CN111191540A - Object state analysis method and system based on temperature gradient - Google Patents

Object state analysis method and system based on temperature gradient Download PDF

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CN111191540A
CN111191540A CN201911323566.7A CN201911323566A CN111191540A CN 111191540 A CN111191540 A CN 111191540A CN 201911323566 A CN201911323566 A CN 201911323566A CN 111191540 A CN111191540 A CN 111191540A
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infrared image
image
denoising
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following formula
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刘志欣
宋柏君
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Datasea Inc
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    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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    • G06V10/30Noise filtering

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Abstract

The invention discloses an object state analysis method based on temperature gradient, which comprises the following steps: acquiring an infrared image of an object; denoising the acquired infrared image to obtain a denoised infrared image; judging the temperature distribution and temperature change of the object based on the de-noised infrared image; judging whether the object has risks or not based on the temperature distribution and the temperature change of the object; and if the object has risk, early warning is carried out.

Description

Object state analysis method and system based on temperature gradient
Technical Field
The present invention relates to the field of image processing and recognition technologies, and in particular, to a method and a system for analyzing an object state based on a temperature gradient.
Background
Hazard identification is an important part of hazard management work. The understanding, generalization and definition of the essential characteristics, development rules and hazard degrees of various dangers objectively existed.
The prior art CN106264568B discloses a non-contact emotion detection method and a non-contact emotion detection device, and relates to the technical field of emotion detection. The method comprises the following steps: respectively acquiring video information containing a detected human body and a heat map/hot spot area map of the detected human body through a camera and an infrared sensor; identifying the video information and the heat map/hot spot area map to obtain human body characteristic data of the detected human body; carrying out deep learning engine association and analysis on the human body characteristic data to obtain the tension degree of the detected human body; and outputting a result of the human body stress degree obtained by the deep learning engine analysis.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The present invention is directed to a method and system for analyzing an object state based on a temperature gradient, which can overcome the disadvantages of the prior art.
In order to achieve the above object, the present invention provides an object state analysis method based on a temperature gradient, which is characterized in that: the object state analysis method based on the temperature gradient comprises the following steps:
acquiring an infrared image of an object;
denoising the acquired infrared image to obtain a denoised infrared image;
judging the temperature distribution and temperature change of the object based on the de-noised infrared image;
judging whether the object has risks or not based on the temperature distribution and the temperature change of the object;
if the object has risk, early warning is carried out;
wherein denoising the acquired infrared image is based on the following formula:
Figure 681799DEST_PATH_IMAGE001
wherein j (x) is a de-noised infrared image, a is a global atmospheric infrared radiation parameter, t is an emission parameter describing a portion of infrared radiation directly reaching the image acquisition device, and i (x) is the acquired infrared image.
In a preferred embodiment, denoising the acquired infrared image to obtain a denoised infrared image includes the following steps:
obtaining a superimposed infrared image based on the obtained infrared image, wherein the superimposed infrared image is formed according to the following formula:
Figure 867930DEST_PATH_IMAGE002
wherein the content of the first and second substances,C(x)is a superimposed infrared image, Sharp () is a laplacian sharpened template, wherein the laplacian sharpened template is represented by the following matrix:
Figure 886701DEST_PATH_IMAGE003
in a preferred embodiment, denoising the acquired infrared image to obtain a denoised infrared image includes the following steps:
establishing a point spread function, wherein the point spread function is represented by the following formula:
Figure 33649DEST_PATH_IMAGE004
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE005
is the euclidean distance of a point in the image to the center point of the template,
Figure 888079DEST_PATH_IMAGE007
is a custom constant value;
establishing a noise signal image of an acquired infrared image based on a point spread functionN(x)Wherein the noise signal imageN (x)Expressed by the following formula:
Figure 804082DEST_PATH_IMAGE009
wherein the content of the first and second substances,
Figure 993755DEST_PATH_IMAGE010
is an inverse fourier transform function.
In a preferred embodiment, denoising the acquired infrared image to obtain a denoised infrared image includes the following steps:
generating a replacement function based on a noise signal imageV(x)Wherein the function is replacedV(x)Is represented by the following formula:
Figure 627999DEST_PATH_IMAGE011
wherein the content of the first and second substances,
Figure 53164DEST_PATH_IMAGE012
the value range is 0.4-0.5.
In a preferred embodiment, denoising the acquired infrared image to obtain a denoised infrared image includes the following steps:
based on alternative functionsV(x)Obtaining a de-noised infrared image J (x), wherein the de-noised infrared image J (x) is represented by the following formula:
Figure 823674DEST_PATH_IMAGE013
the invention provides a device for analyzing the state of an object based on temperature gradient, which is characterized in that: the device for analyzing the state of the object based on the temperature gradient comprises:
a unit for acquiring an infrared image of an object;
a unit for performing denoising processing on the acquired infrared image to obtain a denoised infrared image;
a unit for judging a temperature distribution and a temperature change of the object based on the denoised infrared image;
means for determining whether the subject is at risk based on the temperature distribution and temperature variation of the subject;
means for performing an early warning if the subject is at risk;
wherein denoising the acquired infrared image is based on the following formula:
Figure 184248DEST_PATH_IMAGE001
wherein j (x) is a de-noised infrared image, a is a global atmospheric infrared radiation parameter, t is an emission parameter describing a portion of infrared radiation directly reaching the image acquisition device, and i (x) is the acquired infrared image.
In a preferred embodiment, denoising the acquired infrared image to obtain a denoised infrared image includes the following steps:
obtaining a superimposed infrared image based on the obtained infrared image, wherein the superimposed infrared image is formed according to the following formula:
Figure 305788DEST_PATH_IMAGE014
wherein the content of the first and second substances,C(x)is a superimposed infrared image, Sharp () is a laplacian sharpened template, wherein the laplacian sharpened template is represented by the following matrix:
Figure 410010DEST_PATH_IMAGE003
in a preferred embodiment, denoising the acquired infrared image to obtain a denoised infrared image includes the following steps:
establishing a point spread function, wherein the point spread function is represented by the following formula:
Figure 425240DEST_PATH_IMAGE004
wherein the content of the first and second substances,
Figure 956715DEST_PATH_IMAGE005
is the euclidean distance of a point in the image to the center point of the template,
Figure 299972DEST_PATH_IMAGE007
is a custom constant value;
establishing a noise signal map of an acquired infrared image based on a point spread functionImageN(x)Wherein the noise signal imageN (x)Expressed by the following formula:
Figure 207885DEST_PATH_IMAGE009
wherein the content of the first and second substances,
Figure 77621DEST_PATH_IMAGE010
is an inverse fourier transform function.
In a preferred embodiment, denoising the acquired infrared image to obtain a denoised infrared image includes the following steps:
generating a replacement function based on a noise signal imageV(x)Wherein the function is replacedV(x)Is represented by the following formula:
Figure 779997DEST_PATH_IMAGE011
wherein the content of the first and second substances,
Figure 610550DEST_PATH_IMAGE012
the value range is 0.4-0.5.
In a preferred embodiment, denoising the acquired infrared image to obtain a denoised infrared image includes the following steps:
based on alternative functionsV(x)Obtaining a de-noised infrared image J (x), wherein the de-noised infrared image J (x) is represented by the following formula:
Figure 790996DEST_PATH_IMAGE013
compared with the prior art, the invention has the following advantages: the basis of image recognition is to obtain a clearly recognizable and detailed image, and the more detailed image means the larger the amount of image information, and the larger the amount of image information, the more beneficial the recognition and judgment. However, under the conditions that the resolution of the image capturing device is sufficient and various types of light reflected or emitted by objects are sufficient, there are still many factors that prevent technicians from obtaining high-quality images, a serious distortion is caused by fog, and in rainy seasons, many cities at home and abroad generate thick fog which scatters infrared rays, which causes noise and blur in captured images, and the noise may cover some necessary image details. In order to eliminate the noise, the invention provides an object state analysis method based on temperature gradient, which can eliminate partial noise in an infrared image and restore details covered by the noise.
Drawings
Fig. 1 is a flowchart of a method of object state analysis based on a temperature gradient according to an embodiment of the present invention.
Fig. 2 is a flow chart of a method of denoising processing according to an embodiment of the present invention.
Fig. 3 is an image before denoising processing according to an embodiment of the present invention.
Fig. 4 is an image after denoising processing according to an embodiment of the present invention.
Fig. 5 is an image before denoising processing according to another embodiment of the present invention.
Fig. 6 is an image after denoising processing according to another embodiment of the present invention.
Detailed Description
The following detailed description of the present invention is provided in conjunction with the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the specific embodiments.
Throughout the specification and claims, unless explicitly stated otherwise, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element or component but not the exclusion of any other element or component.
Fig. 1 is a flowchart of a method of object state analysis based on a temperature gradient according to an embodiment of the present invention. As shown in the drawing, the method of the object state analysis method based on the temperature gradient of the present invention includes:
step 101: acquiring an infrared image of an object;
step 102: denoising the acquired infrared image to obtain a denoised infrared image;
step 103: judging the temperature distribution and temperature change of the object based on the de-noised infrared image;
step 104: judging whether the object has risks or not based on the temperature distribution and the temperature change of the object;
step 105: if the subject has a risk (risk prediction method or algorithm such as the test method for the emotion of the subject mentioned in the background of the application), performing early warning;
wherein denoising the acquired infrared image is based on the following formula:
Figure 282282DEST_PATH_IMAGE001
where j (x) is a de-noised infrared image, a is a global atmospheric infrared radiation parameter, t is an emission parameter describing a portion of infrared radiation directly reaching the image acquisition device, which is eventually replaced by other known quantities, so the method of calculating this value is not important, and i (x) is the acquired infrared image. Wherein the value a is calculated by the method described in the article "Single image halogenated using dark channel prior", IEEE Conference on Computer Vision and pattern recognition, 2009, which is calculated for visible light scattering, so that the value a obtained from said article needs to be adjusted for infrared spectroscopy in such a way that the value a obtained from said article is divided by a constant value of 1.5-2.
In a preferred embodiment, denoising the acquired infrared image to obtain a denoised infrared image includes the following steps:
obtaining a superimposed infrared image based on the obtained infrared image, wherein the superimposed infrared image is formed according to the following formula:
Figure 155560DEST_PATH_IMAGE014
wherein the content of the first and second substances,C(x)is a superimposed infrared image, Sharp () is a laplacian sharpened template, wherein the laplacian sharpened template is represented by the following matrix:
Figure 473409DEST_PATH_IMAGE003
in a preferred embodiment, denoising the acquired infrared image to obtain a denoised infrared image includes the following steps:
establishing a point spread function, wherein the point spread function is represented by the following formula:
Figure 457546DEST_PATH_IMAGE004
wherein the content of the first and second substances,
Figure 301874DEST_PATH_IMAGE005
is the euclidean distance of a point in the image to the center point of the template,
Figure 346053DEST_PATH_IMAGE007
is a custom constant value;
establishing a noise signal image of an acquired infrared image based on a point spread functionN(x)Wherein the noise signal imageN (x)Expressed by the following formula:
Figure 151198DEST_PATH_IMAGE009
wherein the content of the first and second substances,
Figure 939026DEST_PATH_IMAGE010
is an inverse fourier transform function.
In a preferred embodiment, denoising the acquired infrared image to obtain a denoised infrared image includes the following steps:
generating a replacement function based on a noise signal imageV(x)Wherein the function is replacedV(x)Is represented by the following formula:
Figure 637860DEST_PATH_IMAGE011
wherein the content of the first and second substances,
Figure 852941DEST_PATH_IMAGE012
the value range is 0.4-0.5.
In a preferred embodiment, denoising the acquired infrared image to obtain a denoised infrared image includes the following steps:
based on alternative functionsV(x)Obtaining a de-noised infrared image J (x), wherein the de-noised infrared image J (x) is represented by the following formula:
Figure 879803DEST_PATH_IMAGE013
the invention provides a device for analyzing the state of an object based on temperature gradient, which is characterized in that: the device for analyzing the state of the object based on the temperature gradient comprises:
a unit for acquiring an infrared image of an object;
a unit for performing denoising processing on the acquired infrared image to obtain a denoised infrared image;
a unit for judging a temperature distribution and a temperature change of the object based on the denoised infrared image;
means for determining whether the subject is at risk based on the temperature distribution and temperature variation of the subject;
means for performing an early warning if the subject is at risk;
wherein denoising the acquired infrared image is based on the following formula:
Figure 736900DEST_PATH_IMAGE001
wherein j (x) is a de-noised infrared image, a is a global atmospheric infrared radiation parameter, t is an emission parameter describing a portion of infrared radiation directly reaching the image acquisition device, and i (x) is the acquired infrared image.
In a preferred embodiment, denoising the acquired infrared image to obtain a denoised infrared image includes the following steps:
obtaining a superimposed infrared image based on the obtained infrared image, wherein the superimposed infrared image is formed according to the following formula:
Figure 165608DEST_PATH_IMAGE002
wherein the content of the first and second substances,C(x)is a superimposed infrared image, Sharp () is a laplacian sharpened template, wherein the laplacian sharpened template is represented by the following matrix:
Figure 676223DEST_PATH_IMAGE003
in a preferred embodiment, denoising the acquired infrared image to obtain a denoised infrared image includes the following steps:
establishing a point spread function, wherein the point spread function is represented by the following formula:
Figure 455961DEST_PATH_IMAGE004
wherein the content of the first and second substances,
Figure 585591DEST_PATH_IMAGE005
is the euclidean distance of a point in the image to the center point of the template,
Figure 868804DEST_PATH_IMAGE016
is a custom constant value;
establishing a noise signal image of an acquired infrared image based on a point spread functionN(x)WhereinNoise signal imageN (x)Expressed by the following formula:
Figure 320295DEST_PATH_IMAGE009
wherein the content of the first and second substances,
Figure 321749DEST_PATH_IMAGE010
is an inverse fourier transform function.
In a preferred embodiment, denoising the acquired infrared image to obtain a denoised infrared image includes the following steps:
generating a replacement function based on a noise signal imageV(x)Wherein the function is replacedV(x)Is represented by the following formula:
Figure 989491DEST_PATH_IMAGE011
wherein the content of the first and second substances,
Figure 392790DEST_PATH_IMAGE012
the value range is 0.4-0.5.
In a preferred embodiment, denoising the acquired infrared image to obtain a denoised infrared image includes the following steps:
based on alternative functionsV(x)Obtaining a de-noised infrared image J (x), wherein the de-noised infrared image J (x) is represented by the following formula:
Figure 510788DEST_PATH_IMAGE013
fig. 3 is an image before denoising processing according to an embodiment of the present invention. Fig. 4 is an image after denoising processing according to an embodiment of the present invention. Fig. 5 is an image before denoising processing according to another embodiment of the present invention. Fig. 6 is an image after denoising processing according to another embodiment of the present invention. Since the noise of the infrared image is not obvious when the infrared image is viewed by naked eyes (but the noise is obvious for machine vision), in order to conveniently and clearly embody the denoising effect of the invention, the visible light image is used for testing. As can be seen from comparing fig. 3 and fig. 4, in fig. 3, the upper left corner and the upper right corner (i.e., the portion framed by the box in fig. 4) of the graph have serious obscuration regions, and in fig. 4, the obscuration regions are weakened after the method of the present invention is applied. Fig. 5 and 6 have similar effects.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing descriptions of specific exemplary embodiments of the present invention have been presented for purposes of illustration and description. It is not intended to limit the invention to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain certain principles of the invention and its practical application to enable one skilled in the art to make and use various exemplary embodiments of the invention and various alternatives and modifications as are suited to the particular use contemplated. It is intended that the scope of the invention be defined by the claims and their equivalents.

Claims (10)

1. An object state analysis method based on temperature gradient is characterized in that: the object state analysis method based on the temperature gradient comprises the following steps:
acquiring an infrared image of an object;
denoising the acquired infrared image to obtain a denoised infrared image;
determining a temperature distribution and a temperature change of the object based on the de-noised infrared image;
judging whether the object has a risk or not based on the temperature distribution and the temperature change of the object;
if the object has risk, early warning is carried out;
wherein denoising the acquired infrared image is based on the following formula:
Figure DEST_PATH_IMAGE001
wherein j (x) is a de-noised infrared image, a is a global atmospheric infrared radiation parameter, t is an emission parameter describing a portion of infrared radiation directly reaching the image acquisition device, and i (x) is the acquired infrared image.
2. The temperature gradient-based object state analysis method according to claim 1, wherein: denoising the acquired infrared image to obtain a denoised infrared image, comprising the following steps:
obtaining a superimposed infrared image based on the obtained infrared image, wherein the superimposed infrared image is formed according to the following formula:
Figure 262745DEST_PATH_IMAGE002
wherein the content of the first and second substances,C(x)is a superimposed infrared image, Sharp () is a laplacian sharpened template, wherein the laplacian sharpened template is represented by the following matrix:
Figure DEST_PATH_IMAGE003
3. the temperature gradient-based object state analysis method according to claim 1, wherein: denoising the acquired infrared image to obtain a denoised infrared image, comprising the following steps:
establishing a point spread function, wherein the point spread function is represented by the following formula:
Figure 530915DEST_PATH_IMAGE004
wherein the content of the first and second substances,
Figure 851038DEST_PATH_IMAGE006
is in the imageThe euclidean distance of the point of (a) to the center point of the template,
Figure DEST_PATH_IMAGE007
is a custom constant value;
establishing a noise signal image of the acquired infrared image based on the point spread functionN(x)Wherein the noise signal imageN(x)Expressed by the following formula:
Figure 812041DEST_PATH_IMAGE008
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE009
is an inverse fourier transform function.
4. The temperature gradient-based object state analysis method according to claim 3, wherein: denoising the acquired infrared image to obtain a denoised infrared image, comprising the following steps:
generating a replacement function based on the noise signal imageV(x)Wherein the replacement functionV(x)Is represented by the following formula:
Figure 584825DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE011
the value range is 0.4-0.5.
5. The temperature gradient-based object state analysis method according to claim 4, wherein: denoising the acquired infrared image to obtain a denoised infrared image, comprising the following steps:
based on the replacement functionV(x)Obtaining a de-noised infrared image J (x), wherein the de-noised infrared imageJ (x) is represented by the following formula:
Figure 594369DEST_PATH_IMAGE012
6. an apparatus for analyzing a state of an object based on a temperature gradient, comprising: the device for analyzing the state of the object based on the temperature gradient comprises:
a unit for acquiring an infrared image of an object;
a unit for performing denoising processing on the acquired infrared image to obtain a denoised infrared image;
means for determining a temperature distribution and a temperature change of the object based on the denoised infrared image;
means for determining whether the subject is at risk based on the temperature distribution and temperature variation of the subject;
means for performing an early warning if the subject is at risk;
wherein denoising the acquired infrared image is based on the following formula:
Figure 536042DEST_PATH_IMAGE001
wherein j (x) is a de-noised infrared image, a is a global atmospheric infrared radiation parameter, t is an emission parameter describing a portion of infrared radiation directly reaching the image acquisition device, and i (x) is the acquired infrared image.
7. The apparatus for analyzing a state of an object based on a temperature gradient according to claim 6, wherein: denoising the acquired infrared image to obtain a denoised infrared image, comprising the following steps:
obtaining a superimposed infrared image based on the obtained infrared image, wherein the superimposed infrared image is formed according to the following formula:
Figure DEST_PATH_IMAGE013
wherein the content of the first and second substances,C(x)is a superimposed infrared image, Sharp () is a laplacian sharpened template, wherein the laplacian sharpened template is represented by the following matrix:
Figure 402367DEST_PATH_IMAGE003
8. the apparatus for analyzing a state of an object based on a temperature gradient according to claim 7, wherein: denoising the acquired infrared image to obtain a denoised infrared image, comprising the following steps:
establishing a point spread function, wherein the point spread function is represented by the following formula:
Figure 865710DEST_PATH_IMAGE004
wherein the content of the first and second substances,
Figure 741262DEST_PATH_IMAGE006
is the euclidean distance of a point in the image to the center point of the template,
Figure 973660DEST_PATH_IMAGE007
is a custom constant value;
establishing a noise signal image of the acquired infrared image based on the point spread functionN(x)Wherein the noise signal imageN(x)Expressed by the following formula:
Figure 542045DEST_PATH_IMAGE008
wherein the content of the first and second substances,
Figure 227104DEST_PATH_IMAGE009
is an inverse fourier transform function.
9. The apparatus for analyzing a state of an object based on a temperature gradient according to claim 8, wherein: denoising the acquired infrared image to obtain a denoised infrared image, comprising the following steps:
generating a replacement function based on the noise signal imageV(x)Wherein the replacement functionV(x)Is represented by the following formula:
Figure 906347DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure 993252DEST_PATH_IMAGE011
the value range is 0.4-0.5.
10. The apparatus for analyzing a state of an object based on a temperature gradient according to claim 9, wherein: denoising the acquired infrared image to obtain a denoised infrared image, comprising the following steps:
based on the replacement functionV(x)Obtaining a de-noised infrared image J (x), wherein the de-noised infrared image J (x) is represented by the following formula:
Figure 732537DEST_PATH_IMAGE012
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Application publication date: 20200522