG. 1, thereby causing visual fatigue. As a method for measuring the visual fatigue generated at this time, it is possible to use the image feature information or to measure the change in the biological signal of the viewer.

First, the method of measuring visual fatigue using image feature information can be obtained by using image features such as depth of 3D image, AC conflict, depth generation position, and average motion speed. Such a method can predict the fatigue in advance before the viewer views the video. Also, stability is high because the root cause of fatigue is fatigue using images. However, it does not take into account the user's external factors or the user's condition, so it can not be seen as a user's custom fatigue measurement.

 Next, the viewer can use the changing parameters by measuring the bio-signals (brain wave, pulse wave, electrocardiogram, EMG, etc.) between the 3D image viewing. The biological signal parameters are changed including everything the viewer feels. In other words, factors such as external environment other than video viewing are included in the bio-signal parameter. However, since all of the 3D image viewing contents are included, the data (data) is unstable as the visual fatigue parameter.

Korean Registered Patent No. 10-1595546 (Registration date: February 12, 2016) Korean Registered Patent No. 10-1356427 (Registered Date: January 22, 2014)

The method and system for measuring three-dimensional visual fatigue based on a depth image and a bio-signal considering a user according to the present invention have the following problems.

First, the present invention is to provide a 3D image visual fatigue measurement method and system that can compensate for the disadvantages of the prior art by using image information and biological signals, and can measure the visual fatigue of each user in real time.

It is another object of the present invention to provide a 3D image visual fatigue measurement method and system capable of accurately measuring the visual fatigue experienced by a user and adjusting the depth image to provide an image with low visual fatigue.

The present invention has been made in view of the above problems, and it is an object of the present invention to provide an apparatus and method for controlling the same.

According to a first aspect of the present invention, there is provided a 3D image visual fatigue measurement method based on a user-considered depth image and a bio-signal, the method comprising the steps of: (a) determining an image visual fatigue Obtaining a Fatigue level parameter; (b) obtaining an Obtained Visual Fatigue level parameter appearing from a bio-signal of a viewer while viewing the 3D image; And (c) generating a weight by using a fuzzy function generated from the acquired image-visual-fatigue parameter and the biometric-visual-fatigue parameter, and selecting one of the generated weights to calculate a user's visual-fatigue do.

Here, the step (a) is a step of calculating and acquiring an image time fatigue parameter using an accommodation distance and an AC conflict between the convergence distance generated from watching the 3D image desirable.

Further, the equation for calculating the image-visual-fatigue parameter is:

(Where a is the view-point angle of the screen to be displayed and beta is the view-point angle in the negative region) .

Preferably, in the step (b), the bio-signal is a pulse wave signal of a viewer while viewing a 3D image, and the step (b) includes the steps of: (b1) Obtaining PPG data by measurement; (b2) obtaining a plurality of parameters of the obtained PPG data according to an analysis method in a time domain and a frequency domain; And (b3) selecting any one of the acquired parameters and selecting the obtained parameter as an obtained visual fatigue parameter.

In addition, the step (c) may further include the steps of: (c1) generating a fuzzy rule table determined by a relationship between input and output using the image visual fatigue parameter and the biometric fatigue parameter; (c2) calculating a fuzzy output minimum value and a fuzzy output maximum value using a fuzzy membership function generated based on the generated fuzzy rule table; (c3) calculating a weight by using a defuzzification process on the calculated fuzzy output minimum value and the calculated maximum fuzzy output value; And (c4) selecting one of the generated weights to calculate a fusion based visual fatigue level.

In addition, the equation for calculating the weight is:

(w _EVF is weight related to image visual fatigue, w _OVF is weight related to biometric fatigue, w _i is weight obtained by purging, and W _i is normalized value of w _i ) and,

The equation for calculating the fusion-based visual fatigue level (FVFL)

(Where EVF denotes an image visual fatigue parameter and OVF denotes a biometric visual fatigue parameter).

According to a second aspect of the present invention, there is provided a 3D image visual fatigue measurement system based on a depth image and a bio-signal considered by a user, comprising: a 3D image device for displaying a 3D image; A bio-signal measuring device for measuring a bio-signal of a viewer while a viewer watches a 3D image displayed from the 3D image device at a certain distance; Acquiring an Expected Visual Fatigue parameter associated with the image feature from the 3D image and connecting the 3D image device to the 3D image device, and displaying the bio-visual fatigue A visual fatigue parameter and a biometric fatigue parameter are obtained, a weight is generated by using a fuzzy function, and one of the generated weights is selected to calculate a user's visual fatigue And a visual fatigue calculating unit.

Preferably, the bio-electrical signal is a pulse wave signal of a viewer while viewing a 3D image, and the visual fatigue calculator calculates a difference between an accommodation distance and a convergence distance generated from viewing the 3D image, It is preferable to calculate and obtain the image visual fatigue parameter using the AC conflict.

Further, the equation for calculating the image-visual-fatigue parameter is:

(Where a is the view-point angle of the screen to be displayed and beta is the view-point angle in the negative region) .

In addition, the visual fatigue calculating unit measures the pulse wave of the applicant while viewing the 3D image to compile the PPG data, acquires a plurality of parameters in the time domain and the frequency domain according to the analysis method, It is preferable to select any one of the acquired parameters and select the parameter as an Obtained Visual Fatigue parameter.

The visual fatigue calculating unit may be configured to generate a fuzzy rule table determined based on a relationship between input and output using the image visual fatigue parameter and the biometric fatigue parameter and generate a fuzzy membership function generated based on the generated fuzzy rule table a fuzzy membership function is used to calculate the fuzzy output minimum value and the maximum fuzzy output value, and the calculated fuzzy output minimum value and the fuzzy output maximum value are calculated using a defuzzification process, It is preferable to select one of them to calculate the fusion based visual fatigue level.

The equation for calculating the weights,

(w _EVF is weight related to image visual fatigue, w _OVF is weight related to biometric fatigue, w _i is weight obtained by purging, and W _i is normalized value of w _i ) and,

The equation for calculating the fusion-based visual fatigue level (FVFL)

(Where EVF denotes an image visual fatigue parameter and OVF denotes a biometric visual fatigue parameter).

A third aspect of the present invention is to provide a computer program stored in a computer-readable recording medium in combination with hardware in order to execute the above-described three-dimensional image visual fatigue measurement method based on a depth image and a bio- .

The 3D image visual fatigue measurement method and system based on the depth image and the bio-signal considering the user according to the present invention have the following effects.

First, the present invention provides a 3D image visual fatigue measurement method and system capable of measuring a more accurate user fatigue in real time by using image information and biological signals.

Secondly, according to the present invention, it is possible to send a danger signal to a user when a fatigue is large when 3D image is viewed through more precise visual fatigue measurement, thereby inducing a rest. When the user's visual fatigue is high, A 3D image visual fatigue measurement method and system capable of viewing 3D images comfortably by reproducing images with low fatigue are provided.

The effects of the present invention are not limited to those mentioned above, and other solutions not mentioned can be clearly understood by those skilled in the art from the following description.

FIG. 1 is a schematic diagram showing the principle of generation of visual fatigue caused by viewing of a 3D image.
FIG. 2 is a flow chart illustrating a method of measuring a 3D image visual fatigue based on a depth image and a bio-signal according to an embodiment of the present invention.
FIG. 3 is a diagram illustrating a configuration of a 3D image visual fatigue measurement system based on a user-considered depth image and a bio-signal according to an embodiment of the present invention.
4 is a schematic diagram of a process for deriving a weight from image and biometric fatigue parameters by applying fuzzy theory.
5 is a schematic diagram illustrating a process of measuring a 3D image visual fatigue based on a depth image and a bio-signal in consideration of a more specific user according to an embodiment of the present invention.
FIG. 6 is a schematic diagram showing a principle of a method for obtaining an AC conflict.
7 is a diagram illustrating a fuzzy rule table using image and biometric fatigue parameter.
Figure 8 is an illustration of a triangle membership function.
FIG. 9 is a table showing a table of an output value for the minimum (min) and an output value for the maximum (max).
FIG. 10 is a diagram illustrating defuzzification. Referring to FIG.
11 is a graph showing the results obtained by linear regression analysis of EVF and SRVF.
12 is a graph showing the results obtained by linear regression analysis of SRVF and OVF.
Fig. 13 is a table showing the results of using EVF alone and OVF as a result of using only EVF and OVF in relation to SRVF.
14 is a graph showing a result of FVFL obtained by applying a weight obtained by using EVF and OVF using fuzzy theory.

Further objects, features and advantages of the present invention will become more apparent from the following detailed description and the accompanying drawings.

Before describing the present invention in detail, it is to be understood that the present invention is capable of various modifications and various embodiments, and the examples described below and illustrated in the drawings are intended to limit the invention to specific embodiments It is to be understood that the invention includes all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.

It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, the terms "comprises" or "having" and the like refer to the presence of stated features, integers, steps, operations, elements, components, or combinations thereof, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.

Further, terms such as " part, "" unit," " module, "and the like described in the specification may mean a unit for processing at least one function or operation.

In the following description of the present invention with reference to the accompanying drawings, the same components are denoted by the same reference numerals regardless of the reference numerals, and redundant explanations thereof will be omitted. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings.

FIG. 2 is a flowchart illustrating a method of measuring a 3D image visual fatigue based on a depth image and a bio-signal according to an exemplary embodiment of the present invention. FIG. Dimensional visual fatigue measurement system based on an image and a bio-signal.

As shown in FIG. 2, a depth image and a 3D image visual fatigue measurement method based on a user-considered depth image and a bio-signal according to an exemplary embodiment of the present invention include (a) an image visual fatigue Obtaining a Fatigue level parameter; (b) obtaining an Obtained Visual Fatigue level parameter appearing from a bio-signal of a viewer while viewing the 3D image; And (c) generating a weight by using a fuzzy function generated from the acquired image-visual-fatigue parameter and the biometric-visual-fatigue parameter, and selecting one of the generated weights to calculate a user's visual-fatigue .

As shown in FIG. 3, the system for measuring three-dimensional image visual fatigue based on a depth image and a bio-signal according to an embodiment of the present invention includes a 3D image apparatus 100 for displaying a 3D image; A bio-signal measuring device 200 for measuring a bio-signal of a viewer while a viewer watches a 3D image displayed from the 3D image device 100 at a certain distance; And acquiring an Expected Visual Fatigue parameter associated with the image feature from the 3D image and connecting the 3D image device 100 to the 3D image device 100. While viewing the 3D image, Acquiring an Obtained Visual Fatigue parameter appearing from a bio-signal, generating a weight by using a fuzzy function of the acquired image-visual-fatigue parameter and the biometric-visual-fatigue parameter, and calculating one of the generated weights And a visual fatigue calculating unit 300 for calculating the user's visual fatigue.

Here, the 3D image device 100 is a 3D image display device such as a 3D TV, a 3D screen, and the like, which can implement a 3D image. The bio-signal measuring device 200 is a device for displaying a 3D image, It is a device that can measure biological signals.

In the embodiment of the present invention, various bio-signals such as brain waves, pulse waves, ECG electromyograms and the like can be measured and used as visual fatigue calculation data. In the following description, pulse-wave signals (PPG: Photo-plethysmography), which can be easily measured by various mobile devices or wearable devices, will be described.

The visual fatigue calculating unit 300 is a computing device that can calculate the visual fatigue using the parameters using the features of the image acquired from the 3D imaging apparatus 100 and the parameters based on the measured bio-signals. Of course, at least one of the visual fatigue calculator 300 can use the computing device.

As described above, the embodiment of the present invention relates to a real-time visual fatigue measurement method and system considering a user when viewing a 3D image. In order to measure visual fatigue, 3D visual feature information and a biological signal (pulse wave signal: PPG), and by using the image information and the bio-signal in combination, it is possible to improve the disadvantages of the related art and provide a method and apparatus for measuring the visual fatigue of the user in real time.

In addition, when fatigue occurs in the 3D image viewing, a risk signal is sent to the user to induce a rest, and when the user's visual fatigue is high, the depth of the 3D image is reduced to reproduce the image with relatively low visual fatigue, In particular, the advantage of being able to easily obtain data by smart watch or smart band compared with other bio-signals (electroencephalogram, electrocardiogram, EMG) in the case of pulse wave signal among biological signals for visual fatigue measurement have.

Hereinafter, the process for each step will be described in detail with reference to the drawings.

First, we use the change of the viewer's pulse while viewing the image information feature and the 3D image for the visual fatigue. Here, the Expected Visual Fatigue Level value expected in the 3D image is a fatigue prediction value obtained using the feature of the image. That is, parameters that can be obtained by image features such as image depth, viewing angle, and motion speed can be used as parameters.

The biometric fatigue parameter is a parameter of the biometric signal (pulse wave signal (PPG)) that the user changes through watching the 3D image. PPG (Photo-plethysmography) is a method of analyzing the activity of the heart using optical devices. It analyzes the minute changes of the heartbeat cycle.

This analysis has various parameters according to time domain analysis and frequency domain analysis. The parameters in the time domain are Min (minimum value of heart rate interval), Max (maximum value of heart rate interval), Mean (average of heart rate interval), meanHR (average of heart rate), sdHR (Heart rate variance), and frequency domain analysis can be expressed as VFL (0.003-0.04Hz), LF (0.04-0.15Hz), and HF (0.15-0.4Hz) depending on the frequency domain. Other parameters can be combined.

Parameters Represent time
domain Min, Max, Mean, meanHR , sdHR Speed of heart rate SDNN External environment adaptability (max-min) / mean, (max-min) / SDNN , RMSSD / mean, RMSSD / SDNN , Combining to distinguish visual fatigue level frequency
domain VLF, LF Sympathetic nerve HF Parasympathetic nerve VLF / HF, LF / HF Balance of VLF, LF and HF

The image visual fatigue parameter and the biometric visual fatigue parameter are obtained through the fuzzy theory. 4 is a schematic diagram of a process for deriving a weight from image and biometric fatigue parameters by applying fuzzy theory.

The obtained image and biometric fatigue parameters are combined by applying the fuzzy theory according to the order shown in FIG. First, a fuzzy rule table is created, and a fuzzy rule table is described according to the characteristics of the input data set and is determined by the relationship between the input and the output.

When a fuzzy table is created, a fuzzy membership function is used to obtain a fuzzy output. The fuzzy output is obtained by applying a min rule and a max rule to a fuzzy rule table. The weight value is obtained by defuzzification of the obtained output minimum value and output maximum value.

The obtained image visual fatigue (EVF) weight and the biometric fatigue (OVF) weight are normalized to obtain w _i . The image visual fatigue (EVF) is an image characteristic parameter and the biological visual fatigue (OVF) is a bio-signal or pulse wave signal (PPG) analysis parameter. Then, the fatigue felt by the user is estimated or calculated by finally obtaining the fusion-based visual fatigue level (FVFL).

Hereinafter, a process of a 3D image visual fatigue measurement method based on a user-considered depth image and a biological signal according to an embodiment of the present invention will be described in detail.

In the embodiment of the present invention, it is a method for real-time measurement of fatigue that occurs during viewing of a 3D image by a user. We measured the user 's pulse wave between 3D image information and image viewing for fatigue measurement. Visual fatigue is predicted by combining the fatigue characteristics of the 3D image with the measured bio-signals. Fuzzy theory is used as a means of combining data. 5 is a schematic diagram illustrating a process of measuring a 3D image visual fatigue based on a depth image and a bio-signal in consideration of a more specific user according to an embodiment of the present invention. That is, the fuzzy based visual fatigue level (FVFL) is finally obtained according to the procedure shown in FIG. 5 to model the visual fatigue of the user.

First, AC conflicts were obtained from various methods of obtaining EVF using 3D image features. FIG. 6 shows a method for obtaining an AC conflict. The AC conflict is the difference between the accommodation distance and the convergence distance. This can be obtained from the Disparity of View-Point Angles (DVA).

In this case, α is the view-point angle of the bottom surface to be displayed and β is the view-point angle in the negative region. DVA is expressed by the following equation (1).

이 필요하다. 다음으로 실제 시역 거리(Viewing distance)는

와 양과 음의 viewing distance 인

와

의 관계를 이용하여 다음의 [수학식 2]와 같이 구할 수 있다.In order to obtain the view-point angle, the difference (Disparity) between the left and right images and the distance between both eyes

Is required. Next, the actual viewing distance is

And the viewing distance of positive and negative

Wow

The following equation (2) can be obtained.

Then, ACc can be obtained from the following equation (3). The obtained ACc is used as an EVF parameter.

Next, the PPG data is acquired through the pulse wave measurement of the user or the viewer during the 3D image viewing. The obtained data can be obtained various parameters according to the analysis method of time domain and frequency domain. Select at least one of several parameters and select OVF.

And a fuzzy rule table is created as shown in FIG. 7 by selecting data of at least two according to the obtained EVF and OVF. The fuzzy rule table may vary depending on the characteristics of the data. 7 is a diagram illustrating a fuzzy rule table using image and biometric fatigue parameter.

Next, EVF and OVF are substituted into the membership function to obtain output values at L, M, and H. Figure 8 is an illustration of a triangle membership function. The output value obtained by the membership function is obtained by applying the minimum and maximum rules to the generated fuzzy rule table and outputting the output value for the minimum (min) and the output value for the maximum (max) Can be obtained.

When the output value for the minimum (min) and the output value for the maximum (max) are obtained, one of min (max) and max (max) values is selected and substituted in the membership function to reverse fuzzy defuzzification. Figure 10 is an illustration of defuzzification.

The value calculated through this process becomes a weight candidate value. The weighted candidate values are determined by selecting one of the following methods. : First of Maximum (FOM), Last of Maximum (LOM), Middle of Maximum, Mean of Maximum (MEOM), and Center of Gravity ; COG).

는 정규화 하여 다음의 [수학식 4]와 같이

로 계산된다.Weight obtained through fuzzy

Is normalized and expressed by the following equation (4)

.

Finally, a weight is applied to the image visual fatigue parameter EVF and the biometric visual fatigue parameter OVF to obtain a fusion visual fatigue level (FVFL) as shown in the following equation (5) Can be estimated.

As described above, in the embodiment of the present invention, the visual fatigue is estimated using the 3D image information and the pulse wave signal (PPG) data as the biological signals that change while the user or the viewer watches the 3D image, A method and system for modeling a visual fatigue with high reliability by combining user characteristics of PPG data.

Experiment contents and results

FIG. 3 is a diagram illustrating a three-dimensional image visual fatigue measurement system based on a depth image and a bio-signal according to an embodiment of the present invention, and shows the experimental environment used in the embodiment of the present invention.

As shown in FIG. 3, the viewer and the 3D video apparatus 100 (3D TV) were spaced 120 to 150 cm apart, and the bio-signal measuring apparatus 200 (PPG apparatus) was worn on the left index finger. The observer observe the image and the biomedical signal data in the space that does not affect TV watching. In addition, the environment is configured so that viewers can concentrate on viewing 3D images. The parameters used in the data analysis were obtained at intervals of 1 minute and the mean value was used.

In order to verify the measured visual fatigue, we used the SRVF from the previous research paper "Simplified Relative Model to Measure Visual Fatigue in a Stereoscopy". SRVF is a model of fatigue using 3D image features based on the result of subjective questionnaire about fatigue after watching 3D video.

We show the experimental results using SRVF. 11 shows the results obtained by linear regression analysis of EVF and SRVF. The blue dot is the EVF and SRVF in the same image, and line is the straight line obtained by linear regression. Similarly, FIG. 12 shows the results obtained by linear regression analysis of SRVF and OVF.

Fig. 13 shows the result of using EVF alone and OVF as a result of using only EVF and OVF in relation to SRVF. When the correlation and the determination coefficient are compared, it can be seen that the visual fatigue measurement method according to the embodiment of the present invention using EVF and OVF has high correlation coefficient and determination coefficient.

14 shows FVFL by applying a weight obtained by using EVF and OVF using fuzzy theory.

As another embodiment of the present invention, in order to execute the 3D image visual fatigue measurement method based on the depth image and the bio-signal combined with the hardware in the above-mentioned user, the computer program stored in the computer- have.

That is, the apparatus according to the embodiment of the present invention can be implemented as a computer-readable code on a computer-readable recording medium. A computer-readable recording medium includes all kinds of recording apparatuses in which data that can be read by a computer system is stored. Examples of the recording medium include ROM, RAM, optical disk, magnetic tape, floppy disk, hard disk, nonvolatile memory and the like. The computer-readable recording medium may also be distributed over a networked computer system so that computer readable code can be stored and executed in a distributed manner.

The embodiments and the accompanying drawings described in the present specification are merely illustrative of some of the technical ideas included in the present invention. Accordingly, the embodiments disclosed herein are for the purpose of describing rather than limiting the technical spirit of the present invention, and it is apparent that the scope of the technical idea of the present invention is not limited by these embodiments. It will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

100: 3D imaging device 200: Biosignal measuring device
300: Visual fatigue calculating unit

Claims (17)

(a) obtaining an Expected Visual Fatigue level parameter associated with an image feature from a 3D image;
(b) obtaining an Obtained Visual Fatigue level parameter appearing from a bio-signal of a viewer while viewing the 3D image; And
(c) generating a weight by using a fuzzy function generated from the acquired image time fatigue parameter and the biometric time fatigue parameter, and selecting any one of the generated weights to calculate a user time fatigue, ;
The step (c) includes the steps of: (c1) generating a fuzzy rule table determined by a relationship between an input and an output using the image visual fatigue parameter and the biometric fatigue parameter; (c2) calculating a fuzzy output minimum value and a fuzzy output maximum value using a fuzzy membership function generated based on the generated fuzzy rule table; (c3) calculating a weight by using a defuzzification process on the calculated fuzzy output minimum value and the calculated maximum fuzzy output value; And (c4) selecting one of the generated weights to calculate a fusion based visual fatigue level;
The equation for calculating the weights,

(w _EVF is weight related to image visual fatigue, w _OVF is weight related to biometric fatigue, w _i is weight obtained by purging, and W _i is normalized value of w _i ) A method for measuring three-dimensional visual fatigue based on a user-considered depth image and a bio-signal. The method according to claim 1,
The step (a)
And a step of calculating and acquiring an image visual fatigue parameter by using an accommodation distance and an AC conflict of a convergence distance arising from viewing of the 3D image, And 3 - D Visual Visual Fatigue Measurement Method Based on Biological Signal. The method of claim 2,
The mathematical formula for calculating the image visual fatigue parameter is:

(Where a is the view-point angle of the screen to be displayed and beta is the view-point angle in the negative region) A method of measuring three-dimensional visual fatigue based on a user-considered depth image and a bio-signal. The method according to claim 1,
In the step (b)
Wherein the bio-signal is a pulse wave signal of a viewer while viewing a 3D image, wherein the user is considered to be a depth image and a bio-signal based 3D image visual fatigue measurement method. The method according to claim 1,
The step (b)
(b1) acquiring PPG data by measuring an applicant's pulse wave while watching a 3D image;
(b2) obtaining a plurality of parameters of the obtained PPG data according to an analysis method in a time domain and a frequency domain; And
(b3) selecting one of a plurality of acquired parameters as an obtained visual fatigue parameter, and selecting the depth image and the three-dimensional image visual fatigue based on the bio-signal, How to measure. delete delete The method according to claim 1,
The equation for calculating the fusion-based visual fatigue level (FVFL)

(Wherein EVF denotes an image visual fatigue parameter and OVF denotes a biometric visual fatigue parameter), and a three-dimensional image visual fatigue measurement method based on a user-considered depth image and a bio-signal . A 3D image device for displaying a 3D image;
A bio-signal measuring device for measuring a bio-signal of a viewer while a viewer watches a 3D image displayed from the 3D image device at a certain distance;
Acquiring an Expected Visual Fatigue parameter associated with the image feature from the 3D image and connecting the 3D image device to the 3D image device, and displaying the bio-visual fatigue A visual fatigue parameter and a biometric fatigue parameter are obtained, a weight is generated by using a fuzzy function, and one of the generated weights is selected to calculate a user's visual fatigue A visual fatigue calculating unit for calculating a visual fatigue of the user;
Wherein the visual fatigue calculating unit generates a fuzzy rule table determined based on a relationship between input and output using the image visual fatigue parameter and the biometric fatigue parameter and generates a fuzzy membership function based on the generated fuzzy rule table The fuzzy output minimum value and the fuzzy output maximum value are calculated by using the membership function of the fuzzy output maximum value and the calculated fuzzy output maximum value and the fuzzy output maximum value are calculated using the defuzzification process, Selecting one to calculate a fusion based visual fatigue level;
The equation for calculating the weights,

(w _EVF is weight related to image visual fatigue, w _OVF is weight related to biometric fatigue, w _i is weight obtained by purging, and W _i is normalized value of w _i ) A 3D image visual fatigue measurement system based on a user - considered depth image and a bio - signal. The method of claim 9,
Wherein the bio-signal is a pulse wave signal of a viewer while viewing a 3D image, wherein the user is considered to have a depth image and a bio-signal based 3D image visual fatigue measurement system. The method of claim 9,
The visual fatigue calculating unit may calculate,
And the image visual fatigue parameter is calculated and obtained by using the accommodation distance and the AC conflict between the accommodation distance and the convergence distance generated from the viewing of the 3D image. Signal - Based 3D Image Visual Fatigue Measurement System. The method of claim 11,
The mathematical formula for calculating the image visual fatigue parameter is:

(Where a is the view-point angle of the screen to be displayed and beta is the view-point angle in the negative region) A 3D image visual fatigue measurement system based on a user - considered depth image and a bio - signal. The method of claim 9,
The visual fatigue calculating unit may calculate,
The PPG data is measured by measuring the pulse wave of the applicant while viewing the 3D image, and the acquired PPG data is obtained by obtaining a plurality of parameters in the time domain and the frequency domain according to the analysis method, Is selected as an Obtained Visual Fatigue parameter, and a 3D image visual fatigue measurement system based on a user-considered depth image and a bio-signal is selected. delete delete The method of claim 9,
The equation for calculating the fusion-based visual fatigue level (FVFL)

(Where EVF denotes an image visual fatigue parameter and OVF denotes a biometric visual fatigue parameter), and a three-dimensional image visual fatigue measurement system based on a user-considered depth image and a bio-signal . Combined with hardware,
A computer program stored in a computer-readable recording medium for executing a method of measuring a three-dimensional image visual fatigue based on a depth image and a bio-signal considered by a user of claim 1.

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