Patent Description:
<CIT> relates to an ultrasonography and an ultrasonic image display method. An image showing the spatial distribution of the hardness of a tissue of an object to be examined from which the influence of the pressure amount is eliminated is displayed. From ultrasonic tomography data measured by pressing the tissue, a physical quantity relating to the distortion of the tissue at measurement points of the tissue is determined. An elasticity image of the tissue is created according to the physical quantity. The physical quantities at the measurement points are indexed by using the physical quantity in a reference region determined in the elasticity image as a reference, and an indexed elasticity image showing the distribution of the index value is created.

<CIT> relates to methods and systems for an illuminant compensation. In particular, these methods and systems include a method for operations on an image, for example, an image of a human face. In the described methods and systems, it is determined for each pixel in the image whether it is part of the face region. A surface fitting is then determined based on only the pixels that are determined to be part of the face region. Also, described are methods and systems for image normalization wherein the standard deviation and average for the gray levels of the pixels are determined and then used to normalize the image so that the gray level for each of the pixels falls between a particular range.

A stress distribution measurement method according to <CIT> is a method of measuring stress distribution generated on a structural object including two support parts and a beam part provided between the support parts. The method includes: generating first image data by performing, through a first image capturing unit, image capturing of a moving object or an identification display object attached to the structural object from the moving object; calculating, based on the first image data, a movement duration in which the moving object moves between the support parts; generating, as second image data, thermal image data by performing image capturing of the surface of the beam part through a second image capturing unit; calculating a temperature change amount based on a second image data group corresponding to the movement duration; and calculating a stress change amount based on the temperature change amount to calculate stress distribution based on the stress change amount.

Conventionally, in a structure such as a bridge on an expressway, a technique related to a method and a system for measuring a stress distribution generated in the structure by the move of a mobile entity such as a vehicle is known (see, for example, <CIT>).

In the above technique, a stress distribution generated on a bridge or the like is determined based on a temperature change amount due to movement of a mobile entity such as a vehicle.

However, when conditions such as the size and weight of the vehicle are different, it is not easy to detect a difference in stress distribution due to the difference in conditions even if stress distributions obtained in the respective conditions are compared as they are.

That is, under conditions where measurement conditions, measurement date and time, load weights, and the like are different, there has been a problem that it is not possible to identify an abnormal portion or the like by a simple comparison.

The present disclosure was conceived in view of the situations and it is therefore one non-limiting and exemplary embodiment provides a stress distribution image processing device capable of obtaining an image that can be easily compared even when conditions are different.

In one general aspect, the techniques disclosed here feature: a stress distribution image processing device as defined in claim <NUM>.

According to the stress distribution image processing device of the present invention, since a normalized image can be obtained by designating a region including a portion of stress equal to or larger than the predetermined threshold value as the normalization region and performing the normalization, it is possible to provide a normalized image that can be compared even under different conditions.

That is, even stress distribution images different in measurement conditions, a measurement date and time, and the like can be compared.

The present disclosure will become readily understood from the following description of non-limiting and exemplary embodiments thereof made with reference to the accompanying drawings, in which like parts are designated by like reference numeral and in which:.

A stress distribution image processing device according to a first aspect is defined in claim <NUM>.

According to the above configuration, since a normalized image can be obtained by designating a region including a portion of stress equal to or larger than the predetermined threshold value as the normalization region and performing the normalization, it is possible to provide a normalized image that can be compared even under different conditions.

In the first aspect, in the stress distribution image processing device according to a second aspect, the region designation unit sets a rectangular range including a portion of stress equal to or larger than the predetermined threshold value as a normalization region.

In any one of the first or second aspect, the stress distribution image processing device according to a third aspect, further comprising an entire screen normalization unit configured to perform normalization over an entire screen of the stress distribution image based on a normalization method in the normalization region to obtain a normalized image.

In any one of the first to third aspect, the stress distribution image processing device according to a fourth aspect, further comprising a four arithmetic operation unit configured to perform, with respect to a plurality of normalized images corresponding to a plurality of different conditions for an identical target object, at least one of four arithmetic operations on all pixels in the normalization region between the respective normalized images to obtain a four arithmetic operation processed image.

According to the above configuration, the feature amount can be extracted from the obtained four arithmetic operation processed image.

In the fourth aspect, the stress distribution image processing device according to an fifth aspect, further comprising an abnormality detection unit configured to detect an abnormality of the target object based on the four arithmetic operation processed image.

Hereinafter, a stress distribution image processing device according to a preferred embodiment will be described with reference to the accompanying drawings.

It should be noted that in the drawings, substantially the same members are denoted by the same reference numerals.

<FIG> is a block diagram showing a configuration of a stress distribution image processing device <NUM> according to a first preferred embodiment.

The stress distribution image processing device <NUM> according to the first preferred embodiment includes a region designation unit 35b that designates a normalization region for performing normalization, and a normalization unit 35c that obtains a normalized image. The region designation unit 35b designates, as a normalization region, a region including a portion of a stress equal to or more than a predetermined threshold value in the entire screen of the stress distribution image of the target object. In addition, the normalization unit 35c normalizes all the pixels in the normalization region based on the stress values in all the pixels in the normalization region to obtain a normalized image. It should be noted that the stress distribution image processing device <NUM> may include an infrared camera <NUM> that captures an infrared image of the target object <NUM> and an image processing unit <NUM> that performs image processing on the stress distribution image. The image processing unit <NUM> may include a region designation unit 35b and a normalization unit 35c. In addition, for example, a predetermined vibration may be applied to the target object <NUM> from a vibrator (not shown) connected by bolts 2a, 2b, and 2c.

The stress distribution image processing device <NUM> can provide a normalized image that can be compared even under different conditions.

Hereinafter, each member constituting the stress distribution image processing device <NUM> will be described.

The image processing unit <NUM> performs image processing on the stress distribution image. The image processing unit <NUM> is, for example, a computer apparatus. As the computer apparatus, a general-purpose computer device can be used, and for example, as shown in <FIG>, a processing unit <NUM>, a storage unit <NUM>, and a display unit <NUM> are included. It should be noted that an input device, a storage device, an interface, and the like may be further included.

The processing unit <NUM> has only to be, for example, a central processing operator (CPU, MPU, or the like), a microcomputer, or a processing device capable of executing a computer-executable instruction.

The storage unit <NUM> may be, for example, at least one of a ROM, an EEPROM, a RAM, a flash SSD, a hard disk, a USB memory, a magnetic disk, an optical disc, a magneto-optical disk, and the like.

The storage unit <NUM> includes a program <NUM>. It should be noted that when the image processing unit <NUM> is connected to a network, the program <NUM> may be downloaded from the network as necessary.

The program <NUM> includes a region designation unit 35b and a normalization unit 35c. It should be noted that if necessary, a stress distribution calculation unit 35a, an entire screen normalization unit 35d, a four arithmetic operation unit 35e, and an abnormality detection unit 35f may be included. The stress distribution calculation unit 35a, the region designation unit 35b, the normalization unit 35c, the entire screen normalization unit 35d, the four arithmetic operation unit 35e, and the abnormality detection unit 35f are read from the storage unit <NUM> and executed by the processing unit <NUM> at the time of execution.

The processing unit <NUM> is configured to obtain a stress distribution image based on a plurality of infrared images at different times to function as a stress distribution calculation unit 35a. That is, the stress change amount is obtained based on the temperature change amount between the plurality of infrared images at different times. An image that has a stress change amount for all pixels is a stress distribution image. It should be noted that when the stress distribution image is provided in advance, it is not necessary to provide this stress distribution calculation unit.

It should be noted that the stress distribution calculation unit 35a can calculate the stress change amount Δδ from the temperature change amount ΔT by using, for example, the following formula (<NUM>) representing a thermoelastic effect. <MAT> where, K is a thermoelastic coefficient, K = α/(CP), and T is an absolute temperature of a surface of a train being a moving body. The α is a linear expansion coefficient of the surface of the target object, ρ is the density of the surface of the target object, and CP is the specific heat of the surface of the target object under constant stress.

Then, the stress distribution calculation unit 35a can determine the stress distribution of each portion based on the stress change amounts of all the pixels.

<FIG> is a diagram showing a stress distribution image <NUM> and some examples of normalization regions 6a, 6b, and 6c.

The processing unit <NUM> is configured to designate, as normalization regions 6a, 6b, and 6c for performing normalization, regions including portions of stress equal to or greater than a predetermined threshold value in the entire screen of the stress distribution image <NUM> of the target object to function as a region designation unit 35b. For example, in the example in <FIG>, it can be seen that the periphery of the bolts 2a and 2c of the target object <NUM> in <FIG> has a region 8a having a particularly large stress value. That is, a rectangular range including the region 8a having a large stress value equal to or larger than a predetermined threshold value may be set as the normalization region. Specifically, rectangular regions including the periphery of the bolt 2a and the bolt 2c can be designated as the normalization regions 6a and 6b. In addition, a larger rectangular region including these normalization regions 6a and 6b may be designated as the normalization region 6c. It should be noted that since normalization needs to include not only the region 8a having a large stress but also the region 8b having a small stress value, the normalization region may be designated to include a certain range. Thus, designating the normalization region by the region designation unit 35b allows the influence of noise or the like included in the case of the entire screen to be avoided. In addition, a region including a range desired to be observed may be set as the normalization region. Furthermore, when, in the stress distribution, the compressive stress is set to be in the positive direction and the tensile stress is set to be in the negative direction, the characteristic of the stress distribution can be further grasped by designating the normalization region with the threshold value. That is, when both the positive direction and the negative direction are included, the characteristic of the stress distribution is less likely to be understood, but limiting to only one direction allows the scale to be widened and the characteristic to be easily understood.

It should be noted that when there is no noise or the like in the stress distribution image <NUM> and the scale is appropriate, the entire screen may be designated as the normalization region.

The processing unit is configured to normalize all the pixels in the normalization region on the basis of the stress values in all the pixels in the designated normalization region to obtain a normalized image to function as a normalization unit 35c. The normalization in this case may be, for example, any one of the following.

The processing unit <NUM> is configured to perform normalization over the entire screen of the stress distribution image based on the normalization method in the normalization region to obtain a normalized image of the entire screen to function as an entire screen normalization unit 35d. In this case, in the entire screen, the normalized maximum value corresponding to the maximum value in the normalization region is also assigned to all the pixels exceeding the maximum value of the stress in the designated normalization region. Similarly, in the entire screen, the normalized minimum value corresponding to the minimum value in the normalization region is assigned also to all the pixels below the minimum value of the stress in the designated normalization region. Thus, performing the normalization in the normalization region being a part of the entire screen, and then expanding the normalization to the entire screen allows also the high-luminance noise included in the case of the entire screen to be suppressed within the range in the normalization.

<FIG> are stress distribution images before normalization with various excitation widths to the target object. <FIG> are normalized images after normalization of the stress distribution images in <FIG>. <FIG> are diagrams showing, on a scale of <NUM> to <NUM>, images of differences between two adjacent normalized images in <FIG>. <FIG> are diagrams showing, on a scale of -<NUM> to <NUM>, images of differences between two adjacent normalized images in <FIG>. <FIG> are diagrams showing, on a scale of <NUM> to <NUM>, images of differences between normalized images in <FIG> and a normalized image in <FIG>. <FIG> are diagrams showing, on a scale of-<NUM> to <NUM>, images of differences between normalized images in <FIG> and a normalized image in <FIG>. <FIG> are diagrams showing, on a scale of -<NUM> to <NUM>, images obtained by dividing each pixel of the normalized images in <FIG> and <FIG> by each pixel of the normalized image in <FIG>. <FIG> are diagrams showing, on a scale of <NUM> to <NUM>, images obtained by multiplying each pixel of the normalized images in <FIG> and <FIG> by each pixel of the normalized image in <FIG>.

With respect to the plurality of normalized images, the processing unit <NUM> is configured to perform at least one of four arithmetic operations, that is, addition, subtraction, multiplication, and division on all pixels between the respective normalized images to obtain a four arithmetic operation processed image to function as a four arithmetic operation unit 35e. It is possible to extract various feature amounts by obtaining a four arithmetic operation processed image obtained by performing four arithmetic operations on normalized images different in excitation conditions to the target object. Thus, it is possible to use the feature amount as the extracted supervised data when performing the mechanization learning.

The difference can be seen by difference between normalized images of adjacent excitation conditions, that is, subtraction. In this case, when a case in which the scale of the image of the difference is <NUM> to <NUM> (<FIG>) and a case in which the scale is -<NUM> to <NUM> (<FIG>) are compared with each other, it can be seen that the feature portion that can be extracted changes depending on whether or not <NUM> is sandwiched in the scale.

In addition, in the images (<FIG> and <FIG>) of the difference between the normalized image in each excitation condition and the normalized image in the minimum excitation condition, the difference from the normalized image in the minimum excitation condition is clear.

When the direction of change of the stress is different in a case where the excitation condition is different, performing addition of each normalized image having a scale sandwiching <NUM> leads to be canceled out to be <NUM>. That is, detecting a portion that becomes <NUM> by addition makes it possible to find a portion where the direction of the stress change is opposite, for example.

In the images obtained by multiplying the normalized image in each excitation condition by the normalized image in the intermediate excitation condition (<FIG>), a singular point is emphasized.

In the images obtained by dividing the normalized image in each excitation condition by the normalized image in the intermediate excitation condition (<FIG>), a characteristic close to subtraction is extracted.

The processing unit <NUM> is configured to detect an abnormality of the target object based on the obtained four arithmetic operation processed images to function as an abnormality detection unit 35f. In the range of elastic deformation, the target object returns to the original state even when deformed by excitation. On the other hand, in the case of being deformed by receiving excessive stress exceeding the range of elastic deformation, the target object undergoes plastic deformation. For example, the portion of plastic deformation due to excessive stress can be detected by images of difference between adjacent excitation conditions (<FIG> and <FIG>), images of difference between the normalized image in each excitation condition and the normalized image in the minimum excitation condition (<FIG> and <FIG>), or the like.

In addition, difference similar to that in the subtraction may be detected in the images of division (<FIG>). Furthermore, singular points may be detected in the images of multiplication (<FIG>).

The display unit <NUM> may display the captured infrared image, the obtained stress distribution image, the normalized image, the four arithmetic operation processed image, and the like.

When an infrared image of the target object <NUM> is captured and a stress distribution image is obtained from the infrared image, an infrared camera <NUM> may be used. When a stress distribution image has already been obtained, there is no need to provide an infrared camera. The infrared camera <NUM> has a plurality of pixels, for example, <NUM> × <NUM> pixels, and captures an infrared image of the target object <NUM>. It should be noted that the above characteristics of the infrared camera are examples, and are not limited thereto.

It should be noted that at least one infrared camera <NUM> has only to be provided. Two or more infrared images may be used in order to capture infrared images over the entire field of view, but in this case, it is desirable to perform alignment on the infrared images captured by the respective infrared cameras.

<FIG> is a flowchart of the stress distribution image processing method according to the first preferred embodiment.

According to this stress distribution image processing method, a region including a portion of stress equal to or larger than a predetermined threshold value is designated as a normalization region, and normalization is performed. Therefore, a normalized image that can be compared even under different conditions can be provided.

In addition, in the region designation step (S01), a rectangular range including a portion of stress equal to or larger than a predetermined threshold value may be set as the normalization region.

Furthermore, the stress distribution image processing method may include an entire screen expansion step of performing normalization over the entire screen of the stress distribution image based on the normalization method in the normalization region to obtain a normalized image.

Furthermore, with respect to a plurality of normalized images corresponding to a plurality of different conditions for the same target object, the stress distribution image processing method may include a four arithmetic operation step of performing at least one of the four arithmetic operations on all the pixels in the normalization region between the respective normalized images to obtain a four arithmetic operation processed image.

In addition, the stress distribution image processing method may include an abnormality detection step of detecting an abnormality of the target object based on the four arithmetic operation processed image.

The stress distribution image processing device and the stress distribution image processing method can handle an image if it is a stress distribution image related to a target object to which stress is applied.

The target object may be, for example, a bridge, a heavy machine, a vehicle, a platform truck, or the like.

It should be noted that the present disclosure includes appropriate combination of any embodiments and/or examples among the various embodiments and/or examples described above, and effects of the respective embodiments and/or examples can be exhibited.

Claim 1:
A stress distribution image processing device (<NUM>) comprising:
a processing unit (<NUM>), characterized in that the processing unit (<NUM>) is configured to : designate (S01) a normalization region (6a, 6b, 6c); and normalize (S02) pixels in the normalization region (6a, 6b, 6c) based on stress values in the normalization region (6a, 6b, 6c) to obtain a normalized image,
wherein the normalization region (6a, 6b, 6c) is designated (S01) such that it includes a portion of stress equal to or larger than a predetermined threshold value in a stress distribution image (<NUM>) of a target object (<NUM>)
wherein the normalizing (S02) uses a normalization method in which a maximum value and a minimum value of stress values in the normalization region (6a, 6b, 6c) are set to <NUM> and <NUM>.