CN111242964A - Contour determination method and device for GDV energy diagram - Google Patents
Contour determination method and device for GDV energy diagram Download PDFInfo
- Publication number
- CN111242964A CN111242964A CN201911337800.1A CN201911337800A CN111242964A CN 111242964 A CN111242964 A CN 111242964A CN 201911337800 A CN201911337800 A CN 201911337800A CN 111242964 A CN111242964 A CN 111242964A
- Authority
- CN
- China
- Prior art keywords
- image
- contour
- gray
- gdv
- acquiring
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000010586 diagram Methods 0.000 title claims abstract description 52
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000001914 filtration Methods 0.000 claims description 16
- 238000012163 sequencing technique Methods 0.000 claims description 13
- 230000003044 adaptive effect Effects 0.000 claims description 9
- 230000000877 morphologic effect Effects 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 230000010339 dilation Effects 0.000 claims description 2
- 238000001514 detection method Methods 0.000 description 6
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000005672 electromagnetic field Effects 0.000 description 2
- 210000000056 organ Anatomy 0.000 description 2
- 230000002996 emotional effect Effects 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000005381 potential energy Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 230000004936 stimulating effect Effects 0.000 description 1
- 230000000638 stimulation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration using local operators
- G06T5/30—Erosion or dilatation, e.g. thinning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Geometry (AREA)
- Image Analysis (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The invention discloses a method for determining the outline of a GDV energy diagram, which comprises the following steps: collecting a GDV finger energy diagram; converting the GDV finger energy diagram into a gray level image, and then carrying out local self-adaptive binarization processing and expansion processing on the image to obtain a gray level image with uniformly distributed gray levels and communicated domains; acquiring a set of points of the contour of the gray image, and calculating an image moment according to the set of points of the contour so as to obtain the area of each contour; the method comprises the steps of obtaining the two largest contours in the areas of all contours, wherein the two largest contours are the inner contour and the outer contour of a GDV finger energy diagram respectively, and the problem of the requirement for obtaining the inner contour and the outer contour of the finger energy diagram at present is solved.
Description
Technical Field
The application relates to the field of image processing, in particular to a method for determining the outline of a GDV energy map, and also relates to a device for determining the outline of the GDV energy map.
Background
Biophotonic studies now show that the human body can spontaneously emit electrons and photons, producing a glow that is invisible to the naked eye. Scientists regard electrons and photons emitted by the human body as the representation of the energy of the human body; the energy generated by the human body spontaneously forms a human body energy field, and ten finger energies of the human body can be detected by using the energy field detection equipment. After the finger energy image is collected, calculating the inner contour and the outer contour of the energy image, fitting the data of the inner contour and the outer contour to form an ellipse of the inner contour and the outer contour, and further calculating the specific attribute characteristics of the inner contour and the outer contour, such as contour length, contour radius, ellipse size, inner circle radius and the like; through the analysis of the attribute characteristics of the inner contour and the outer contour, relevant indexes of energy in the finger energy image, such as entropy coefficients, form coefficients and the like, can be obtained, and further the health conditions of the human body, including emotional stress, organ balance and the like, can be evaluated. Therefore, the acquisition of the inner contour and the outer contour of the finger energy map is crucial.
Disclosure of Invention
The application provides a method for determining the outline of a GDV energy diagram, which solves the problem of the requirement for acquiring the inner outline and the outer outline of a finger energy diagram at present.
The application provides a method for determining the outline of a GDV energy diagram, which comprises the following steps:
collecting a GDV finger energy diagram;
converting the GDV finger energy diagram into a gray level image, and then carrying out local self-adaptive binarization processing and expansion processing on the image to obtain a gray level image with uniformly distributed gray levels and communicated domains;
acquiring a set of points of the contour of the gray image, and calculating an image moment according to the set of points of the contour so as to obtain the area of each contour;
and acquiring two maximum contours in the areas of all the contours, wherein the two maximum contours are respectively an inner contour and an outer contour of the GDV finger energy diagram.
Preferably, converting the GDV finger energy map into a grayscale image includes:
decomposing the GDV finger energy map into R, G, B three channel images; r (x, y), G (x, y), B (x, y) respectively represent pixel values on the R, G, B channel image;
calculating the mean value m of R (x, y), G (x, y) and B (x, y)
m＝(R(x,y)+G(x,y)+B(x,y))/3；
Assign the mean m to the original image I (x, y)
I(x,y)＝m；
And finishing the conversion of the GDV finger energy diagram into a gray level image.
Preferably, before the step of performing the local adaptive binarization processing and the dilation processing on the image, the method further includes: energy pixels smaller than the threshold value of the gray scale are filtered out, specifically,
traversing the grayscale image;
calculating the sum I of the pixel gray values of which the pixel gray values are greater than 0 and the number S of pixels of which the pixel gray values are greater than 0 in the gray image;
calculating a gray-scale average intensity of the gray-scale image,
ICP＝I/S
and traversing the gray level image, and when the pixel value P is smaller than the image gray level average intensity ICP, setting the pixel value P to be 0, and finishing filtering of energy pixels smaller than the gray level threshold value.
Preferably, the local adaptive binarization processing and the expansion processing are performed on the image to obtain a gray level image with uniformly distributed gray levels and connected domains, and the method includes:
calculating the average value of each pixel field, wherein the field size of each pixel point is blocksize, sum is the sum of pixel values in all the fields, and avg is the average value;
if blocksize is 3, then
sum＝I(x-1,y-1)+I(x-1,y)+…+I(x+1,y)+I(x+1,y+1)
avg＝sum/3*3；
I (x, y) is the processed target pixel point, avg is the threshold value, the local adaptive threshold value function is obtained,
traversing the whole gray level image through the steps to complete local self-adaptive binarization processing of the image;
and connecting the broken connected domains of the gray level images by adopting a morphological expansion method, and increasing the transverse direction and the vertical direction of the extended connected domain to finish the expansion processing of the gray level images.
Preferably, acquiring a set of points of the contour of the grayscale image includes:
and acquiring a set of points of the contour of the gray-scale image by adopting a findContours method of an OpenCV function.
Preferably, two contours with the largest area are obtained, and the two contours with the largest area are respectively the inner contour and the outer contour of the GDV finger energy diagram, and the method comprises the following steps:
sequencing the areas of the profiles according to a bubble sequencing method, acquiring two profiles with the largest area of the profiles, and filtering out other profiles;
the two outlines with the largest areas are respectively the inner outline and the outer outline of the GDV finger energy diagram.
The present application also provides a contour determination device for a GDV energy map, comprising:
the acquisition unit is used for acquiring a GDV finger energy diagram;
the gray processing unit is used for converting the GDV finger energy diagram into a gray image, and then carrying out local self-adaptive binarization processing and expansion processing on the image to obtain a gray image with uniformly distributed gray and communicated domains;
the area determining unit is used for acquiring a set of points of the contour of the gray image, calculating an image moment according to the set of points of the contour, and further obtaining the area of each contour;
and the contour determining unit is used for acquiring two maximum contours in the areas of all the contours, wherein the two maximum contours are respectively an inner contour and an outer contour of the GDV finger energy diagram.
Preferably, the method further comprises the following steps:
and the filtering unit is used for filtering the energy pixels smaller than the gray threshold.
Preferably, the area determination unit includes:
and the collective acquisition subunit of the points of the contour is used for acquiring a set of the points of the contour of the gray-scale image by adopting a findContours method of an OpenCV function.
Preferably, the contour determination unit includes:
the sequencing subunit is used for sequencing the areas of the profiles according to a bubble sequencing method, acquiring two profiles with the largest areas of the profiles, and filtering other profiles;
and the two contours with the largest areas are respectively the inner contour and the outer contour of the GDV finger energy diagram.
The application provides a GDV energy diagram's profile determination method, through to GDV finger energy diagram carry out grey scale processing, local self-adaptation binarization processing, inflation processing after, acquire the area of each profile of grey scale image, two profiles that the area is the biggest are the interior profile and the outline of GDV finger energy diagram respectively, solve the demand problem of present acquisition to the interior profile and the outline of finger energy diagram.
Drawings
FIG. 1 is a flow chart of a GDV energy diagram direction line calculation method provided by the present application;
FIG. 2 is an original drawing of a GDV finger energy map according to the present application;
FIG. 3 is a grayscale image of a GDV finger energy map to which the present application relates;
FIG. 4 is a graph of the filtered gray scale image results of the present application;
fig. 5 is a result diagram after the local binarization processing of the grayscale image according to the present application;
fig. 6 is a diagram showing a result of a gray scale image expansion process according to the present application;
fig. 7 is a diagram of a set of points of an outline of a gray-scale image according to the present application;
FIG. 8 is a resultant graph of the profile of a gray scale image to which the present application relates;
FIG. 9 is a graph of the inner and outer contours of a GDV finger energy map according to the present application;
fig. 10 is a schematic diagram of a GDV energy diagram direction line calculation device provided in the present application.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of implementation in many different ways than those herein set forth and of similar import by those skilled in the art without departing from the spirit of this application and is therefore not limited to the specific implementations disclosed below.
Fig. 1 is a schematic flow chart of a method for determining a contour of a GDV energy map provided by the present application, and the method provided by the present application is described in detail below with reference to fig. 1.
And step S101, acquiring a GDV finger energy diagram.
The GDV finger energy map is also called a gas discharge energy map, which is an energy image sample of ten fingers collected by an energy field detection device. An original image of the GDV finger energy map is collected, as shown in fig. 2.
Modern biophotonic studies have shown that the human body can spontaneously emit electrons and photons, producing a glow that is invisible to the naked eye. Scientists regard electrons and photons emitted by the human body as the representation of the energy of the human body; the energy generated spontaneously by the human body constitutes the energy field of the human body.
Energy field detection devices are based on photon, electron emission by stimulating the surface of an object. This stimulation originates from short electrical pulses. When an object is subjected to an electromagnetic field, it is first charged and to some extent photons are separated from the object surface, a process called photoelectron emission. The emitted particles are accelerated in an electromagnetic field, and an electron avalanche is generated on the surface of a dielectric plate (glass). This process is called gas discharge. This discharge results in light of the excitable molecules in the surrounding gas. This light is detected by the energy field detection device. The voltage pulses stimulate the emission of optical electrons and affect the emission of gas electrons. Energy field sensing devices measure the electron density of human systems and organs, as well as the characteristics of the excited electron current. These electron densities are the fundamental basis for physiological energy, and so it can be said that energy field detection devices can measure potential energy reserves of the human body.
And S102, converting the GDV finger energy diagram into a gray level image, and then carrying out local self-adaptive binarization processing and expansion processing on the image to obtain a gray level image with uniformly distributed gray levels and communicated domains. There are four main methods for image graying, such as: component, maximum, average, and weighted average. The method uses an average value method to carry out image graying operation, namely, three-component brightness in a color image in an RGB color space is averaged to obtain a gray value. Specifically, the following is:
decomposing the GDV finger energy map into R, G, B three channel images; r (x, y), G (x, y), B (x, y) respectively represent pixel values on the R, G, B channel image;
calculating the mean value m of R (x, y), G (x, y) and B (x, y)
m＝(R(x,y)+G(x,y)+B(x,y))/3；
Assign the mean m to the original image I (x, y)
I(x,y)＝m；
And converting the GDV finger energy diagram into a gray image, wherein the gray image is shown in figure 3.
Then, filtering out energy pixels smaller than a gray threshold, and in a GDV energy image acquired by energy field detection equipment, specifying energy pixels with a pixel gray value larger than a threshold ICP as effective energy, so as to perform pixel filtering operation on the finger energy image, and the specific steps are as follows:
(1) traversing finger energy map grayscale images
(2) Calculating the sum I of all pixel gray values with the pixel gray value larger than 0 in the gray image
(3) Calculating the number S of pixels with the gray value of more than 0 in the gray image
(4) Calculating the average intensity of the gray scale of the finger energy map:
ICP＝I/S
(5) traversing the finger energy map gray level image, when the pixel value P is smaller than the image gray level average intensity ICP, setting the pixel value P to 0, and completing the filtering of the energy pixels smaller than the gray level threshold, the effect of which is shown in fig. 4.
And then, local self-adaptive binarization processing is carried out on the gray meaning image which is filtered by the energy pixels smaller than the gray threshold, the gray of the image may not be uniformly distributed due to the influence of illumination, and the effect obtained by adopting a single threshold segmentation method is not good. Adaptive thresholding is a local method whose principle is to calculate a threshold from the neighborhood of each pixel, comparing the value of each pixel to the average of the neighborhoods.
Specifically, (1) calculating an average value of each pixel field, wherein the field size of a pixel point is blocksize, sum is the sum of pixel values in all the fields, and avg is the average value;
if blocksize is 3, the field schematic diagram is as follows
| I(x-1,y-1) | I(x-1,y) | I(x-1,y+1) |
| I(x,y-1) | I(x,y) | I(x,y+1) |
| I(x+1,y-1) | I(x+1,y) | I(x+1,y+1) |
sum＝I(x-1,y-1)+I(x-1,y)+…+I(x+1,y)+I(x+1,y+1)
avg＝sum/3*3；
(2) Obtaining a local self-adaptive threshold function, wherein I (x, y) is a processed target pixel point, and avg is a threshold;
(3) repeating the steps (1) and (2) and traversing the whole image;
the result after the image binarization processing is shown in fig. 5.
After the finger energy image is subjected to local adaptive binarization, a phenomenon that connected domains are broken exists in the image, so that the broken connected domains are connected by adopting a morphological expansion method, the horizontal direction and the vertical direction of the extended connected domains are increased, and a processing result is shown in fig. 6.
Step S103, acquiring a set of points of the contour of the gray image, and calculating an image moment according to the set of points of the contour to further obtain the area of each contour.
And acquiring a set of points of the contour of the gray-scale image by adopting a findContours method of an OpenCV function. The results are shown in FIG. 7.
In fig. 7, the GDV energy image includes three contours, one of which is located at the lower half of the energy distribution diagram and has a smaller area, as enclosed by the rectangular box in fig. 8. And then, calculating a closed region (contour) by using the image moment so as to obtain the area of each contour.
And step S104, acquiring two contours with the maximum area, wherein the two contours with the maximum area are respectively an inner contour and an outer contour of the GDV finger energy diagram.
Bubble sorting is a simpler sorting algorithm in the field of computer science. It repeatedly walks through the columns of elements to be sorted, compares two adjacent elements in turn, and swaps them if their order (e.g., from large to small, with the first letter from a to Z) is wrong. The task of walking through the elements is repeated until no more neighboring elements need to be swapped, i.e., the element column has been sorted to completion.
In the previous step, the areas of all the contours in the GDV finger energy map have been calculated, the two contours with the largest areas are selected according to the bubble sorting algorithm, and the remaining contours are filtered out, with the result shown in fig. 9. And finishing the determination of the inner contour and the outer contour of the GDV finger energy diagram.
The present application also provides a contour determination apparatus 1000 of a GDV energy map, as shown in fig. 10, including:
the acquisition unit 1010 is used for acquiring a GDV finger energy diagram;
a gray processing unit 1020, configured to convert the GDV finger energy map into a gray image, and perform local adaptive binarization processing and expansion processing on the image to obtain a gray image with uniformly distributed gray levels and connected domains;
an area determining unit 1030, configured to obtain a set of points of the contour of the grayscale image, and calculate an image moment according to the set of points of the contour, so as to obtain an area of each contour;
the outline determining unit 1040 is configured to obtain two outlines with the largest areas of all outlines, where the two outlines with the largest areas are the inner outline and the outer outline of the GDV finger energy map, respectively.
Preferably, the method further comprises the following steps:
and the filtering unit is used for filtering the energy pixels smaller than the gray threshold.
Preferably, the area determination unit includes:
and the collective acquisition subunit of the points of the contour is used for acquiring a set of the points of the contour of the gray-scale image by adopting a findContours method of an OpenCV function.
Preferably, the contour determination unit includes:
the sequencing subunit is used for sequencing the areas of the profiles according to a bubble sequencing method, acquiring two profiles with the largest areas of the profiles, and filtering other profiles;
and the two contours with the largest areas are respectively the inner contour and the outer contour of the GDV finger energy diagram.
The application provides a GDV energy diagram's profile determination method, through to GDV finger energy diagram carry out grey scale processing, local self-adaptation binarization processing, inflation processing after, acquire the area of each profile of grey scale image, two profiles that the area is the biggest are the interior profile and the outline of GDV finger energy diagram respectively, solve the demand problem of present acquisition to the interior profile and the outline of finger energy diagram.
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the invention.
Claims (10)
1. A method for determining the contour of a GDV energy map, comprising:
collecting a GDV finger energy diagram;
converting the GDV finger energy diagram into a gray level image, and then carrying out local self-adaptive binarization processing and expansion processing on the image to obtain a gray level image with uniformly distributed gray levels and communicated domains;
acquiring a set of points of the contour of the gray image, and calculating an image moment according to the set of points of the contour so as to obtain the area of each contour;
and acquiring two maximum contours in the areas of all the contours, wherein the two maximum contours are respectively an inner contour and an outer contour of the GDV finger energy diagram.
2. The method of claim 1, wherein converting the GDV finger energy map into a grayscale image comprises:
decomposing the GDV finger energy map into R, G, B three channel images; r (x, y), G (x, y), B (x, y) respectively represent pixel values on the R, G, B channel image;
calculating the mean value m of R (x, y), G (x, y) and B (x, y)
m＝(R(x,y)+G(x,y)+B(x,y))/3；
Assign the mean m to the original image I (x, y)
I(x,y)＝m；
And finishing the conversion of the GDV finger energy diagram into a gray level image.
3. The method according to claim 1, wherein the step of performing the local adaptive binarization processing and the dilation processing on the image is preceded by: energy pixels smaller than the threshold value of the gray scale are filtered out, specifically,
traversing the grayscale image;
calculating the sum I of the pixel gray values of which the pixel gray values are greater than 0 and the number S of pixels of which the pixel gray values are greater than 0 in the gray image;
calculating a gray-scale average intensity of the gray-scale image,
ICP＝I/S
and traversing the gray level image, and when the pixel value P is smaller than the image gray level average intensity ICP, setting the pixel value P to be 0, and finishing filtering of energy pixels smaller than the gray level threshold value.
4. The method according to claim 1, wherein the local adaptive binarization processing and the expansion processing are performed on the image to obtain a gray image with uniformly distributed gray levels and connected domains, and the method comprises the following steps:
calculating the average value of each pixel field, wherein the field size of each pixel point is blocksize, sum is the sum of pixel values in all the fields, and avg is the average value;
if blocksize is 3, then
sum＝I(x-1,y-1)+I(x-1,y)+…+I(x+1,y)+I(x+1,y+1)
avg＝sum/3*3；
I (x, y) is the processed target pixel point, avg is the threshold value, the local adaptive threshold value function is obtained,
traversing the whole gray level image through the steps to complete local self-adaptive binarization processing of the image;
and connecting the broken connected domains of the gray level images by adopting a morphological expansion method, and increasing the transverse direction and the vertical direction of the extended connected domain to finish the expansion processing of the gray level images.
5. The method of claim 1, wherein obtaining the set of points of the contour of the grayscale image comprises:
and acquiring a set of points of the contour of the gray-scale image by adopting a findContours method of an OpenCV function.
6. The method of claim 1, wherein obtaining two contours with the largest area, which are the inner contour and the outer contour of the GDV finger energy map, respectively, comprises:
sequencing the areas of the profiles according to a bubble sequencing method, acquiring two profiles with the largest area of the profiles, and filtering out other profiles;
the two outlines with the largest areas are respectively the inner outline and the outer outline of the GDV finger energy diagram.
7. A contour determination device for a GDV energy map, comprising:
the acquisition unit is used for acquiring a GDV finger energy diagram;
the gray processing unit is used for converting the GDV finger energy diagram into a gray image, and then carrying out local self-adaptive binarization processing and expansion processing on the image to obtain a gray image with uniformly distributed gray and communicated domains;
the area determining unit is used for acquiring a set of points of the contour of the gray image, calculating an image moment according to the set of points of the contour, and further obtaining the area of each contour;
and the contour determining unit is used for acquiring two maximum contours in the areas of all the contours, wherein the two maximum contours are respectively an inner contour and an outer contour of the GDV finger energy diagram.
8. The apparatus of claim 7, further comprising:
and the filtering unit is used for filtering the energy pixels smaller than the gray threshold.
9. The apparatus of claim 7, wherein the area determination unit comprises:
and the collective acquisition subunit of the points of the contour is used for acquiring a set of the points of the contour of the gray-scale image by adopting a findContours method of an OpenCV function.
10. The apparatus of claim 7, wherein the contour determination unit comprises:
the sequencing subunit is used for sequencing the areas of the profiles according to a bubble sequencing method, acquiring two profiles with the largest areas of the profiles, and filtering other profiles;
and the two contours with the largest areas are respectively the inner contour and the outer contour of the GDV finger energy diagram.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911337800.1A CN111242964B (en) | 2019-12-23 | 2019-12-23 | Contour determination method and device for GDV energy map |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911337800.1A CN111242964B (en) | 2019-12-23 | 2019-12-23 | Contour determination method and device for GDV energy map |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111242964A true CN111242964A (en) | 2020-06-05 |
| CN111242964B CN111242964B (en) | 2023-11-03 |
Family
ID=70865597
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911337800.1A Active CN111242964B (en) | 2019-12-23 | 2019-12-23 | Contour determination method and device for GDV energy map |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111242964B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105447851A (en) * | 2015-11-12 | 2016-03-30 | 刘新辉 | Glass panel sound hole defect detection method and system |
| CN107093167A (en) * | 2017-03-07 | 2017-08-25 | 北京环境特性研究所 | A kind of self-adaptive solution algorithm for ultraviolet imagery system |
| CN107480662A (en) * | 2017-10-11 | 2017-12-15 | 青岛华晶生物技术有限公司 | A kind of mould image-recognizing method and its device |
| CN108596891A (en) * | 2018-04-23 | 2018-09-28 | 中国计量大学 | A kind of method of counting towards multiple types mixing silk cocoon |
| CN109712125A (en) * | 2018-12-19 | 2019-05-03 | 汕头大学 | A kind of dip roll coating application process generation bubble machine vision detection method |
| CN109886983A (en) * | 2018-12-27 | 2019-06-14 | 新绎健康科技有限公司 | A kind of image tongue body dividing method, device and computer equipment |
-
2019
- 2019-12-23 CN CN201911337800.1A patent/CN111242964B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105447851A (en) * | 2015-11-12 | 2016-03-30 | 刘新辉 | Glass panel sound hole defect detection method and system |
| CN107093167A (en) * | 2017-03-07 | 2017-08-25 | 北京环境特性研究所 | A kind of self-adaptive solution algorithm for ultraviolet imagery system |
| CN107480662A (en) * | 2017-10-11 | 2017-12-15 | 青岛华晶生物技术有限公司 | A kind of mould image-recognizing method and its device |
| CN108596891A (en) * | 2018-04-23 | 2018-09-28 | 中国计量大学 | A kind of method of counting towards multiple types mixing silk cocoon |
| CN109712125A (en) * | 2018-12-19 | 2019-05-03 | 汕头大学 | A kind of dip roll coating application process generation bubble machine vision detection method |
| CN109886983A (en) * | 2018-12-27 | 2019-06-14 | 新绎健康科技有限公司 | A kind of image tongue body dividing method, device and computer equipment |
Non-Patent Citations (1)
| Title |
|---|
| K. G. KOROTKOV等: "\"Analysis and monitoring of the human energy state with gas discharge visualization technique\"" * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111242964B (en) | 2023-11-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Su et al. | Combination of document image binarization techniques | |
| Yousefi | Image binarization using Otsu thresholding algorithm | |
| CN111985329B (en) | Remote sensing image information extraction method based on FCN-8s and improved Canny edge detection | |
| Agarwal et al. | Medical image contrast enhancement using range limited weighted histogram equalization | |
| CN104933709B (en) | Random walk CT lung tissue image automatic segmentation methods based on prior information | |
| Yimit et al. | 2-D direction histogram based entropic thresholding | |
| Ayed et al. | Unsupervised variational image segmentation/classification using a Weibull observation model | |
| CN109325490A (en) | Terahertz image target identification method based on deep learning and RPCA | |
| CN111192243A (en) | GDV energy diagram direction line calculation method and device | |
| CN108241865A (en) | Multiple dimensioned more subgraph liver fibrosis multi-stage quantizations based on ultrasonoscopy method by stages | |
| CN113870135A (en) | NSST domain infrared image enhancement method based on longicorn stigma optimization algorithm | |
| CN108460337A (en) | Dense fog scene aerial image fusion identification method based on adaptive cloud model | |
| CN104933719B (en) | One kind integration segment spacing is from detection image edge method | |
| Frucci et al. | From segmentation to binarization of gray-level images | |
| CN111242964B (en) | Contour determination method and device for GDV energy map | |
| CN112819739B (en) | Image processing method and system for scanning electron microscope | |
| Chen et al. | An improved edge detection in noisy image using fuzzy enhancement | |
| Krawczyk et al. | Segmentation of bone structures with the use of deep learning techniques | |
| CN111985488A (en) | Target detection segmentation method and system based on offline Gaussian model | |
| Hui et al. | An acute leukemia subtype classification procedure based on blood sample images | |
| Sriram et al. | Classification of human epithelial type-2 cells using hierarchical segregation | |
| CN104063874A (en) | Method for quantitatively evaluating gray level image binarization algorithms based on weight model | |
| Pradhan et al. | Adaptive thresholding based image segmentation with uneven lighting condition | |
| Priya | Detection of cancerous lesion by uterine cervix image segmentation | |
| CN111210437B (en) | Method and system for dividing gas discharge energy map sectors based on auxiliary circles |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |