CN106875357A - Image in 2 D code processing method - Google Patents
Image in 2 D code processing method Download PDFInfo
- Publication number
- CN106875357A CN106875357A CN201710057565.7A CN201710057565A CN106875357A CN 106875357 A CN106875357 A CN 106875357A CN 201710057565 A CN201710057565 A CN 201710057565A CN 106875357 A CN106875357 A CN 106875357A
- Authority
- CN
- China
- Prior art keywords
- image
- value
- dimensional code
- processing method
- setting
- 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.)
- Pending
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 16
- 238000000034 method Methods 0.000 claims abstract description 26
- 230000003044 adaptive effect Effects 0.000 claims abstract description 4
- 239000011159 matrix material Substances 0.000 claims description 22
- 238000004364 calculation method Methods 0.000 claims description 6
- 101100083446 Danio rerio plekhh1 gene Proteins 0.000 claims description 4
- 101100311460 Schizosaccharomyces pombe (strain 972 / ATCC 24843) sum2 gene Proteins 0.000 claims description 3
- 101100129500 Caenorhabditis elegans max-2 gene Proteins 0.000 claims description 2
- 238000010146 3D printing Methods 0.000 abstract description 4
- 238000005286 illumination Methods 0.000 abstract description 4
- 238000009499 grossing Methods 0.000 abstract description 3
- 239000000463 material Substances 0.000 abstract description 3
- 239000011347 resin Substances 0.000 abstract description 2
- 229920005989 resin Polymers 0.000 abstract description 2
- 238000012937 correction Methods 0.000 description 5
- 238000004519 manufacturing process Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000007639 printing Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 239000003086 colorant Substances 0.000 description 1
- 238000010017 direct printing Methods 0.000 description 1
- 238000005530 etching Methods 0.000 description 1
- 238000001259 photo etching Methods 0.000 description 1
- 238000000206 photolithography Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a kind of image in 2 D code processing method, carried out according to following steps order：1）Obtain positioning region；2）Adjust after obtaining positioning region, it is square to use bilinear interpolation to adjust graphical rule；3）Algorithm is stretched with nonlinear gray to improve the contrast of image；4）To step 3）The result for obtaining is corroded；5）With dimensional Gaussian convolution operator to image f (x, y) by block size for q*q carries out convolution；6）The binaryzation computing of adaptive threshold is carried out to image G (x, y) after convolution；7）The datamatrix Quick Response Codes of the standard that will be obtained are input into and are decoded into decoder.The present invention instead of smoothing method of the prior art using for the insensitive Quick Response Code of illumination variation using the method for gray scale stretching so that resolution increases.Identification of the present invention suitable for 3D printing resin material Quick Response Code.
Description
Technical Field
The invention belongs to the field of algorithms, relates to an image processing method, and particularly relates to a two-dimensional code image processing method.
Background
Data Matrix is a two-dimensional code widely used in the international manufacturing field. The Data Matrix is a member of two-dimensional codes, is invented by the American International Data corporation in 1989, and is widely used for anti-counterfeiting and overall marking of commodities. The code can be directly marked on the solid surface, can be automatically read by a corresponding scanning device like a common bar code, and is favored by the manufacturing industry. At present, Data Matrix is widely used in systems of product identification, anti-counterfeiting, quality tracking, automatic warehousing, logistics management and control and the like. The Data Matrix adopts a complex error correcting code technology, so that the code has super pollution resistance. Even if the code part is damaged, the whole information can not be read out. The printing characteristics of Data Matrix make it the only support at present for directly marking (printing, engraving, photolithography, etching, stamping, etc.) the surface of the product or component. Its high-efficiency fault-tolerant performance makes it possible to bear the pollution of surface mark of component in the course of manufacture or circulation, so that it is very popular in manufacturing industry. For various applications, various forms of DataMatrix symbology standards systems have been promulgated internationally. The minimum size of Data Matrix is the smallest of all barcodes at present, and is particularly suitable for the identification of small parts and the direct printing on a solid body.
The Data Matrix can be divided into two types of ECC000-140 and ECC200, the ECC000-140 has error correction functions of different levels, and the ECC200 generates a polynomial through a Reed-Solomon algorithm to calculate error correction codes, and the sizes of the error correction codes can be printed into different sizes according to requirements, but the adopted error correction codes are matched with the sizes, and the ECC200 is generally common due to the fact that the algorithm is easy and the sizes are flexible. The Data Matrix code has high density, small size and large information amount, provides possibility for the identification, and has less research on the DM code in China. The Data Matrix code is a Matrix type two-dimensional bar code, and has the biggest characteristic of high density, and the smallest size of the Matrix type two-dimensional bar code is the smallest code in all the bar codes at present. The DM code can be only 25mm2Encode 30 numbers on the area. The DM adopts a complex error correcting code technology, so that the code has super-strong anti-pollution capability. The Data Matrix provides a very small and high-density label and can still store reasonable Data content, so the Data Matrix is particularly suitable for small part identification, commodity anti-counterfeiting, circuit identification and the like. Due to its excellent error correction capability, the DM code has become the mainstream technology of the korean mobile phone two-dimensional barcode. Compared with QR, the DM code is simple in application due to small difference of information capacity, is called as a 'simple code' in the industry, has low requirement on a terminal, can be identified by a mobile phone with 30 ten thousand pixels, and is more added value based on WAP. The two-dimensional code brings a new entrance to the internet of the mobile phone, and a user can quickly enter a WAP website to quickly browse through scanning various bar codes. The Data Matrix symbol looks like a chessboard consisting of two colors of light and dark, each of which is a black or white square of the same size called a Data unit, Data MatrixThe x symbol is composed of many such data units. And a dead zone with the width of one data unit is arranged outside the edge searching zone. The border-seeking area is the boundary of the chessboard and is only used for positioning and defining the size of a data unit, and does not contain any coding information. The data area surrounded by the seek edge area contains the encoded information.
In the prior art, Data Matrix is mostly used for printing, engraving, photoetching, corroding, stamping and other modes, the two-dimensional codes generated by the modes are simple in identification mode, the identification degree of the edges is high, but the two-dimensional codes are directly generated by adopting a 3D printing mode, the identification edges are fuzzy, the positioning is difficult to realize, and due to the material, the contrast is relatively poor under the illumination condition, and the difficulty is high when the two-dimensional codes are processed and identified, so that the method for processing the images of the two-dimensional codes generated by the 3D printing method has important significance.
Disclosure of Invention
The invention aims to solve the technical problem of providing a two-dimensional code image processing method, which adopts a two-dimensional code insensitive to illumination change and adopts a gray stretching method to replace a smoothing method in the prior art, so that the recognition degree is increased.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a two-dimensional code image processing method comprises the following steps:
1) acquiring a positioning area;
2) after the positioning area is obtained through adjustment, adjusting the image scale to be square by adopting a bilinear interpolation method according to the code system standard of the datamatrix two-dimensional code;
3) improving the contrast of the image by a nonlinear gray scale stretching algorithm;
4) corroding the result obtained in the step 3) to eliminate shadows caused by light source deviation;
5) convolving the image f (x, y) by a two-dimensional Gaussian convolution operator according to the block size q;
6) performing binarization operation of an adaptive threshold value on the convolved image G (x, y) to obtain a processed datamatrix two-dimensional code;
7) and inputting the obtained standard datamatrix two-dimensional code into a decoder for decoding.
As a limitation of the present invention, the specific method of step 2) is:
(a) setting the pixel position of the new image as (m, n), and the magnification times as k, p, respectively, then the adjusted pixel position in the corresponding position of the original image is:；
(b) setting four points of the original image, f (i, j), f (i +1, j), f (i, j +1), f (i +1, j +1), calculating the interpolation of the four-point area to obtain the pixel value f (x, y) at the corresponding position of the new image:
wherein,,band, and:
a=x-i；b=y-j；
(c) changing the image into a square with p × q width by adjusting parameters k and p; wherein p is the number of code words of the two-dimensional code, and q is the scale for expanding p.
As another limitation of the present invention, the specific method of step 3) is:
(a) assuming that the minimum gray scale value of the image f (x, y) is min and the maximum gray scale value is max, the average gray scale value of the image is:
；
(b) calculating a minimum value E according to the gray average value:
setting E1=0.05, E2=0.95, E = min (E1, E2), eps a constant approaching 0;
wherein,；
(c) substituting the obtained E into an equation for calculation to obtain an image with enhanced contrast, and setting a function input image d = f (x, y), the calculation equation is as follows:。
as a further limitation of the above definition, said eps in step b) is 10-26~10-28。
As a third limitation of the present invention, the specific method of step 4) is: and setting a structural element g to corrode the image f (x, y):
(fΘg)(s, t)=min{f(s+x, t+y)- g(s,t)|s+x, t+y∈Df , x+y∈Dg }
wherein g is a structural element, s is a vertical dimension, t is a horizontal dimension of the structural element, Df is a vertical boundary of the image f (x, y), and Dg is a horizontal boundary of the image f (x, y).
As a fourth limitation of the present invention, said convolution result in step 5) outputs a 12 × 12 matrix in which the value of each pixel represents a gaussian weighted mean value of each block;
wherein the gaussian operator is: g (x, y) =；
Assuming that the convolved image is G (x, y), then: g (x, y) = G (x, y) × f (x, y); wherein G (x, y) is a gaussian operator, f (x, y) is an image, G (x, y) is a convolved image, σ is a standard deviation of a gaussian function, σ =0.5, "×" is a convolution operator, and the scale of the gaussian operator is q.
In another limitation of the present invention, the specific method of step 6) is:
(a) calculating the obtained 12 x 12 matrix by applying a maximum inter-class variance method to form a binarization region;
(b) setting the value range of pixels in the region to be min-max, the total number of the pixels in the region to be N, and setting the optimal binarization threshold value of the region to be T, wherein T belongs to min-max, and when the threshold value is Ti:
i) if the number of pixels with the gray scale value larger than Ti in the region is sum1, and the sum of the pixel values is max1, the average gray scale value is:(ii) a The weight is:；
ii) the number of pixels with the gray value of the area less than Ti is sum2, the sum of the pixel values is max2, and the gray average value is:(ii) a The weight is:；
iii) obtaining the overall gray average value of the region as:；
thus, the two-part sum of variance is obtained as:；
and traversing all Ti, and taking the threshold value of the maximum variance value as a threshold value for binarizing the area.
Due to the adoption of the technical scheme, compared with the prior art, the invention has the technical progress that:
the invention adopts the two-dimensional code which is insensitive to the illumination change and adopts the gray stretching method to replace the smoothing method in the prior art, so that the identification degree is increased.
The method is suitable for identifying the two-dimensional code of the 3D printing resin material.
The invention will be described in further detail below with reference to the drawings and specific examples.
Drawings
FIG. 1 is a schematic view of step 1) in example 1 of the present invention;
FIG. 2 is a schematic view of step 2) in example 1 of the present invention;
FIG. 3 is a schematic view of step 3) in example 1 of the present invention;
FIG. 4 is a schematic view of step 4) in example 1 of the present invention;
FIG. 5 is a schematic view of step 5) in example 1 of the present invention;
FIG. 6 is a schematic view of step 6) in example 1 of the present invention.
Detailed Description
Embodiment 1 two-dimensional code image processing method
A two-dimensional code image processing method comprises the following steps:
1) acquiring a positioning area, as shown in fig. 1;
2) after the positioning area is obtained through adjustment, adjusting the image scale to be a square by adopting a bilinear interpolation method according to the code system standard of the datamatrix two-dimensional code, as shown in fig. 2;
(a) setting the pixel position of the new image as (m, n), and the magnification times as k, p, respectively, then the adjusted pixel position in the corresponding position of the original image is:；
(b) setting four points of the original image, f (i, j), f (i +1, j), f (i, j +1), f (i +1, j +1), calculating the interpolation of the four-point area to obtain the pixel value f (x, y) at the corresponding position of the new image:
wherein,,band, and:
a=x-i；b=y-j；
(c) changing the image into a square with p × q width by adjusting parameters k and p; wherein p is the number of code words of the two-dimensional code, and q is the scale for expanding p.
3) The contrast of the image is improved by using a non-linear gray scale stretching algorithm, as shown in fig. 3, the method is as follows:
(a) assuming that the minimum gray scale value of the image f (x, y) is min and the maximum gray scale value is max, the average gray scale value of the image is:
；
(b) calculating a minimum value E according to the gray average value:
setting E1=0.05, E2=0.95, E = min (E1, E2), eps is a constant close to 0, eps is 10-26~10-28。；
Wherein,；
(c) substituting the obtained E into an equation for calculation to obtain an image with enhanced contrast, and setting a function input image d = f (x, y), the calculation equation is as follows:。
4) corroding the result obtained in the step 3) to eliminate the shadow caused by the light source deviation, as shown in fig. 4, the specific method is as follows: and setting a structural element g to corrode the image f (x, y):
(fΘg)(s, t)=min{f(s+x, t+y)- g(s,t)|s+x, t+y∈Df , x+y∈Dg }
wherein g is a structural element, s is a vertical dimension, t is a horizontal dimension of the structural element, Df is a vertical boundary of the image f (x, y), and Dg is a horizontal boundary of the image f (x, y).
5) The image f (x, y) is convolved with a two-dimensional gaussian convolution operator with a block size q, and as shown in fig. 5, the result of the convolution outputs a 12 × 12 matrix with each pixel value representing a gaussian weighted mean for each block, where g (x, y) = gaussian operator；
Assuming that the convolved image is G (x, y), then: g (x, y) = G (x, y) × f (x, y); wherein G (x, y) is a gaussian operator, f (x, y) is an image, G (x, y) is a convolved image, σ is a standard deviation of a gaussian function, σ =0.5, "×" is a convolution operator, and the scale of the gaussian operator is q.
6) Performing binarization operation of adaptive threshold on the convolved image G (x, y) to obtain a processed datamatrix two-dimensional code, as shown in fig. 6, the specific method is as follows:
(a) calculating the obtained 12 x 12 matrix by applying a maximum inter-class variance method to form a binarization region;
(b) setting the value range of pixels in the region to be min-max, the total number of the pixels in the region to be N, and setting the optimal binarization threshold value of the region to be T, wherein T belongs to min-max, and when the threshold value is Ti:
i) if the number of pixels with the gray scale value larger than Ti in the region is sum1, and the sum of the pixel values is max1, the average gray scale value is:(ii) a The weight is:；
ii) the number of pixels with the gray value of the area less than Ti is sum2, the sum of the pixel values is max1, and the gray average value is:(ii) a The weight is:；
iii) obtaining the overall gray average value of the region as:；
thus, the two-part sum of variance is obtained as:；
and traversing all Ti, and taking the threshold value of the maximum variance value as a threshold value for binarizing the area.
7) And inputting the obtained standard datamatrix two-dimensional code into a decoder for decoding.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention, and equivalents thereof, as may be devised by those skilled in the art using the foregoing teachings. However, simple modifications, equivalent changes and modifications made to the above embodiments according to the technical essence of the present invention are within the scope of the claims of the present invention, unless departing from the technical idea of the present invention.
Claims (7)
1. A two-dimensional code image processing method is characterized by comprising the following steps in sequence:
1) acquiring a positioning area;
2) after the positioning area is obtained through adjustment, adjusting the image scale to be square by adopting a bilinear interpolation method according to the code system standard of the datamatrix two-dimensional code;
3) improving the contrast of the image by a nonlinear gray scale stretching algorithm;
4) corroding the result obtained in the step 3) to eliminate shadows caused by light source deviation;
5) convolving the image f (x, y) by a two-dimensional Gaussian convolution operator according to the block size q;
6) performing binarization operation of an adaptive threshold value on the convolved image G (x, y) to obtain a processed datamatrix two-dimensional code;
7) and inputting the obtained standard datamatrix two-dimensional code into a decoder for decoding.
2. The two-dimensional code image processing method according to claim 1, characterized in that: the specific method of the step 2) comprises the following steps:
(a) setting the pixel position of the new image as (m, n), and the magnification times as k, p, respectively, then the adjusted pixel position in the corresponding position of the original image is:；
(b) setting four points of the original image, f (i, j), f (i +1, j), f (i, j +1), f (i +1, j +1), calculating the interpolation of the four-point area to obtain the pixel value f (x, y) at the corresponding position of the new image:
wherein,,band, and:
a=x-i；b=y-j；
(c) changing the image into a square with p × q width by adjusting parameters k and p; wherein p is the number of code words of the two-dimensional code, and q is the scale for expanding p.
3. The two-dimensional code image processing method according to claim 1, characterized in that: the specific method of the step 3) is as follows:
(a) assuming that the minimum gray scale value of the image f (x, y) is min and the maximum gray scale value is max, the average gray scale value of the image is:
；
(b) calculating a minimum value E according to the gray average value:
setting parameters E1=0.05, E2=0.95, E = min (E1, E2), eps a constant approaching 0;
wherein,；
(c) substituting the obtained E into an equation for calculation to obtain an image with enhanced contrast, and setting a function input image d = f (x, y), the calculation equation is as follows:。
4. the two-dimensional code image processing method according to claim 3, characterized in that: in step b) eps is 10-26~10-28。
5. The two-dimensional code image processing method according to claim 1, characterized in that: the specific method of the step 4) comprises the following steps: and setting a structural element g to corrode the image f (x, y):
(fΘg)(s, t)=min{f(s+x, t+y)- g(s,t)|s+x, t+y∈Df , x+y∈Dg }
wherein g is a structural element, s is a vertical dimension, t is a horizontal dimension of the structural element, Df is a vertical boundary of the image f (x, y), and Dg is a horizontal boundary of the image f (x, y).
6. The two-dimensional code image processing method according to claim 1, characterized in that: outputting a 12 × 12 matrix by the convolution result in the step 5), wherein the value of each pixel in the matrix represents the gaussian weighted mean value of each block;
wherein the normalized gaussian operator is: g (x, y) =；
Assuming that the convolved image is G (x, y), then: g (x, y) = G (x, y) × f (x, y); wherein G (x, y) is a normalized gaussian operator, f (x, y) is an image, G (x, y) is a convolved image, σ is a standard deviation of a gaussian function, σ =0.5, "-" is a convolution operator, and the scale of the gaussian operator is q.
7. The two-dimensional code image processing method according to claim 1, characterized in that: the specific method of the step 6) comprises the following steps:
(a) calculating the obtained 12 x 12 matrix by applying a maximum inter-class variance method to form a binarization region;
(b) setting the value range of pixels in the region to be min-max, the total number of the pixels in the region to be N, and setting the optimal binarization threshold value of the region to be T, wherein T belongs to min-max, and when the threshold value is Ti:
i) if the number of pixels with the gray scale value larger than Ti in the region is sum1, and the sum of the pixel values is max1, the average gray scale value is:(ii) a The weight is:；
ii) the number of pixels with the gray value of the area less than Ti is sum2, the sum of the pixel values is max2, and the gray average value is:(ii) a The weight is:；
iii) obtaining the overall gray average value of the region as:；
thus, the two-part sum of variance is obtained as:；
and traversing all Ti, and taking the threshold value of the maximum variance value as a threshold value for binarizing the area.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710057565.7A CN106875357A (en) | 2017-01-26 | 2017-01-26 | Image in 2 D code processing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710057565.7A CN106875357A (en) | 2017-01-26 | 2017-01-26 | Image in 2 D code processing method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106875357A true CN106875357A (en) | 2017-06-20 |
Family
ID=59158364
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710057565.7A Pending CN106875357A (en) | 2017-01-26 | 2017-01-26 | Image in 2 D code processing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106875357A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110046528A (en) * | 2018-11-20 | 2019-07-23 | 维库（厦门）信息技术有限公司 | A kind of dotted DataMatrix two-dimensional code identification method |
| CN110991457A (en) * | 2019-11-26 | 2020-04-10 | 北京达佳互联信息技术有限公司 | Two-dimensional code processing method and device, electronic equipment and storage medium |
| CN112651260A (en) * | 2020-12-30 | 2021-04-13 | 凌云光技术股份有限公司 | Method and system for converting self-adaptive discrete code into continuous code |
| CN114648038A (en) * | 2020-12-17 | 2022-06-21 | 顺丰科技有限公司 | Graphic code decoding method and device, computer equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5644646A (en) * | 1994-08-05 | 1997-07-01 | University Of Utah Research Foundation | Vessel enhancement filtering in magnetic resonance angiography |
| CN102176243A (en) * | 2010-12-30 | 2011-09-07 | 浙江理工大学 | Target ranging method based on visible light and infrared camera |
| CN104463795A (en) * | 2014-11-21 | 2015-03-25 | 高韬 | Processing method and device for dot matrix type data matrix (DM) two-dimension code images |
-
2017
- 2017-01-26 CN CN201710057565.7A patent/CN106875357A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5644646A (en) * | 1994-08-05 | 1997-07-01 | University Of Utah Research Foundation | Vessel enhancement filtering in magnetic resonance angiography |
| CN102176243A (en) * | 2010-12-30 | 2011-09-07 | 浙江理工大学 | Target ranging method based on visible light and infrared camera |
| CN104463795A (en) * | 2014-11-21 | 2015-03-25 | 高韬 | Processing method and device for dot matrix type data matrix (DM) two-dimension code images |
Non-Patent Citations (3)
| Title |
|---|
| 张一凡: "Data Matrix二维条码预处理方法研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 王建宾: "基于粒子群优化絮体图像分割算法的设计和应用", 《中国优秀硕士论文全文数据库 信息科技辑》 * |
| 苗莎 等: "双线性插值图像放大并行算法的设计与实现", 《微电子学与计算机》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110046528A (en) * | 2018-11-20 | 2019-07-23 | 维库（厦门）信息技术有限公司 | A kind of dotted DataMatrix two-dimensional code identification method |
| CN110991457A (en) * | 2019-11-26 | 2020-04-10 | 北京达佳互联信息技术有限公司 | Two-dimensional code processing method and device, electronic equipment and storage medium |
| CN110991457B (en) * | 2019-11-26 | 2023-12-08 | 北京达佳互联信息技术有限公司 | Two-dimensional code processing method and device, electronic equipment and storage medium |
| CN114648038A (en) * | 2020-12-17 | 2022-06-21 | 顺丰科技有限公司 | Graphic code decoding method and device, computer equipment and storage medium |
| CN112651260A (en) * | 2020-12-30 | 2021-04-13 | 凌云光技术股份有限公司 | Method and system for converting self-adaptive discrete code into continuous code |
| CN112651260B (en) * | 2020-12-30 | 2024-01-30 | 凌云光技术股份有限公司 | Method and system for converting self-adaptive discrete codes into continuous codes |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chai et al. | Locating and decoding EAN-13 barcodes from images captured by digital cameras | |
| CN105069394B (en) | Quick Response Code weighted average gray level method coding/decoding method and system | |
| CN107633192A (en) | Bar code segmentation and reading method under a kind of complex background based on machine vision | |
| CN105989317B (en) | A two-dimensional code identification method and device | |
| EP2393038A1 (en) | Matrix-type two-dimensional barcode decoding chip and decoding method thereof | |
| CN106875357A (en) | Image in 2 D code processing method | |
| EP2393037A1 (en) | Data matrix decoding chip and decoding method thereof | |
| CN110647795A (en) | Form recognition method | |
| CN111783495B (en) | Bar code identification method and device, electronic equipment and storage medium | |
| CN107247985B (en) | Coding, positioning and identifying method of two-dimensional code | |
| CN110569683B (en) | Barcode detection method and barcode detection system | |
| CN104463795A (en) | Processing method and device for dot matrix type data matrix (DM) two-dimension code images | |
| CN108073966B (en) | A kind of conversion method of two-dimensional code and hole array code | |
| CN107273777A (en) | A kind of Quick Response Code identification of code type method matched based on slide unit | |
| CN106845312B (en) | A pre-judgment method for image quality of two-dimensional code | |
| CN108734250B (en) | Visual two-dimensional code generation method based on Sobel operator | |
| CN108573184A (en) | A kind of two-dimensional code identification method, module and computer readable storage medium | |
| CN112163443A (en) | Code scanning method, code scanning device and mobile terminal | |
| CN112651257A (en) | Two-dimensional code, bar code image positioning and identifying method and storage medium thereof | |
| CN115456003A (en) | DPM two-dimensional code identification method and storage medium | |
| US20230289922A1 (en) | Glare Mitigation Techniques in Symbologies | |
| CN115270839A (en) | Industrial scene QR Code detection and identification method based on PPYOLOv2 model | |
| CN104346596A (en) | Identification method and identification device for QR (Quick Response) code | |
| WO2022021687A1 (en) | Method for positioning quick response code area, and electronic device and storage medium | |
| CN106778435B (en) | Feature extraction method based on image neighborhood structure tensor equation |
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 | ||
| CB02 | Change of applicant information |
Address after: 201210 Shanghai city Pudong New Area Zu Road No. 2305 A block 122 Building 2 North Gate God's favored one Applicant after: Shanghai Ya Fang dental Polytron Technologies Inc Address before: 201210 Shanghai city Pudong New Area Zu Road No. 2305 A block 122 Building 2 North Gate God's favored one Applicant before: Shanghai Smartee Dental Technology Co., Ltd. |
|
| CB02 | Change of applicant information | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170620 |
|
| RJ01 | Rejection of invention patent application after publication |