KR20160083784A - Method and apparatus for real time detection of airborne fibers by image processing program - Google Patents
Method and apparatus for real time detection of airborne fibers by image processing program Download PDFInfo
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- KR20160083784A KR20160083784A KR1020150045293A KR20150045293A KR20160083784A KR 20160083784 A KR20160083784 A KR 20160083784A KR 1020150045293 A KR1020150045293 A KR 1020150045293A KR 20150045293 A KR20150045293 A KR 20150045293A KR 20160083784 A KR20160083784 A KR 20160083784A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/02—Investigating particle size or size distribution
- G01N15/0205—Investigating particle size or size distribution by optical means, e.g. by light scattering, diffraction, holography or imaging
- G01N15/0227—Investigating particle size or size distribution by optical means, e.g. by light scattering, diffraction, holography or imaging using imaging, e.g. a projected image of suspension; using holography
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/06—Investigating concentration of particle suspensions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/06—Investigating concentration of particle suspensions
- G01N2015/0693—Investigating concentration of particle suspensions by optical means, e.g. by integrated nephelometry
Abstract
Description
BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a technique for detecting asbestos fibers present in a certain environment such as air, and more particularly to a technique for detecting asbestos fibers by modeling asbestos fibers The present invention relates to a technique for analyzing images taken to detect existing asbestos fibers.
Asbestos has been used in various fields such as interior materials of buildings such as buildings and brake pads of automobiles due to its high heat resistance characteristics. However, when the asbestos is scattered in the air, it becomes a cause of lung cancer and the like when sucked in the human body, and the damage by the asbestos becomes a big problem. This is particularly problematic in that it is inevitable that asbestos is scattered around in the dismantling work of buildings or the like during construction and expansion work on buildings. Accordingly, a technique for calculating the concentration of asbestos in a certain environment such as air has emerged as an important technique for coping with and preventing the above problems.
Conventional asbestos detection methods for grasping the concentration of asbestos in the air include phase contrast microscopy, electron microscopy and the like. Particularly, in the case of the phase difference microscope method, although it is widely used as the simplest method, since it is easy to divide and fade even if enlarged through a high magnification optical lens due to the fine structure of asbestos, Which may lead to inaccurate results. Another method is to use expensive equipment, and it takes a lot of time and labor.
Accordingly, an object of the present invention is to provide a technique for automatically processing and detecting an image of an asbestos fiber having the above characteristics taken in an environment in which asbestos fibers are scattered and floating.
It is also an object of the present invention to provide a technique for detecting various types of asbestos fibers comprehensively, thereby improving the accuracy of detection of asbestos fibers in air.
In order to achieve the above object, a method for real-time detection of a fibrous substance in air through an image processing program according to an embodiment of the present invention is characterized in that an apparatus for real- Pre-processing the photographed image to calculate a gradient of a pixel included in the photographed image of the target environment for detection; Selecting a pixel as a boundary candidate pixel among the pixels included in the photographed image, wherein the calculated gradient is greater than a preset threshold value; And modeling the pixels of the boundary candidate pixel with the first fibrous material by grouping pixels of the calculated gradient with respect to the boundary candidate pixel and less than a predetermined error range among the pixels adjacent to the boundary candidate pixel.
An apparatus for real-time detection of fibrous material in air through an image processing program according to an embodiment of the present invention is an apparatus for detecting a fibrous material in air by using an image processing program A preprocessor for preprocessing the image; A reference pixel selecting unit that selects, as a boundary candidate pixel, a pixel among the pixels included in the photographed image, wherein the calculated gradient value is larger than a preset threshold value; And a fiber modeling unit for grouping pixels among the pixels adjacent to the boundary candidate pixel, wherein the calculated direction of the calculated gradient is less than a preset error range with respect to the boundary candidate pixel, and modeling the pixels as a first fibrous material .
According to the present invention, in the calculation of the concentration of asbestos fibers through the detection of fibrous substances in the air, the problem of the above-mentioned conventional asbestos fiber detection technology is that, It is possible to solve the problem of the reduction of the accuracy due to the limit of the execution site and the subjectivity which can be performed by automatically detecting the asbestos fibers by analyzing the images taken in real time.
In addition, it is possible to accurately detect linear, curved, and branching type fibrous materials according to the diversity of the fibrous material scattered in the air, thereby enabling accurate recognition of asbestos fiber objects analyzed through the fibrous material in real time have.
Figures 1 and 2 are flow charts of a method for real-time detection of airborne fibrous material through an image processing program in accordance with an embodiment of the present invention.
3 is a block diagram of a configuration of an apparatus for real-time detection of airborne fibrous material through an image processing program according to an embodiment of the present invention.
Figures 4-6 are sample illustrations to illustrate specific embodiments for modeling fibrous materials in accordance with one embodiment of the present invention.
Figures 7 to 10 are graphs illustrating experimental results for modeling fibrous materials in accordance with one embodiment of the present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, a method and apparatus for real-time detection of airborne fibrous substances through an image processing program according to embodiments of the present invention will be described with reference to the accompanying drawings.
The following examples are intended to illustrate the present invention and should not be construed as limiting the scope of the present invention. Accordingly, an equivalent invention performing the same function as the present invention is also within the scope of the present invention.
In addition, in adding reference numerals to the constituent elements of the drawings, it is to be noted that the same constituent elements are denoted by the same reference numerals even though they are shown in different drawings. In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.
In describing the components of the present invention, terms such as first, second, A, B, (a), and (b) may be used. These terms are intended to distinguish the constituent elements from other constituent elements, and the terms do not limit the nature, order or order of the constituent elements. When a component is described as being "connected", "coupled", or "connected" to another component, the component may be directly connected to or connected to the other component, It should be understood that an element may be "connected," "coupled," or "connected."
Figures 1 and 2 are flow charts of a method for real-time detection of airborne fibrous material through an image processing program in accordance with an embodiment of the present invention. In the following description, each function of the method for real-time detection of airborne fibrous substances through an image processing program according to an embodiment of the present invention will be described below with reference to FIG. 3, It will be understood that each configuration of the apparatus for real-time detection of the fibrous material in air through the program is performed, but it will be appreciated that it can be performed by other terminals and systems.
Referring to FIG. 1, in a method for real-time detection of airborne fibrous material through an image processing program according to an embodiment of the present invention, an apparatus for real-time detection of airborne fibrous material through an image processing program First, in order to detect a fibrous substance in the air, a step (S10) of preprocessing a photographed image is performed in order to calculate a gradient of a pixel included in an image of a target environment.
The calculation of the asbestos concentration in air is performed by detecting the fibrous material in the detection target area scattered in the air and deriving a state in which the asbestos fibers are scattered. That is, a fibrous material scattered in the air is generally detected by a phase difference microscope or other method, except that expensive equipment capable of extracting components of a detection object such as a transmission electron microscope (TEM) It is predicted that a predetermined ratio among the fibrous materials is asbestos fibers, and the concentration of the asbestos fibers is calculated.
The present invention relates to a method for calculating the concentration of asbestos fibers which is generally used, wherein the fibrous material scattered in the air in the target environment is automatically modeled from the image of the target environment and the asbestos fiber concentration is predicted based on the model Thereby detecting asbestos fibers automatically as a result.
Therefore, in step S10, an image of the target environment is used. In the present invention, a high-speed camera is used to capture the target environment. However, in the micro environment for detecting fibrous substances in a microscopic environment such as a microscope Various imaging apparatuses may be used depending on the conditions of the target environment such as a photographing apparatus.
The photographed data includes information on pixels included in the photographed image. In step S10, the photographed image is preprocessed to calculate the gradients of the pixels.
When a gradient for all the pixels of the image photographed through step S10 is calculated, the apparatus selects a pixel as a boundary candidate pixel among the pixels included in the photographed image, the calculated gradient having a value larger than a predetermined threshold value Step S20 is performed.
The gradients of the pixels included in the image of the air have various directions and magnitudes due to various materials scattered in the air. At this time, in the case of a specific fibrous substance which is not generally scattered in the air such as asbestos fibers, the gradient may vary greatly at the interface between the substance and the air. Therefore, by setting a predetermined threshold value and comparing the value of the gradient of the pixels, a pixel whose fibrous substance is present and judged to be a boundary with air is set as the boundary candidate pixel, so that the modeling of the fibrous substance is started .
Thereafter, the apparatus groups the pixels of the boundary candidate pixel and the pixels adjacent to the boundary candidate pixel so that the calculated gradient direction is less than a predetermined error range and the gradient direction of the set boundary candidate pixel so that the image formed by the grouped pixels is converted into the first fibrous substance A modeling step S30 is performed.
The fibrous material scattered in the air will scatter in an irregular shape, so that the irregular shape basically includes a straight shape. On the other hand, in the case of fibers scattered in the air, the direction of the gradient has a consistent characteristic along the boundary. Using this algorithm, in step S30, by selecting and grouping pixels having the same directionality as the pixel set as the boundary candidate pixel, that is, the direction of the gradient is less than the predetermined error range and the direction of the gradient of the boundary candidate pixel, And the asbestos fibers are detected.
In the present invention, the first fibrous substance means a fiber which is a unit fiber material at the time of modeling an amorphous fibrous substance as mentioned above, and which is detected as a result of linear modeling. That is, upon modeling the first fibrous material, the device is modeled as a first fibrous material by grouping the pixels into a rectangle (e.g., a rectangle).
When step S30 is performed, a rectangular (straight) fibrous material is modeled as a plurality of first fibrous materials. The first fibrous materials will be modeled as straight fibers having various sizes and widths.
There is an effect that the fibrous material scattered in the air can be modeled using only the gradients of the pixels included in the image of the air of the target environment simply through steps S10 through S30 of FIG. Accordingly, there is an effect that the concentration of asbestos fibers can be predicted based on the detection of fibrous materials scattered in air in real time with an accurate probability, without manual operation or extraction of samples.
On the other hand, in order to calculate the gradient value in step S10, the apparatus first converts the photographed image into a grayscale image, performs a normalization preprocess on the converted image, and preprocesses the gradient so that each pixel can be calculated Can be performed. In addition, any processing method can be used as long as it is an image processing process capable of performing the function of step S10.
Further, as mentioned in the embodiment corresponding to FIG. 2 to be described below, when all the asbestos fibers are modeled and the detection of the fibrous material scattered in the air is completed, the apparatus uses the amount of the fibrous material modeled, A step of calculating a concentration of a predetermined proportion of the concentration of the fibrous material to the concentration of asbestos scattered in the target environment may be further performed. In calculating the asbestos concentration, the detecting fibrous material will be understood to include a second fibrous material, which will be referred to in the description of Fig. 2, in addition to the first fibrous material described in Fig.
On the other hand, the asbestos fibers, which are the object of the detection of fibrous materials, especially fibrous materials, are much larger than the probability that they exist in the form of a straight line, as mentioned above. At this time, detection can be duplicated when only the first fibrous substance is detected, so it is necessary to further improve the detection accuracy of the fibrous substance scattered in the air by modeling the irregular fibrous substance. A description thereof is shown in Fig.
Referring to FIG. 2, the apparatus first performs the step of modeling the first fibrous material, as mentioned in the description of step S30 in FIG. Thereafter, the apparatus performs a step S40 of connecting at least one modeled first fibrous material adjacent to each other to model them as one second fibrous material.
The first fibrous material includes, for example, a linear (rectangular) modeling image as mentioned above, and in the case of an amorphous form, a plurality of linear fiber fragments can basically be represented as connected. When a plurality of first fibrous materials, which are connected to each other by being connected to each other to be recognized as one fiber, are connected to each other through the step S40 and are modeled as one second fibrous material, redundant detection of a plurality of asbestos fibers is prevented There is an effect that can be done.
Specifically, in the case of performing the step S40, for example, among the first fibrous materials, when the first fibrous materials sharing the pixels included in the first fibrous material are present, the first fibrous materials sharing the pixels are connected Can be modeled with a second fibrous material. Linear fibrous materials will share at least one pixel in order to be connected.
The second fibrous material in which the first fibrous materials are connected may include a state in which straight fibrous pieces are connected to each other in a curved form, and a state in which the first fibrous material is branched in a branched form.
To this end, the apparatus can be modeled as a second fibrous material of the fibrous material type branched into branches by connecting each first fibrous material when the number of the first fibrous material sharing one pixel is three or more.
Alternatively, when the first fibrous material is connected in a branched form, that is, in the form of a branch, the direction of the gradient can largely vary among adjacent first fibrous materials. That is, it is determined that, among the gradients of the pixels included in the first fibrous materials sharing the pixel, the direction of the gradation of the pixels included in the first fibrous materials adjacent to the shared pixel changes beyond a predetermined threshold angle , The first fibrous material may be modeled as a second fibrous material that is connected to a type of fibrous material branched into a branched form on the basis of the shared pixels.
Thus, by connecting the first fibrous materials to the second fibrous material according to the performance criteria of step S40 of various examples, it is possible to accurately detect the fibrous material scattered in the air without detecting duplication.
3 is a block diagram of a configuration of an apparatus for detecting a fibrous substance in air in real time through an image processing program according to an embodiment of the present invention. In the following description, the description of the parts overlapping with the description of FIG. 1 and FIG. 2 will be omitted.
3, an apparatus 10 for real-time detection of airborne fibrous substances through an image processing program according to an embodiment of the present invention includes a pre-processing unit 11, a reference
Meanwhile, the reference
4-6 are sample drawings for illustrating a specific embodiment for modeling fibrous material according to an embodiment of the present invention. In the following description, portions overlapping with the description of Figs. 1 to 3 will be omitted.
Referring to FIG. 4, by selecting the above-mentioned
Referring to FIG. 5, the first
Meanwhile, referring to FIG. 6, a detailed modeling process at the time of modeling the fibrous material shown in FIGS. 4 and 5 is shown. When one first
FIGS. 7 to 8 are graphs showing experimental results of modeling fibrous materials according to an embodiment of the present invention. FIG. Specifically, FIGS. 7 and 8 illustrate the results of detecting asbestos fibers using a conventional PCM, that is, a method of directly detecting a fibrous substance by the naked eye, through a microscope, and a method of detecting a fibrous substance according to an embodiment of the present invention And comparing the result of detecting the fibrous substance by automatically modeling it.
Referring to FIG. 7, there is shown a graph showing a comparison between phase difference microscopy and detection results obtained by automatic modeling according to an embodiment of the present invention, in accordance with the content of spallite having a fibrous shape similar to that of asbestos fibers .
As shown in the graph of FIG. 7, it can be confirmed that the detection rate is very high, 0.997 and 0.999, depending on the content of the hemp stock. Accordingly, the detection rate of the fibrous material according to the embodiment of the present invention is not greatly different from that of the phase difference microscope. Thus, it can be confirmed that the effect of the present invention is excellent.
On the other hand, in FIG. 8, there is shown a graph of the correlation between the phase difference microscope and detection results through automatic modeling, which is different from the embodiment of the present invention.
As can be seen from FIG. 8, according to the phase difference microscope and the method of automatically modeling and detecting the fibrous material according to the embodiment of the present invention, the fibrous material is automatically correlated with 0.998, Modeling method can be expected to be an alternative to the phase difference microscope method.
The functions of the method for real-time detection of airborne fibrous material through the image processing program according to the embodiment of the present invention described above can be applied to applications installed in the user terminal (such as a program basically installed in the terminal, ) And may be executed by an application (i.e., a program) that the user has installed directly on the user terminal via an application provision server, such as an application store server, an application, or a web server associated with the service . In this sense, the function of the method for real-time detection of fibers in air through the image processing program according to the above-described embodiment of the present invention is basically installed in the user terminal or implemented as an application (i.e., program) directly installed by the user, And recorded in a computer-readable recording medium such as a terminal.
Such a program may be recorded on a recording medium that can be read by a computer and executed by a computer so that the above-described functions can be executed.
As described above, in order to execute the function of the method for real-time detection of airborne fibrous substances through the image processing program according to each embodiment of the present invention, the above-mentioned program may be stored in the C, C ++, JAVA , A machine language, and the like.
The code may include a function code related to a function or the like that defines the functions described above and may include an execution procedure related control code necessary for the processor of the computer to execute the functions described above according to a predetermined procedure.
In addition, such code may further include memory reference related code as to what additional information or media needed to cause the processor of the computer to execute the aforementioned functions should be referenced at any location (address) of the internal or external memory of the computer .
In addition, when a processor of a computer needs to communicate with any other computer or server, etc., to perform the above-described functions, the code may be stored in a computer's communication module (e.g., a wired and / ) May be used to further include communication related codes such as how to communicate with any other computer or server in the remote, and what information or media should be transmitted or received during communication.
In addition, a functional program for implementing the present invention, a code and a code segment associated with the functional program, and the like are provided to programmers in the technical field of the present invention in consideration of a system environment of a computer that reads a recording medium and executes a program Lt; RTI ID = 0.0 > and / or < / RTI >
The computer-readable recording medium on which the above-described program is recorded includes ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical media storage, and the like.
In addition, the computer-readable recording medium on which the above-described program is recorded may be distributed to a network-connected computer system so that computer-readable codes can be stored and executed in a distributed manner. In this case, one or more of the plurality of distributed computers may execute some of the functions presented above and send the results of the execution to one or more of the other distributed computers, The computer may also perform some of the functions described above and provide the results to other distributed computers as well.
In particular, a computer-readable recording medium storing an application, which is a program for executing a function of a method for real-time detection of airborne fibrous substances through an image processing program according to each embodiment of the present invention, ), A storage medium (e.g., a hard disk, etc.) included in an application provider server such as a web server associated with the application or the service, or an application providing server itself.
A computer capable of reading a recording medium on which an application, which is a program for executing a function of a method for real-time detection of fibrous substances in air through an image processing program according to each embodiment of the present invention, In addition, it may include a mobile terminal such as a smart phone, a tablet PC, a PDA (Personal Digital Assistants), and a mobile communication terminal, and should be interpreted as all devices capable of computing.
Further, a computer for reading a recording medium on which an application, which is a program for executing a function of a method for real-time detection of fibrous substances in air through an image processing program according to an embodiment of the present invention, is read is a smart phone, a tablet PC, a PDA Digital assistants) and a mobile communication terminal, the application may be downloaded from the application providing server to the general PC and installed in the mobile terminal through the synchronization program.
While the present invention has been described in connection with what is presently considered to be the most practical and preferred embodiments, it is to be understood that the invention is not limited to the disclosed embodiments. That is, within the scope of the present invention, all of the components may be selectively coupled to one or more of them. In addition, although all of the components may be implemented as one independent hardware, some or all of the components may be selectively combined to perform a part or all of the functions in one or a plurality of hardware. As shown in FIG. The codes and code segments constituting the computer program may be easily deduced by those skilled in the art. Such a computer program is stored in a computer-readable storage medium, readable and executed by a computer, thereby realizing an embodiment of the present invention. As a storage medium of the computer program, a magnetic recording medium, an optical recording medium, or the like can be included.
It is also to be understood that the terms such as " comprises, " " comprising, " or " having ", as used herein, mean that a component can be implanted unless specifically stated to the contrary. But should be construed as including other elements. All terms, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, unless otherwise defined. Commonly used terms, such as predefined terms, should be interpreted to be consistent with the contextual meanings of the related art, and are not to be construed as ideal or overly formal, unless expressly defined to the contrary.
The foregoing description is merely illustrative of the technical idea of the present invention and various changes and modifications may be made by those skilled in the art without departing from the essential characteristics of the present invention. Therefore, the embodiments disclosed in the present invention are not intended to limit the scope of the present invention but to limit the scope of the technical idea of the present invention. The scope of protection of the present invention should be construed according to the following claims, and all technical ideas within the scope of equivalents thereof should be construed as falling within the scope of the present invention.
Claims (10)
Pre-processing the photographed image to produce a gradient of pixels included in the image of the target environment for detection of fibrous material in the air;
Selecting a pixel as a boundary candidate pixel among the pixels included in the photographed image, wherein the calculated gradient is greater than a preset threshold value; And
And modeling the pixels of the boundary candidate pixel with the first fibrous material by grouping the pixels of the boundary candidate pixel and the calculated direction of the calculated gradient with pixels of the boundary candidate pixel that are less than a predetermined error range, / RTI > a method for the real-time detection of fibrous material in air through a membrane.
Wherein modeling with the first fibrous material comprises:
Wherein the pixel of the boundary candidate pixel is modeled as a first fibrous material by approximating a pixel having a direction of the calculated gradient that is less than a predetermined error range from the boundary candidate pixel, A method for detecting a fibrous material in real time.
After modeling with the first fibrous material,
And connecting at least one of the modeled first fibrous materials adjacent to each other to the first fibrous material to form a second fibrous material.
Wherein modeling the second fibrous material comprises:
The first fibrous materials sharing the pixel are modeled by connecting the first fibrous materials in the presence of the first fibrous materials sharing the pixels included in the first fibrous materials among the first fibrous materials, A method for real-time detection of airborne fibrous material through an image processing program.
Wherein modeling the second fibrous material comprises:
Characterized in that said second fibrous material in the form of a branched fibrous material is modeled as a branched fibrous material when the number of the first fibrous materials sharing the pixel is 3 or more. .
Wherein modeling the second fibrous material comprises:
When a direction of a gradient of a pixel included in first fibrous materials adjacent to the shared pixel among the gradients of pixels included in the first fibrous materials sharing the pixel is changed beyond a predetermined threshold angle, Characterized by modeling the second fibrous material of the fibrous material type branched in branched form on the basis of the shared pixel. ≪ Desc / Clms Page number 13 >
The pre-
Converting the photographed image into a grayscale image; And
And performing a normalization preprocessing on the converted image so as to calculate a gradient of each pixel. A method for real time detection of airborne fibrous material by using an image processing program.
After modeling with the first fibrous material,
Calculating a concentration of a predetermined proportion of the concentration of the first fibrous material to the concentration of asbestos scattered in the target environment using the amount of the modeled first fibrous material, A method for real time detection of fibrous material in air through a treatment program.
A reference pixel selecting unit that selects, as a boundary candidate pixel, a pixel among the pixels included in the photographed image, wherein the calculated gradient value is larger than a preset threshold value; And
And a fiber modeling unit for grouping the pixels of the boundary candidate pixel and the pixels of which the calculated direction of the calculated gradient is less than a predetermined error range with respect to the boundary candidate pixel to model the same with the first fibrous material An apparatus for real-time detection of airborne fibrous material through a treatment program.
Pre-processing the photographed image to produce a gradient of pixels included in the image of the target environment for detection of fibrous material in the air;
Selecting a pixel as a boundary candidate pixel among the pixels included in the photographed image, wherein the calculated gradient is greater than a preset threshold value; And
And modeling the pixels of the boundary candidate pixel with the first fibrous material by grouping the pixels of the boundary candidate pixel and the calculated direction of the calculated gradient with pixels of the boundary candidate pixel that are less than a predetermined error range, Readable recording medium having recorded thereon a program for performing real-time detection of a fibrous substance in air through a recording medium.
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| KR20140195383 | 2014-12-31 | ||
| KR1020140195383 | 2014-12-31 |
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| KR20160083784A true KR20160083784A (en) | 2016-07-12 |
| KR101674802B1 KR101674802B1 (en) | 2016-11-10 |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102039016B1 (en) * | 2018-09-13 | 2019-10-31 | 황준철 | Concentration monitoring system and method |
| WO2020210870A1 (en) * | 2019-04-17 | 2020-10-22 | Swinburne University Of Technology | A system and method for asbestos identification |
| CN114985150A (en) * | 2022-08-02 | 2022-09-02 | 山东大拇指喷雾设备有限公司 | Visual perception-based control method for accurate spraying of spraying machine |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20210036168A (en) | 2019-09-25 | 2021-04-02 | 에스케이하이닉스 주식회사 | Electronic Device |
-
2015
- 2015-03-31 KR KR1020150045293A patent/KR101674802B1/en active IP Right Grant
Non-Patent Citations (3)
| Title |
|---|
| Automated Counting of Airborne Asbestos Fibers by a High-Throughput Microscopy (HTM) Method(sensors, 2011)* * |
| Extracting Straight Lines(Burns JB, IEEE TPAMI, 1986)* * |
| IMAGE PROCESSING Handbook 4TH_Chapter4(2002)* * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102039016B1 (en) * | 2018-09-13 | 2019-10-31 | 황준철 | Concentration monitoring system and method |
| WO2020210870A1 (en) * | 2019-04-17 | 2020-10-22 | Swinburne University Of Technology | A system and method for asbestos identification |
| GB2596967A (en) * | 2019-04-17 | 2022-01-12 | Univ Swinburne Technology | A system and method for asbestos identification |
| GB2596967B (en) * | 2019-04-17 | 2023-09-13 | Univ Swinburne Technology | A system and method for asbestos identification |
| CN114985150A (en) * | 2022-08-02 | 2022-09-02 | 山东大拇指喷雾设备有限公司 | Visual perception-based control method for accurate spraying of spraying machine |
| CN114985150B (en) * | 2022-08-02 | 2022-11-01 | 山东大拇指喷雾设备有限公司 | Visual perception-based control method for accurate spraying of spraying machine |
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| KR101674802B1 (en) | 2016-11-10 |
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