CN102750637B - The product false proof querying method based on machine vision of To enterprises user and system - Google Patents
The product false proof querying method based on machine vision of To enterprises user and system Download PDFInfo
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Abstract
The product false proof querying method based on machine vision of To enterprises user and system, relate to product false proof inquiring technology field, and its method is to install three photographic head at production line；The coded lock label of product, security code label, bar coded sticker are taken pictures by three photographic head respectively, to obtain the Digital Character Image of coded lock label, security code label, bar coded sticker respectively；Inquiry table is obtained by calculating；Utilize algorithm and in conjunction with inquiry list processing, obtain coded lock password, digit strings that security code is corresponding with bar code；The digit strings of this correspondence is formed the document with one-to-one relationship and is uploaded to data base, it is supplied to user side inquiry, this pattern can well improve efficiency on producing, its system includes the smart machine and the Antiforge inquiry terminal server that possess machine vision, the built-in security code automatic recognition software of smart machine, allowing user can accomplish to carry out whenever and wherever possible product false proof inquiry, the suitability is high, can replace traditional artificial Antiforge inquiry.
Description
Technical field
The present invention relates to product false proof inquiring technology field, particularly relate to the product false proof querying method based on machine vision and the system of To enterprises user.
Background technology
Present product fake and forged commodity problem emerges in an endless stream along with development in science and technology, and fake and forged object is mainly for the product etc. of famous and precious tobacco and wine, high-grade dress ornament, luxury goods and some reputable brands at present.A lot of counterfeit and shoddy goods adopt the used packing box/bottle of original-pack certified products to pretend, and obscure the sight line of consumer, reach the purpose mixed the spurious with the genuine.In the prior art, smart mobile phone makes rapid progress along with the development of science and technology, and commercially smart mobile phone occupation rate proportion is more and more higher, progressively replaces functional mobile phone simultaneously；Functionally smart mobile phone progressively realizes the function that personal computer has；Smart mobile phone is pursued by consumer, and popularity rate is more and more higher.
Existing product false proof inquiry service has for the true and false discriminating of product, manually security code is inquired about if adopted, and especially old people's error rate is high, causes that consumer is all unwilling to carry out true and false inquiry, and efficiency is low.
In prior art, data base's forming method of Antiforge inquiry terminal server falls behind so that the production efficiency of enterprise customer is low.
Summary of the invention
An object of the present invention is in that to avoid weak point of the prior art to provide the product false proof querying method based on machine vision of To enterprises user, the product false proof querying method based on machine vision of these To enterprises user can improve production efficiency, the accuracy rate of Antiforge inquiry is high
The two of the purpose of the present invention are in that to avoid weak point of the prior art to provide the product false proof inquiry service based on machine vision of To enterprises user, the product false proof inquiry service based on machine vision of these To enterprises user, can accomplish to carry out whenever and wherever possible product false proof inquiry, the suitability is high, can replace traditional artificial Antiforge inquiry.
An object of the present invention is achieved through the following technical solutions.
The product false proof querying method based on machine vision of To enterprises user is provided, comprises the steps:
1) correspond to three photographic head of position installation of the coded lock label of product, security code label, bar coded sticker at production line；The coded lock label of product, security code label, bar coded sticker are taken pictures by three photographic head respectively, to obtain the Digital Character Image of coded lock label, security code label, bar coded sticker respectively；
2) by the coded lock label of acquisition, security code label, bar coded sticker Digital Character Image obtain inquiry table by calculating；
3) utilize algorithm integrating step 2) inquiry list processing, obtain coded lock password, digit strings that security code is corresponding with bar code；
4) digit strings corresponding with bar code to coded lock password, security code is formed the document with one-to-one relationship；
5) document that step 4) generates is uploaded to data base, it is provided that inquire about to user side.
Preferably, described step 2) in inquiry table obtaining step include:
2.1) by MATLAB software by coded lock label, security code label, bar coded sticker Digital Character Image carry out pretreatment, denoising, greyscale transformation and binary conversion treatment；
2.2) by MATLAB software give each numerical character position size identical square-shaped frame, frame selects each numerical character in Digital Character Image；
2.3) square-shaped frame is divided into four parts；
2.4) by MATLAB computed in software go out the left and right ratio of each numerical character of rectangular box frame choosing, row concentration degree, row concentration degree, the ratio of width to height and central area and, upper and lower ratio, central area and the ratio of width to height.
It is furthermore preferred that the algorithm process step in described step 3) includes:
3.1) by the left and right ratio of numerical character 3,4,5,9,2,7,6,0,1 and 8 with set threshold value A compare, more than set threshold value A, identify numerical character be 3 or numerical character be 4；
3.1.1) by numerical character be 3 and row concentration degree that numerical character is 4 compare with setting threshold value C, more than the threshold value C set, then identifying numerical character is 3；Less than the threshold value C set, then identifying numerical character is 4；
3.2) the left and right ratio of numerical character 5,9,2,7,6,0,1 and 8 is compared with the threshold value B setting threshold value A and setting, more than set threshold value A and less than set threshold value B, identify numerical character be 5 or numerical character be 9 or numerical character be 2 or numerical character be 7；
3.2.1) the row concentration degree of numerical character 5, numerical character 9, numerical character 2 and numerical character 7 is compared with the threshold value D of setting, less than set threshold value D, then identify numerical character be 5 or numerical character be 9；
3.2.1.1) by the ratio of width to height of numerical character 5 and numerical character 9 and central area and compare with the threshold value E set, more than the threshold value E set, then identifying numerical character is 9；Less than the threshold value E set, then identifying numerical character is 5；
3.2.2) the row concentration degree of numerical character 2 and numerical character 7 is compared with the threshold value D of setting,
More than the threshold value D set, then identifying numerical character is 2 or 7；
3.2.2.1) ratio up and down of numerical character 2 and numerical character 7 being compared with the threshold value F of setting, more than the threshold value F set, then identifying numerical character is 2；Less than the threshold value F set, then identifying numerical character is 7；
3.3) by the left and right ratio of numerical character 5,9,2,7,6,0,1 and 8 with set threshold value B compare, less than set threshold value B, then identify numerical character be 6 or numerical character be 0 or numerical character be 1 or numerical character be 8；
3.3.1) numerical character 6, numerical character 0, numerical character 1 and the central area of numerical character 8 are compared with the threshold value G of setting, more than set threshold value G, then identify numerical character be 0 or numerical character be 6；
3.3.1.1) the row concentration degree of numerical character 0 and numerical character 6 being compared with the threshold value H of setting, more than the threshold value H set, then identifying numerical character is 6；
3.3.1.2) the row concentration degree of numerical character 0 and numerical character 6 being compared with the threshold value H of setting, less than the threshold value H set, then identifying numerical character is 0；
3.3.2) central area of numerical character 1 and numerical character 8 is compared with the threshold value G of setting, less than set threshold value G, then identify numerical character be 1 or numerical character be 8；
3.3.2.1) the ratio of width to height of numerical character 1 and numerical character 8 being compared with the threshold value I of setting, more than the threshold value I set, then identifying numerical character is 8；Less than the threshold value I set, then identifying numerical character is 1.
Another is preferred, and described threshold value A is set as 1.2955；Described threshold value B is set as 1.0617；Described threshold value C is set as 0.2122；Described threshold value D is set as 0.2467；Described threshold value E is set as 1.3333；Described threshold value F is set as 0.8803；Described threshold value G is set as 0.6706；Described threshold value H is set as 1.0850；Described threshold value I is set as 0.4621.
Another is preferred, described step 2) the content of inquiry table include: the left and right ratio of digital 0 correspondence is 009918；The left and right ratio of numeral 1 correspondence is 009866；The left and right ratio of numeral 2 correspondences is 1.1943；The left and right ratio of numeral 3 correspondences is 1.3929；The left and right ratio of numeral 4 correspondences is 1.5071；The left and right ratio of numeral 5 correspondences is 1.1980；The left and right ratio of numeral 6 correspondences is 0.9035；The left and right ratio of numeral 7 correspondences is 1.1722；The left and right ratio of numeral 8 correspondences is 0.9954；The left and right ratio that numerical character is 9 correspondences is 1.1280.
Another is preferred, described step 2) the content of inquiry table include: the row concentration degree of digital 0 correspondence is 0.2121；The row concentration degree of numeral 1 correspondence is 0.0303；The row concentration degree of numeral 2 correspondences is 0.5606；The row concentration degree of numeral 3 correspondences is 0.409；The row concentration degree of numeral 4 correspondences is 0.0152；The row concentration degree of numeral 5 correspondences is 0.5758；The row concentration degree of numeral 6 correspondences is 0.4394；The row concentration degree of numeral 7 correspondences is 0.6364；The row concentration degree of numeral 8 correspondences is 0.2576；Numerical character is the row concentration degree of 9 correspondences is 0.4394.
Another is preferred, described step 2) the content of inquiry table include: the row concentration degree of digital 0 correspondence is 0.5500；The row concentration degree of numeral 1 correspondence is 0.4583；The row concentration degree of numeral 2 correspondences is 0.1220；The row concentration degree of numeral 3 correspondences is 0.3889；The row concentration degree of numeral 4 correspondences is 0.0000；The row concentration degree of numeral 5 correspondences is 0.3714；The row concentration degree of numeral 6 correspondences is 0.4500；The row concentration degree of numeral 7 correspondences is 0.1026；The row concentration degree of numeral 8 correspondences is 0.4865；Numerical character is the row concentration degree of 9 correspondences is 0.4500.
Another is preferred, described step 2) the content of inquiry table include: ratio 1.0059 up and down of digital 0 correspondence；Ratio 1.0214 up and down of numeral 1 correspondence；Ratio 0.9840 up and down of numeral 2 correspondences；Ratio 1.0000 up and down of numeral 3 correspondences；Ratio 1.1693 up and down of numeral 4 correspondences；Ratio 0.8275 up and down of numeral 5 correspondences；Ratio 1.1640 up and down of numeral 6 correspondences；Ratio 0.7765 up and down of numeral 7 correspondences；Ratio 1.0360 up and down of numeral 8 correspondences；Numerical character is ratio 0.8378 up and down of 9 correspondences.
Another is preferred, described step 2) the content of inquiry table include: the central area of digital 0 correspondence is 1.0000；The central area of numeral 1 correspondence is 0.3333；The central area of numeral 2 correspondences is 0.8745；The central area of numeral 3 correspondences is 0.8266；The central area of numeral 4 correspondences is 0.4949；The central area of numeral 5 correspondences is 0.7166；The central area of numeral 6 correspondences is 0.8197；The central area of numeral 7 correspondences is 0.7560；The central area of numeral 8 correspondences is 0.5215；Numerical character is the central area of 9 correspondences is 0.8136.
Another is preferred, described step 2) the content of inquiry table include: the ratio of width to height of digital 0 correspondence is 0.6061；The ratio of width to height of numeral 1 correspondence is 0.3636；The ratio of width to height of numeral 2 correspondences is 0.6212；The ratio of width to height of numeral 3 correspondences is 0.5455；The ratio of width to height of numeral 4 correspondences is 0.6364；The ratio of width to height of numeral 5 correspondences is 0.5303；The ratio of width to height of numeral 6 correspondences is 0.6061；The ratio of width to height of numeral 7 correspondences is 0.5909；The ratio of width to height of numeral 8 correspondences is 0.5606；Numerical character is the ratio of width to height of 9 correspondences is 0.6061；
Described step 2) the content of inquiry table include: the ratio of width to height of digital 0 correspondence and central area and be 1.6061；The ratio of width to height and the central area of numeral 1 correspondence and be 0.6969；The ratio of width to height and the central area of numeral 2 correspondences and be 1.4957；The ratio of width to height and the central area of numeral 3 correspondences and be 1.3721；The ratio of width to height and the central area of numeral 4 correspondences and be 1.1313；The ratio of width to height and the central area of numeral 5 correspondences and be 1.2469；The ratio of width to height and the central area of numeral 6 correspondences and be 1.4258；The ratio of width to height and the central area of numeral 7 correspondences and be 1.3469；The ratio of width to height and the central area of numeral 8 correspondences and be 1.0821；Numerical character be the ratio of width to height and the central area of 9 correspondences and be 1.4197.
The two of the purpose of the present invention are achieved through the following technical solutions.
The product false proof inquiry service based on machine vision providing To enterprises user includes the smart machine and the Antiforge inquiry terminal server that possess machine vision, the described smart machine possessing machine vision is built-in with security code automatic recognition software, and the data base of described Antiforge inquiry terminal server includes, described in technique scheme, digit strings corresponding with bar code to coded lock password, security code formed the document with one-to-one relationship.
Beneficial effects of the present invention is as follows:
The product false proof querying method based on machine vision of the To enterprises user of the present invention is to install three photographic head at production line；The coded lock label of product, security code label, bar coded sticker are taken pictures by three photographic head respectively, to obtain the Digital Character Image of coded lock label, security code label, bar coded sticker respectively；Inquiry table is obtained by calculating；Utilize algorithm and in conjunction with inquiry list processing, obtain coded lock password, digit strings that security code is corresponding with bar code；The digit strings of this correspondence is formed the document with one-to-one relationship and is uploaded to data base, it is supplied to user side inquiry, this pattern can well improve efficiency on producing, the product false proof inquiry system based on machine vision of To enterprises user includes the smart machine and the Antiforge inquiry terminal server that possess machine vision, the built-in security code automatic recognition software of smart machine, allow user can accomplish to carry out whenever and wherever possible product false proof inquiry, the suitability is high, traditional artificial Antiforge inquiry can be replaced, need compared with security code one by one input inquiry terminal with prior art, the efficiency of Antiforge inquiry can be improved, the accuracy rate of Antiforge inquiry is high.
Accompanying drawing explanation
Fig. 1 is the flow chart of the product false proof querying method based on machine vision of To enterprises user of the present invention.
Fig. 2 be To enterprises user of the present invention based on the algorithm process flow chart in the step 4) of the product false proof querying method of machine vision.
Detailed description of the invention
The invention will be further described with the following Examples.
Embodiment 1.
The product false proof querying method based on machine vision of the To enterprises user of the present embodiment, as depicted in figs. 1 and 2, comprises the steps:
1) correspond to three photographic head of position installation of the coded lock label of product, security code label, bar coded sticker at production line；The coded lock label of product, security code label, bar coded sticker are taken pictures by three photographic head respectively, to obtain the Digital Character Image of coded lock label, security code label, bar coded sticker respectively；
2) by the coded lock label of acquisition, security code label, bar coded sticker Digital Character Image obtain inquiry table by calculating；
3) utilize algorithm integrating step 2) inquiry list processing, obtain coded lock password, digit strings that security code is corresponding with bar code；
4) digit strings corresponding with bar code to coded lock password, security code is formed the document with one-to-one relationship；
5) document that step 4) generates is uploaded to data base, it is provided that inquire about to user side.
Concrete, step 2) in inquiry table obtaining step include:
2.1) by MATLAB software by coded lock label, security code label, bar coded sticker Digital Character Image carry out pretreatment, denoising, greyscale transformation and binary conversion treatment；
2.2) by MATLAB software give each numerical character position size identical square-shaped frame, frame selects each numerical character in Digital Character Image；
2.3) square-shaped frame is divided into four parts；
2.4) by MATLAB computed in software go out the left and right ratio of each numerical character of rectangular box frame choosing, row concentration degree, row concentration degree, the ratio of width to height and central area and, upper and lower ratio, central area and the ratio of width to height.
Concrete, the algorithm process step in step 3) includes:
3.1) by the left and right ratio of numerical character 3,4,5,9,2,7,6,0,1 and 8 with set threshold value A compare, more than set threshold value A, identify numerical character be 3 or numerical character be 4；
3.1.1) by numerical character be 3 and row concentration degree that numerical character is 4 compare with setting threshold value C, more than the threshold value C set, then identifying numerical character is 3；Less than the threshold value C set, then identifying numerical character is 4；
3.2) the left and right ratio of numerical character 5,9,2,7,6,0,1 and 8 is compared with the threshold value B setting threshold value A and setting, more than set threshold value A and less than set threshold value B, identify numerical character be 5 or numerical character be 9 or numerical character be 2 or numerical character be 7；
3.2.1) the row concentration degree of numerical character 5, numerical character 9, numerical character 2 and numerical character 7 is compared with the threshold value D of setting, less than set threshold value D, then identify numerical character be 5 or numerical character be 9；
3.2.1.1) by the ratio of width to height of numerical character 5 and numerical character 9 and central area and compare with the threshold value E set, more than the threshold value E set, then identifying numerical character is 9；Less than the threshold value E set, then identifying numerical character is 5；
3.2.2) the row concentration degree of numerical character 2 and numerical character 7 being compared with the threshold value D of setting, more than the threshold value D set, then identifying numerical character is 2 or 7；
3.2.2.1) ratio up and down of numerical character 2 and numerical character 7 being compared with the threshold value F of setting, more than the threshold value F set, then identifying numerical character is 2；Less than the threshold value F set, then identifying numerical character is 7；
3.3) by the left and right ratio of numerical character 5,9,2,7,6,0,1 and 8 with set threshold value B compare, less than set threshold value B, then identify numerical character be 6 or numerical character be 0 or numerical character be 1 or numerical character be 8；
3.3.1) numerical character 6, numerical character 0, numerical character 1 and the central area of numerical character 8 are compared with the threshold value G of setting, more than set threshold value G, then identify numerical character be 0 or numerical character be 6；
3.3.1.1) the row concentration degree of numerical character 0 and numerical character 6 being compared with the threshold value H of setting, more than the threshold value H set, then identifying numerical character is 6；
3.3.1.2) the row concentration degree of numerical character 0 and numerical character 6 being compared with the threshold value H of setting, less than the threshold value H set, then identifying numerical character is 0；
3.3.2) central area of numerical character 1 and numerical character 8 is compared with the threshold value G of setting, less than set threshold value G, then identify numerical character be 1 or numerical character be 8；
3.3.2.1) the ratio of width to height of numerical character 1 and numerical character 8 being compared with the threshold value I of setting, more than the threshold value I set, then identifying numerical character is 8；Less than the threshold value I set, then identifying numerical character is 1.
Concrete, threshold value A is set as 1.2955；Threshold value B is set as 1.0617；Threshold value C is set as 0.2122；Threshold value D is set as 0.2467；Threshold value E is set as 1.3333；Threshold value F is set as 0.8803；Threshold value G is set as 0.6706；Threshold value H is set as 1.0850；Threshold value I is set as 0.4621.
Concrete, step 2) the content of inquiry table include: the left and right ratio of digital 0 correspondence is 009918；The left and right ratio of numeral 1 correspondence is 009866；The left and right ratio of numeral 2 correspondences is 1.1943；The left and right ratio of numeral 3 correspondences is 1.3929；The left and right ratio of numeral 4 correspondences is 1.5071；The left and right ratio of numeral 5 correspondences is 1.1980；The left and right ratio of numeral 6 correspondences is 0.9035；The left and right ratio of numeral 7 correspondences is 1.1722；The left and right ratio of numeral 8 correspondences is 0.9954；The left and right ratio that numerical character is 9 correspondences is 1.1280.
Concrete, step 2) the content of inquiry table include: the row concentration degree of digital 0 correspondence is 0.2121；The row concentration degree of numeral 1 correspondence is 0.0303；The row concentration degree of numeral 2 correspondences is 0.5606；The row concentration degree of numeral 3 correspondences is 0.409；The row concentration degree of numeral 4 correspondences is 0.0152；The row concentration degree of numeral 5 correspondences is 0.5758；The row concentration degree of numeral 6 correspondences is 0.4394；The row concentration degree of numeral 7 correspondences is 0.6364；The row concentration degree of numeral 8 correspondences is 0.2576；Numerical character is the row concentration degree of 9 correspondences is 0.4394.
Concrete, step 2) the content of inquiry table include: the row concentration degree of digital 0 correspondence is 0.5500；The row concentration degree of numeral 1 correspondence is 0.4583；The row concentration degree of numeral 2 correspondences is 0.1220；The row concentration degree of numeral 3 correspondences is 0.3889；The row concentration degree of numeral 4 correspondences is 0.0000；The row concentration degree of numeral 5 correspondences is 0.3714；The row concentration degree of numeral 6 correspondences is 0.4500；The row concentration degree of numeral 7 correspondences is 0.1026；The row concentration degree of numeral 8 correspondences is 0.4865；Numerical character is the row concentration degree of 9 correspondences is 0.4500.
Concrete, step 2) the content of inquiry table include: ratio 1.0059 up and down of digital 0 correspondence；Ratio 1.0214 up and down of numeral 1 correspondence；Ratio 0.9840 up and down of numeral 2 correspondences；Ratio 1.0000 up and down of numeral 3 correspondences；Ratio 1.1693 up and down of numeral 4 correspondences；Ratio 0.8275 up and down of numeral 5 correspondences；Ratio 1.1640 up and down of numeral 6 correspondences；Ratio 0.7765 up and down of numeral 7 correspondences；Ratio 1.0360 up and down of numeral 8 correspondences；Numerical character is ratio 0.8378 up and down of 9 correspondences.
Concrete, step 2) the content of inquiry table include: the central area of digital 0 correspondence is 1.0000；The central area of numeral 1 correspondence is 0.3333；The central area of numeral 2 correspondences is 0.8745；The central area of numeral 3 correspondences is 0.8266；The central area of numeral 4 correspondences is 0.4949；The central area of numeral 5 correspondences is 0.7166；The central area of numeral 6 correspondences is 0.8197；The central area of numeral 7 correspondences is 0.7560；The central area of numeral 8 correspondences is 0.5215；Numerical character is the central area of 9 correspondences is 0.8136.
Concrete, step 2) the content of inquiry table include: the ratio of width to height of digital 0 correspondence is 0.6061；The ratio of width to height of numeral 1 correspondence is 0.3636；The ratio of width to height of numeral 2 correspondences is 0.6212；The ratio of width to height of numeral 3 correspondences is 0.5455；The ratio of width to height of numeral 4 correspondences is 0.6364；The ratio of width to height of numeral 5 correspondences is 0.5303；The ratio of width to height of numeral 6 correspondences is 0.6061；The ratio of width to height of numeral 7 correspondences is 0.5909；The ratio of width to height of numeral 8 correspondences is 0.5606；Numerical character is the ratio of width to height of 9 correspondences is 0.6061；
Step 2) the content of inquiry table include: the ratio of width to height of digital 0 correspondence and central area and be 1.6061；The ratio of width to height and the central area of numeral 1 correspondence and be 0.6969；The ratio of width to height and the central area of numeral 2 correspondences and be 1.4957；The ratio of width to height and the central area of numeral 3 correspondences and be 1.3721；The ratio of width to height and the central area of numeral 4 correspondences and be 1.1313；The ratio of width to height and the central area of numeral 5 correspondences and be 1.2469；The ratio of width to height and the central area of numeral 6 correspondences and be 1.4258；The ratio of width to height and the central area of numeral 7 correspondences and be 1.3469；The ratio of width to height and the central area of numeral 8 correspondences and be 1.0821；Numerical character be the ratio of width to height and the central area of 9 correspondences and be 1.4197.
Beneficial effects of the present invention is as follows:
The present invention can improve production efficiency so that the accuracy rate of Antiforge inquiry is high.
The identification process of character 3 is as follows:
Get rid of 1,2,5,6,7,8,9,0 by the threshold value of character left and right ratio, by the row concentration degree in inquiry table, get rid of 4, identify character 3.
The method of the present invention can be divided into Three models.
The first, its unlocking cipher, security code, bar code are supplied to enterprise customer by coded lock manufacturer, security code manufacturer, bar code generating business men respectively, enterprise customer can select arrange unlocking cipher or arranged unlocking cipher by the raw manufacturer of coded lock, utilize machine vision that security code, coded lock password, bar code scan formation trigram one-to-one relationship simultaneously, go into a document and upload to data base.The second, the raw manufacturer of coded lock buys security code, and security code and coded lock password utilize machine vision form one-to-one relationship, and is supplied to enterprise customer, and enterprise customer makes trigram set up unique mapping relations by bar code scanning.
The second, the raw manufacturer of coded lock buys security code, and security code and coded lock password utilize machine vision form one-to-one relationship, and is supplied to enterprise customer, and enterprise customer makes trigram set up unique mapping relations by bar code scanning.
The third, the raw manufacturer oneself of coded lock produces security code, and security code and coded lock password utilize machine vision form one-to-one relationship, and is supplied to enterprise customer, and enterprise customer makes trigram set up unique mapping relations by bar code scanning.
The mode by machine vision that is mainly characterized by of the present invention obtains coded lock password, security code, bar code, and trigram is formed relation one to one.By machine vision applications in Antiforge inquiry system, enterprise customer, after obtaining the coded lock of manufacturer's offer, security code, bar code, adopts machine vision that password password lock, security code label, bar coded sticker are formed one-to-one relationship.In the specific position of production line, three photographic head are installed, can be CCD (Charge-coupledDevice, Chinese full name: charge coupled cell, it is properly termed as ccd image sensor) photographic head, correspond to coded lock, security code label, bar coded sticker are taken pictures, and by the image clapped by algorithm process, obtain coded lock password, security code, bar code, and trigram is unified, form the document with one-to-one relationship.This pattern can well improve efficiency on producing.
Embodiment 2.
The product false proof inquiry service based on machine vision of To enterprises user, including the smart machine and the Antiforge inquiry terminal server that possess machine vision, the smart machine possessing machine vision is built-in with security code automatic recognition software, and what the data base of Antiforge inquiry terminal server included embodiment 1 forms the document with one-to-one relationship by digit strings corresponding with bar code to coded lock password, security code.
The smart machine possessing machine vision can be smart mobile phone.
Automatic recognition software includes image collection module, image processing module, picture recognition module, micro treatment module, image display and information sending/receiving module, the outfan of image grabber is connected with the input of image processor, the outfan of image processing module is connected with the input of picture recognition module, the outfan of picture recognition module is connected with the input of micro treatment module, first outfan of micro treatment module is connected with the input of image display, and the second outfan of micro treatment module is connected with the input of information sending/receiving module.
Information sending/receiving module includes MSG sending/receiving module or the sub-sending/receiving module of HTTP or includes MSG sending/receiving module and the sub-sending/receiving module of HTTP simultaneously.
The algorithm that picture recognition module adopts is as follows:
1) select the smart machine possessing machine vision, and security code tag processes software is installed；
2) security code label is taken pictures by personal user's end by the machine vision of smart machine, obtains image；
3) acquired image of taking pictures is processed by recognizer and identifies security code character string by security code tag processes software；
4) security code character string is sent to Antiforge inquiry terminal server；
5) this security code character string is carried out the true and false and distinguishes by Antiforge inquiry terminal server；
6) will distinguish that result feeds back to the smart machine possessing machine vision.
Concrete, the recognizer of the security code tag processes software in step 3) comprises the following steps:
3.1) image of security code label is obtained；
3.2) this image be sequentially carried out normalization, remove noise, file pre-treatment that image rectification, picture and text analysis, literal line separate with word；
3.3) security code feature is extracted；
3.4) in conjunction with security code character feature storehouse, security code coupling is carried out；
3.5) check whether security code mates correctly；
3.51) if extracting security code feature；
3.52) if security code matching error, then again mate once, as correct in security code coupling, then output security code character string；
3.53) if security code matching error twice, then step 3.1 is returned) reacquire the image of security code label.
Personal user can process software-driven machine vision equipment by security code and complete the acquisition to security code, and is transferred to product false proof inquiry service by the mode of instant short message or network and carries out Antiforge inquiry.
MSG is the abbreviation of Messenger, and Chinese is instant message.HTTP, i.e. HTML (Hypertext Markup Language), be the abbreviation of HyperTextTransferProtocol.In browser address bar, input web-site address when browsing webpage all start with " HTTP: // ".HTTP defines how formatted information is, how to be transmitted, and the response that server and browser are taked under various orders.
The smart machine possessing machine vision of the present invention can be smart mobile phone, is mobile machine visual apparatus, can accomplish whenever and wherever possible product to be carried out Antiforge inquiry.
The present invention is directed to query aspects and propose improved procedure, the malice inquiry for consumer is prevented by application program restriction inquiry times and charging mode.Data base, at the automatic record queries number of consumer's first time inquiry, inquiry times or query facility number, exceedes certain number of times and then takes charging mode, and this product then empties the enquiry number log of this product record after being acquired unlocking cipher.
Image (multimedia message mode), short message can also be carried out process and parse security code by the SMS platform of the Antiforge inquiry terminal server of the present invention, inquiry system is by obtaining security code, data are entered data base querying and obtains Antiforge inquiry, obtain Query Result, return to client；In like manner processing for needing the product unblanked also to take same method to split code-locked, coded lock opened by the password returned by system.After some product is inquired about by this inquiring technology, same phone number (smart mobile phone) or uniform machinery visual apparatus number cannot be carried out again inquiry next time, code of unblanking is inquired about once only, prevent packing box from reusing the phenomenon such as fraud and malice inquiry, contrast compared with technology, the suitability of the present invention is high, efficiency is high, accuracy rate is high, and false proof is effective, can replace traditional artificial Antiforge inquiry.
Finally should be noted that; above example is merely to illustrate technical scheme but not limiting the scope of the invention; although the present invention being explained in detail with reference to preferred embodiment; it will be understood by those within the art that; technical scheme can be modified or equivalent replacement, without deviating from the spirit and scope of technical solution of the present invention.
Claims (8)
1. the product false proof querying method based on machine vision of To enterprises user, comprises the steps:
1) correspond to three photographic head of position installation of the coded lock label of product, security code label, bar coded sticker at production line；The coded lock label of product, security code label, bar coded sticker are taken pictures by three photographic head respectively, to obtain the Digital Character Image of coded lock label, security code label, bar coded sticker respectively；
2) by the coded lock label of acquisition, security code label, bar coded sticker Digital Character Image obtain inquiry table by calculating；
3) utilize algorithm and in conjunction with described step 2) inquiry list processing, obtain coded lock password, digit strings that security code is corresponding with bar code；
4) digit strings corresponding with bar code to coded lock password, security code is formed the document with one-to-one relationship；
5) document that described step 4) generates is uploaded to data base, it is provided that inquire about to user side；
Described step 2) in inquiry table obtaining step include:
2.1) by MATLAB software by coded lock label, security code label, bar coded sticker Digital Character Image carry out pretreatment, denoising, greyscale transformation and binary conversion treatment；
2.2) by MATLAB software give each numerical character position size identical square-shaped frame, frame selects each numerical character in Digital Character Image；
2.3) square-shaped frame is divided into four parts；
2.4) by MATLAB computed in software go out the left and right ratio of each numerical character of rectangular box frame choosing, row concentration degree, row concentration degree, the ratio of width to height and central area and, upper and lower ratio, central area and the ratio of width to height；
Algorithm process step in described step 3) includes:
3.1) by the left and right ratio of numerical character 3,4,5,9,2,7,6,0,1 and 8 with set threshold value A compare, more than set threshold value A, identify numerical character be 3 or numerical character be 4；
3.1.1) by numerical character be 3 and row concentration degree that numerical character is 4 compare with setting threshold value C, more than the threshold value C set, then identifying numerical character is 3；Less than the threshold value C set, then identifying numerical character is 4；
3.2) the left and right ratio of numerical character 5,9,2,7,6,0,1 and 8 is compared with the threshold value B setting threshold value A and setting, more than set threshold value A and less than set threshold value B, identify numerical character be 5 or numerical character be 9 or numerical character be 2 or numerical character be 7；
3.2.1) the row concentration degree of numerical character 5, numerical character 9, numerical character 2 and numerical character 7 is compared with the threshold value D of setting, less than set threshold value D, then identify numerical character be 5 or numerical character be 9；
3.2.1.1) by the ratio of width to height of numerical character 5 and numerical character 9 and central area and compare with the threshold value E set, more than the threshold value E set, then identifying numerical character is 9；Less than the threshold value E set, then identifying numerical character is 5；
3.2.2) the row concentration degree of numerical character 2 and numerical character 7 being compared with the threshold value D of setting, more than the threshold value D set, then identifying numerical character is 2 or 7；
3.2.2.1) ratio up and down of numerical character 2 and numerical character 7 being compared with the threshold value F of setting, more than the threshold value F set, then identifying numerical character is 2；Less than the threshold value F set, then identifying numerical character is 7；
3.3) by the left and right ratio of numerical character 5,9,2,7,6,0,1 and 8 with set threshold value B compare, less than set threshold value B, then identify numerical character be 6 or numerical character be 0 or numerical character be 1 or numerical character be 8；
3.3.1) numerical character 6, numerical character 0, numerical character 1 and the central area of numerical character 8 are compared with the threshold value G of setting, more than set threshold value G, then identify numerical character be 0 or numerical character be 6；
3.3.1.1) the row concentration degree of numerical character 0 and numerical character 6 being compared with the threshold value H of setting, more than the threshold value H set, then identifying numerical character is 6；
3.3.1.2) the row concentration degree of numerical character 0 and numerical character 6 being compared with the threshold value H of setting, less than the threshold value H set, then identifying numerical character is 0；
3.3.2) central area of numerical character 1 and numerical character 8 is compared with the threshold value G of setting, less than set threshold value G, then identify numerical character be 1 or numerical character be 8；
3.3.2.1) the ratio of width to height of numerical character 1 and numerical character 8 being compared with the threshold value I of setting, more than the threshold value I set, then identifying numerical character is 8；Less than the threshold value I set, then identifying numerical character is 1.
2. the product false proof querying method based on machine vision of To enterprises user according to claim 1, it is characterised in that: described threshold value A is set as 1.2955；Described threshold value B is set as 1.0617；Described threshold value C is set as 0.2122；Described threshold value D is set as 0.2467；Described threshold value E is set as 1.3333；Described threshold value F is set as 0.8803；Described threshold value G is set as 0.6706；Described threshold value H is set as 1.0850；Described threshold value I is set as 0.4621.
3. the product false proof querying method based on machine vision of To enterprises user according to claim 1, it is characterised in that: described step 2) the content of inquiry table include: the left and right ratio of digital 0 correspondence is 0.09918；The left and right ratio of numeral 1 correspondence is 0.09866；The left and right ratio of numeral 2 correspondences is 1.1943；The left and right ratio of numeral 3 correspondences is 1.3929；The left and right ratio of numeral 4 correspondences is 1.5071；The left and right ratio of numeral 5 correspondences is 1.1980；The left and right ratio of numeral 6 correspondences is 0.9035；The left and right ratio of numeral 7 correspondences is 1.1722；The left and right ratio of numeral 8 correspondences is 0.9954；The left and right ratio that numerical character is 9 correspondences is 1.1280；
Described step 2) the content of inquiry table include: the row concentration degree of digital 0 correspondence is 0.2121；The row concentration degree of numeral 1 correspondence is 0.0303；The row concentration degree of numeral 2 correspondences is 0.5606；The row concentration degree of numeral 3 correspondences is 0.409；The row concentration degree of numeral 4 correspondences is 0.0152；The row concentration degree of numeral 5 correspondences is 0.5758；The row concentration degree of numeral 6 correspondences is 0.4394；The row concentration degree of numeral 7 correspondences is 0.6364；The row concentration degree of numeral 8 correspondences is 0.2576；Numerical character is the row concentration degree of 9 correspondences is 0.4394.
4. the product false proof querying method based on machine vision of To enterprises user according to claim 1, it is characterised in that: described step 2) the content of inquiry table include: the row concentration degree of digital 0 correspondence is 0.5500；The row concentration degree of numeral 1 correspondence is 0.4583；The row concentration degree of numeral 2 correspondences is 0.1220；The row concentration degree of numeral 3 correspondences is 0.3889；The row concentration degree of numeral 4 correspondences is 0.0000；The row concentration degree of numeral 5 correspondences is 0.3714；The row concentration degree of numeral 6 correspondences is 0.4500；The row concentration degree of numeral 7 correspondences is 0.1026；The row concentration degree of numeral 8 correspondences is 0.4865；Numerical character is the row concentration degree of 9 correspondences is 0.4500.
5. the product false proof querying method based on machine vision of To enterprises user according to claim 1, it is characterised in that: described step 2) the content of inquiry table include: ratio 1.0059 up and down of digital 0 correspondence；Ratio 1.0214 up and down of numeral 1 correspondence；Ratio 0.9840 up and down of numeral 2 correspondences；Ratio 1.0000 up and down of numeral 3 correspondences；Ratio 1.1693 up and down of numeral 4 correspondences；Ratio 0.8275 up and down of numeral 5 correspondences；Ratio 1.1640 up and down of numeral 6 correspondences；Ratio 0.7765 up and down of numeral 7 correspondences；Ratio 1.0360 up and down of numeral 8 correspondences；Numerical character is ratio 0.8378 up and down of 9 correspondences.
6. the product false proof querying method based on machine vision of To enterprises user according to claim 1, it is characterised in that: described step 2) the content of inquiry table include: the central area of digital 0 correspondence is 1.0000；The central area of numeral 1 correspondence is 0.3333；The central area of numeral 2 correspondences is 0.8745；The central area of numeral 3 correspondences is 0.8266；The central area of numeral 4 correspondences is 0.4949；The central area of numeral 5 correspondences is 0.7166；The central area of numeral 6 correspondences is 0.8197；The central area of numeral 7 correspondences is 0.7560；The central area of numeral 8 correspondences is 0.5215；Numerical character is the central area of 9 correspondences is 0.8136.
7. the product false proof querying method based on machine vision of To enterprises user according to claim 1, it is characterised in that: described step 2) the content of inquiry table include: the ratio of width to height of digital 0 correspondence is 0.6061；The ratio of width to height of numeral 1 correspondence is 0.3636；The ratio of width to height of numeral 2 correspondences is 0.6212；The ratio of width to height of numeral 3 correspondences is 0.5455；The ratio of width to height of numeral 4 correspondences is 0.6364；The ratio of width to height of numeral 5 correspondences is 0.5303；The ratio of width to height of numeral 6 correspondences is 0.6061；The ratio of width to height of numeral 7 correspondences is 0.5909；The ratio of width to height of numeral 8 correspondences is 0.5606；Numerical character is the ratio of width to height of 9 correspondences is 0.6061；
Described step 2) the content of inquiry table include: the ratio of width to height of digital 0 correspondence and central area and be 1.6061；The ratio of width to height and the central area of numeral 1 correspondence and be 0.6969；The ratio of width to height and the central area of numeral 2 correspondences and be 1.4957；The ratio of width to height and the central area of numeral 3 correspondences and be 1.3721；The ratio of width to height and the central area of numeral 4 correspondences and be 1.1313；The ratio of width to height and the central area of numeral 5 correspondences and be 1.2469；The ratio of width to height and the central area of numeral 6 correspondences and be 1.4258；The ratio of width to height and the central area of numeral 7 correspondences and be 1.3469；The ratio of width to height and the central area of numeral 8 correspondences and be 1.0821；Numerical character be the ratio of width to height and the central area of 9 correspondences and be 1.4197.
8. the product false proof inquiry service based on machine vision of To enterprises user, it is characterized in that: include the smart machine and the Antiforge inquiry terminal server that possess machine vision, the described smart machine possessing machine vision is built-in with security code automatic recognition software, the document with one-to-one relationship that the digit strings that the data base of described Antiforge inquiry terminal server includes the coded lock password of product false proof querying method acquisition described in any one of claim 1 to 7, security code is corresponding with bar code is formed.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6366696B1 (en) * | 1996-12-20 | 2002-04-02 | Ncr Corporation | Visual bar code recognition method |
| CN101515299A (en) * | 2009-04-08 | 2009-08-26 | 海南亚元防伪技术研究所 | False-proof/anti-channeling method for recording and examining position relation |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102446273B (en) * | 2011-09-06 | 2013-10-02 | 贵州卓霖防伪科技有限公司 | Method for producing pair code of code-locked anti-counterfeit bottle cap |
-
2012
- 2012-05-31 CN CN201210174981.2A patent/CN102750637B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6366696B1 (en) * | 1996-12-20 | 2002-04-02 | Ncr Corporation | Visual bar code recognition method |
| CN101515299A (en) * | 2009-04-08 | 2009-08-26 | 海南亚元防伪技术研究所 | False-proof/anti-channeling method for recording and examining position relation |
Non-Patent Citations (2)
| Title |
|---|
| Edge Detection Methods for Gray-R Barcode Grade Rod;Gengbiao Chen等;《Proceedings of 2010 International Conference on Broadcast Technology and Multimedia Communication》;20101213;全文 * |
| 一种人民币纸币号码自动识别快速方法;娄元芳等;《微计算机信息》;20081225;第24卷(第12-3期);第210-212页 * |
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