CN108764344A - A kind of method, apparatus and storage device based on limb recognition card - Google Patents
A kind of method, apparatus and storage device based on limb recognition card Download PDFInfo
- Publication number
- CN108764344A CN108764344A CN201810535672.0A CN201810535672A CN108764344A CN 108764344 A CN108764344 A CN 108764344A CN 201810535672 A CN201810535672 A CN 201810535672A CN 108764344 A CN108764344 A CN 108764344A
- Authority
- CN
- China
- Prior art keywords
- card
- candidate region
- angle
- pixel
- edge
- 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
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
- G06V10/753—Transform-based matching, e.g. Hough transform
-
- 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
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
- G06T2207/20061—Hough transform
Landscapes
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Databases & Information Systems (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Computing Systems (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Multimedia (AREA)
- Image Analysis (AREA)
Abstract
This application discloses a kind of method, apparatus and storage device based on limb recognition card, this method include：Detect the edge of card；The corresponding pixel in the edge of card is converted to the sine curve of Hough Hough parameter spaces；Wherein, sinusoidal intersection point is peak point；The ballot poll of peak point is obtained according to the ballot accumulator of Hough parameter spaces；Candidate region is determined according to the ballot poll of peak point；Determining candidate region is matched with angle card template, obtains card recognition result.The advantageous effect that the application can obtain is, votes the stage in hough, and the ballot poll that the marginal point of preset value is only more than to gradient carries out accumulated counts, reduces the interference of noise, the straight line detected is more accurate；Support the detection of multi-angle card and identification；It is detected the result is that accurate location information, the card by perspective transform only need to can reach performance requirement with angle card template matches, need the sorter model that mass data supports without training.
Description
Technical field
This application involves field of artificial intelligence, more particularly, to a kind of method, apparatus based on limb recognition card
And storage device.
Background technology
In the prior art, adaboost (iterative algorithm), the inspection methods such as deep learning is mainly utilized first to be carried out to object
Detection.Recycle svm (Support Vector Machine, support vector machines), the sorting techniques such as deep learning to object into
Row classification.The identification of object is finally a classification problem.Identification object main flow is target detection, feature extraction, target
Classification.With the development of depth learning technology, achieved compared with other methods better performance in target detection, classification evaluation and test,
It is increasingly used in target detection classification application.For example, can be identified using the prior art in the picture that camera acquires
With the presence or absence of children's early education card, while such as word of the content on identification card, letter, the variously-shaped, vehicles, musical instrument,
The contents such as animals and plants carry out voice broadcast or screen display, to promote the cognitive ability of children.
Although depth learning technology is increasingly used in image detection classification field, one practical inspection of training
It surveys, a large amount of training data support of disaggregated model needs, additionally, due to card, angle is not fixed in picture, it is difficult to train one
A model for supporting the detection of multi-angle card is difficult to meet real-time processing and want simultaneously because computationally intensive in embedded device
It asks.
Invention content
The embodiment of the present application provides a kind of method, apparatus and storage device based on limb recognition card.Solve due to
Card angle in picture is not fixed, it is difficult to train a model problem for supporting the detection of multi-angle card, solve simultaneously
Due to computationally intensive, the problem of embedded device is difficult to meet real-time processing requirement.
The embodiment of the present application provides a kind of method based on limb recognition card, and this method includes：
Detect the edge of card；
The corresponding pixel in the edge of the card is converted to the sine curve of Hough Hough parameter spaces；Wherein, just
The intersection point of chord curve is peak point；
The ballot poll of the peak point is obtained according to the ballot accumulator of Hough parameter spaces；
Candidate region is determined according to the ballot poll of the peak point；
The determining candidate region is matched with angle card template, obtains card recognition result.
Further, the edge of the detection card includes：
It is respectively Gx and Gy to calculate pixel gradient in the directions x and the directions y in theorem in Euclid space using sobel operators；
The pixel is added in the directions x with the gradient absolute value in the directions y, the global gradient G of the pixel is obtained；
Global gradient G is more than the pixel of predetermined gradient threshold value as the edge of card.
Further, the gradient direction θ of the corresponding pixel in the edge is θ=arctan2 (Gy, Gx), theorem in Euclid space
Distance ρ=x of the origin to the corresponding pixel in the edge0cosθ+y0sinθ；Wherein, the corresponding pixel in the edge is in Europe
Formula is expressed as (x in space0,y0), it is expressed as in Hough parameter spaces (ρ, θ).
Further, the ballot poll according to the peak point determines that candidate region includes：
The peak point for being more than default poll threshold value according to the ballot poll determines directed line；
Candidate region is formed using the directed line.
Further, the condition for forming candidate region includes：
The angle that the opposite directed line in direction is formed is within the scope of preset first angle；With
The distance between opposite directed line in direction is within the scope of pre-determined distance；With
Angle is within the scope of preset second angle between adjacent directed line；With
The direction of directed line should be all clockwise or be all counterclockwise；With
The candidate region size of formation is in default magnitude range.
Further, candidate region efficient frontier point
It is defined as：Gradient direction directed line corresponding with marginal point is consistent, and the marginal point is answered apart from the marginal point pair
Directed line distance be less than pre-determined distance；
It is described to further include using directed line formation candidate region：
Marginal density is more than the candidate region of default marginal density threshold value as final candidate region；Wherein, edge
Density=candidate region efficient frontier point quantity/candidate region perimeter.
Further, described to match the determining candidate region with angle card template, obtain card recognition
As a result include：
Final candidate region is passed through into perspective transform, formation rule figure；
The regular figure of formation is normalized to consistent with angle card template size；
Regular figure after normalization is matched by difference of two squares matching process with angle card model, card is obtained
Recognition result.
Further, the regular figure by after normalization is carried out by difference of two squares matching process and angle card model
Matching, obtaining card recognition result includes：
Utilize formula R (x ", y ")=∑X ', y '(T (x ', y ')-I (x "+x ', y "+y '))2By the regular figure after normalization
It is matched with angle card model；Wherein, R (x ", y ") indicates that matching score, (x ', y ') indicate angle card model edge
Corresponding pixel, the corresponding pixel in regular figure edge after (x ", y ") expression normalization, T (x ', y ') expressions (x ',
Y ') pixel pixel value, I (x "+x ', y "+y ') indicates the pixel value of (x ", y ") pixel；
Using the minimum corresponding angle card model of matching score as card recognition result.
The embodiment of the present application also provides a kind of device based on limb recognition card, which includes：
Storage device, for storing program data；
Processor, for executing the program data in the storage device to realize the above-mentioned side based on limb recognition card
Method.
The embodiment of the present application also provides a kind of storage devices, are stored thereon with program data, and described program data are used for
The above-mentioned method based on limb recognition card is realized when being executed by processor.
The advantageous effect that the application can obtain is, votes the stage in hough, is only more than predetermined gradient threshold value to gradient
Marginal point ballot poll carry out accumulated counts, the interference of noise can be effectively reduced, the straight line detected is more accurate；It supports more
Angle card detects and identification；It is detected the result is that accurate location information, the card image after perspective transform is only
It needs to can reach performance requirement with angle card template matches, needs the grader mould that mass data supports without training
Type.
Description of the drawings
Attached drawing described herein is used for providing further understanding of the present application, constitutes part of this application, this Shen
Illustrative embodiments and their description please do not constitute the improper restriction to the application for explaining the application.In the accompanying drawings：
Fig. 1 is computer composed structure block diagram；
Fig. 2 is a kind of flow diagram of the method based on limb recognition card provided by the embodiments of the present application；
Fig. 3 is a kind of structural schematic diagram of the device based on limb recognition card provided by the embodiments of the present application；
Fig. 4 is quadrangle card image schematic diagram provided by the embodiments of the present application；
Fig. 5 is that the gradient provided by the embodiments of the present application using sobe l operators calculating pixel in the directions x and y is illustrated
Figure；
Fig. 6 is Hough transform schematic diagram provided by the embodiments of the present application；
Fig. 7 is candidate region schematic diagram provided by the embodiments of the present application.
Specific implementation mode
To keep the purpose, technical scheme and advantage of the application clearer, below in conjunction with the application specific embodiment and
Technical scheme is clearly and completely described in corresponding attached drawing.Obviously, described embodiment is only the application one
Section Example, instead of all the embodiments.Based on the embodiment in the application, those of ordinary skill in the art are not doing
Go out the every other embodiment obtained under the premise of creative work, shall fall in the protection scope of this application.
Fig. 1 is computer composed structure block diagram, and the critical piece of computer is shown.It is processor 110, interior in Fig. 1
115 access system bus 140 of portion's memory 105, bus bridge 120 and network interface, bus bridge 120 are used for bridge system bus
140 and I/O buses 145, I/O interfaces access I/O buses 145, and USB interface and external memory are connect with I/O interfaces.Fig. 1
In, processor 110 can be one or more processors, and each processing can have one or more processor cores；It is interior
Portion's memory 105 is volatile memory, such as register, buffer, various types of random access memory etc.；It is calculating
When machine booting operation, the data in internal storage 105 include operating system and application program；Network interface 115 can be with
For Ethernet interface, optical fiber interface etc.；System bus 140 can be used for data information, address information and control letter
Breath；Bus bridge 120 can be used for carrying out protocol conversion, system bus protocol is converted to I/O agreements or by I/O protocol conversions
It is system bus protocol to realize data transmission；I/O buses 145 are used for data information and control information, can be with bus termination
Resistance or circuit interfere to reduce signal reflex；I/O interfaces 130 are mainly connect with various external equipments, for example, keyboard, mouse,
Sensor etc., flash memory can access I/O buses by USB interface, and external memory is nonvolatile memory, such as firmly
Disk, CD etc..After power the computer, processor can will be stored in external storage digital independent therein to storage inside
In device, and storage inside computer instruction therein is handled, completes the function of operating system and application program.This shows
Example computer can be desktop computer, laptop, tablet computer, smart mobile phone etc..
Fig. 2 is a kind of flow diagram of the method based on limb recognition card provided by the embodiments of the present application, the flow
Schematic diagram includes：
Step 205, the edge of card is detected；
In the present embodiment, for identifying the quadrangle card in image shown in Fig. 4.Optionally, utilization is shown in fig. 5
It is respectively Gx and Gy that sobel operators, which calculate gradient of the pixel on image shown in Fig. 4 in the directions theorem in Euclid space x and y, specifically
For, when calculating Gx, the picture on card image shown in Fig. 4 is traversed using the left side template of sobel operators template shown in fig. 5
Vegetarian refreshments, the 3rd columns value are multiplied by the pixel value of corresponding three pixels respectively, then by three product additions, result A；1st row
Numerical value is multiplied by the pixel value of corresponding three pixels respectively, then by three product additions, result B；The difference of A and B is
Gx.When calculating Gy, the pixel on image shown in Fig. 4 is traversed using the right template of sobel operators template shown in fig. 5,
1st line number value is multiplied by the pixel value of corresponding three pixels respectively, then by three product additions, result C；3rd line number value
It is multiplied by the pixel value of corresponding three pixels respectively, then by three product additions, result D；The difference of C and D is Gy；It will
Gradient G x of the above-mentioned pixel in the directions x with y is added with Gy absolute values, obtains global gradient G=Gx+Gy of the pixel；
Global gradient G is more than predetermined gradient threshold value TGPixel as edge.In the present embodiment, TG values are 40.
Further, the gradient direction θ of the corresponding pixel in above-mentioned edge is θ=arctan2 (Gy, Gx), theorem in Euclid space
Distance ρ=x of the origin to the corresponding pixel in above-mentioned edge0cosθ+y0sinθ；Wherein, the corresponding pixel in above-mentioned edge is in Europe
Formula is expressed as (x in space0,y0), it is expressed as in Hough parameter spaces (ρ, θ).
Step 210, the sine that the corresponding pixel in the edge of the card is converted to Hough Hough parameter spaces is bent
Line；Wherein, sinusoidal intersection point is peak point；
Hough transform is in image procossing from the method for image recognition geometry.It utilizes the duality of point and line, will
The given curve in original image space becomes a point in Hough parameter spaces by curve representation form.So original graph
As the test problems of given curve translate into the spike problem in Hough parameter spaces, that is, detects overall permanence and be converted into inspection
Survey local characteristics.Point in theorem in Euclid space on straight line is a sine curve in Hough parameter spaces；In theorem in Euclid space
Multiple points on same straight line are a sinusoid series in Hough parameter spaces and set of curves intersects at a point, and claim this
Point is peak point.And the peak point under Hough parameter spaces, then the straight line under theorem in Euclid space is corresponded to, as shown in Figure 6.
Pixel is expressed as (x in theorem in Euclid space0,y0), and (ρ, θ) is expressed as in Hough parameter spaces, P points are Hough
Peak point in parameter space, the meaning represented by it are the l straight lines of theorem in Euclid space.Straight line phase is detected with traditional hough
Than detecting directed line.Directional information has important meaning when being subsequently formed polygon, while considering direction in the ballot stage
Information can remove the interference of ambient noise.
In the present embodiment, four sides of the quadrangle card in image shown in Fig. 4 correspond to four of theorem in Euclid space it is straight
Line.Point on four straight lines of theorem in Euclid space is converted to four sinusoidal songs of Hough parameter spaces respectively using Hough transform
Line cluster；Wherein, the intersection point of each sinusoid series is peak point, and above-mentioned peak point corresponds to four straight lines of theorem in Euclid space respectively.
Step 215, the ballot poll of the peak point is obtained according to the ballot accumulator of Hough parameter spaces；
In the present embodiment, in Hough parameter spaces, it is a ballot unit to define 52 ° of pixels.Optionally, it traverses
Hough parameter spaces are each voted the ballot accumulator of unit；The ballot of above-mentioned peak point is obtained according to above-mentioned ballot accumulator
Poll.
Step 220, candidate region is determined according to the ballot poll of the peak point；
Optionally, the peak point for default poll threshold value being more than according to ballot poll determines directed line；Using above-mentioned oriented
Straight line forms candidate region.
Further, in the present embodiment, the peak value that ballot poll is more than maximum poll quantity 5 percent is first counted
Point determines directed line further according to the highest peak point of poll of voting in the peak point counted；It is linear using above-mentioned directed line
Quadrangularly.Since quadrangle sum may be excessively high, in order to reduce quantity, the quadrangle by satisfaction as the condition of candidate region
As candidate region, as shown in Figure 7.
Further, the candidate region of above-mentioned formation needs while meeting the following conditions：The opposite directed line in direction is linear
At angle within the scope of preset first angle；The distance between opposite directed line in direction is within the scope of pre-determined distance；Phase
Angle is within the scope of preset second angle between adjacent directed line；The direction of directed line should be all clockwise or be all
Counterclockwise；The candidate region size of formation is in default magnitude range.
Still further, the candidate region of above-mentioned formation needs while meeting the following conditions：The opposite directed line in direction
The angle of formation is in 180 ± 30 degree；Magnitude range ± 20 picture of the distance between the opposite directed line in direction in card
In element；Angle is in 90 ± 30 degree between adjacent directed line；The direction of directed line should be all clockwise or be all
Counterclockwise；The candidate region size of formation is in size ± 30% of card.
Candidate region as shown in Figure 7 disclosure satisfy that all conditions of above-mentioned formation candidate region, but need further verification
Whether candidate region is true card.Specifically, the candidate region that marginal density is more than to default marginal density threshold value is made
For final candidate region；In the present embodiment, it is 50% to preset marginal density threshold value；Wherein, marginal density=candidate region
Efficient frontier point quantity/candidate region perimeter；In the present embodiment, candidate region efficient frontier point quantity as shown in Figure 7 is 60
Pixel, 100 pixels of candidate region Zhou Changwei, then marginal density is 60%, is more than default marginal density threshold value 50%, then such as Fig. 7
It shown candidate region can be as final candidate region.Efficient frontier point is defined as：Gradient direction is corresponding with marginal point to be had
It is consistent to straight line, and distance of the marginal point apart from the corresponding directed line of the marginal point is less than pre-determined distance.In the present embodiment
In, above-mentioned pre-determined distance is 3 pixels.
Step 225, the determining candidate region is matched with angle card template, obtains card recognition result；
Optionally, final candidate region is passed through into perspective transform, formation rule figure；By the regular figure of formation
It normalizes to consistent with angle card template size；Regular figure after normalization is passed through into difference of two squares matching process and angle card
Piece model is matched, and card recognition result is obtained.
Further, formula R (x ", y ")=∑ is utilizedX ', y '((x "+x ', y "+y ' 2 will be after normalization by T (x ', y ')-I
Regular figure is matched with angle card model；Wherein, R (x ", y ") indicates that matching score, (x ', y ') indicate angle card
The corresponding pixel of edge of model, (x ", y ") indicate the corresponding pixel in regular figure edge after normalization, T (x ', y ') table
Show the pixel value of (x ', y ') pixel, I (x "+x ', y "+y ') indicates the pixel value of (x ", y ") pixel；By minimum matching
The corresponding angle card model of score is as card recognition result.
Still further, in the present embodiment, the quadrangle card in image shown in Fig. 4 has corresponding four angle cards
Model, four angles are respectively 0 degree, 90 degree, 180 degree and 270 degree.Utilize formula R (x ", y ")=∑X ', y '(T (x ', y ')-I
(x "+x ', y "+y '))2By the regular rectangular shape after normalization respectively with 0 degree, 90 degree, 180 degree and 270 degree of angle card models into
Row matching, it is respectively 10,40,60 and 90 to obtain matching score；By minimum 10 corresponding 0 degree of angle card models of matching score
As card recognition result.It is matched by difference of two squares matching process, matching score is smaller, and matching result is more accurate.
Compared with prior art, the advantageous effect of the embodiment of the present application is, votes the stage in hough, only big to gradient
Accumulated counts are carried out in the ballot poll of the marginal point of predetermined gradient threshold value, the interference of noise can be effectively reduced, what is detected is straight
Line is more accurate；Support the detection of multi-angle card and identification；It is detected the result is that accurate location information, by perspective transform
Card image afterwards only needs to can reach performance requirement with angle card template matches, needs mass data branch without training
The sorter model of support.
Fig. 3 is a kind of structural schematic diagram of the device based on limb recognition card provided by the embodiments of the present application；The structure
Schematic diagram includes：Storage device 305 and processor 310；
Storage device 305, for storing program data；
Processor 310, for executing the program data in the storage device to realize the edge of detection card；It will be described
The corresponding pixel in edge of card is converted to the sine curve of Hough Hough parameter spaces；Wherein, sinusoidal intersection point is
Peak point；The ballot poll of the peak point is obtained according to the ballot accumulator of Hough parameter spaces；According to the peak point
Ballot poll determines candidate region；The determining candidate region is matched with angle card template, obtains card recognition
As a result.
Compared with prior art, the advantageous effect of the embodiment of the present application is, votes the stage in hough, only big to gradient
Accumulated counts are carried out in the ballot poll of the marginal point of predetermined gradient threshold value, the interference of noise can be effectively reduced, what is detected is straight
Line is more accurate；Support the detection of multi-angle card and identification；It is detected the result is that accurate location information, by perspective transform
Card image afterwards only needs to can reach performance requirement with angle card template matches, needs mass data branch without training
The sorter model of support.
Present invention also provides a kind of storage devices, are stored thereon with program data, described program data are for being handled
The edge of detection card is realized when device executes；It is empty that the corresponding pixel in the edge of the card is converted into Hough Hough parameters
Between sine curve；Wherein, sinusoidal intersection point is peak point；Institute is obtained according to the ballot accumulator of Hough parameter spaces
State the ballot poll of peak point；Candidate region is determined according to the ballot poll of the peak point；By the determining candidate region
It is matched with angle card template, obtains card recognition result.
It should also be noted that, the terms "include", "comprise" or its any other variant are intended to nonexcludability
Including so that process, method, commodity or equipment including a series of elements include not only those elements, but also wrap
Include other elements that are not explicitly listed, or further include for this process, method, commodity or equipment intrinsic want
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that wanted including described
There is also other identical elements in the process of element, method, commodity or equipment.
Above is only an example of the present application, it is not intended to limit this application.For those skilled in the art
For, the application can have various modifications and variations.It is all within spirit herein and principle made by any modification, equivalent
Replace, improve etc., it should be included within the scope of claims hereof.
Claims (10)
1. a kind of method based on limb recognition card, which is characterized in that this method includes：
Detect the edge of card；
The corresponding pixel in the edge of the card is converted to the sine curve of Hough Hough parameter spaces；Wherein, sinusoidal bent
The intersection point of line is peak point；
The ballot poll of the peak point is obtained according to the ballot accumulator of Hough parameter spaces；
Candidate region is determined according to the ballot poll of the peak point；
The determining candidate region is matched with angle card template, obtains card recognition result.
2. the method according to claim 1 based on limb recognition card, which is characterized in that the edge of the detection card
Including：
It is respectively Gx and Gy to calculate pixel gradient in the directions x and the directions y in theorem in Euclid space using sobel operators；
The pixel is added in the directions x with the gradient absolute value in the directions y, the global gradient G of the pixel is obtained；
Global gradient G is more than the pixel of predetermined gradient threshold value as the edge of card.
3. the method according to claim 2 based on limb recognition card, which is characterized in that the corresponding pixel in the edge
The gradient direction θ of point is θ=arctan2 (Gy, Gx), the distance ρ of theorem in Euclid space origin to the corresponding pixel in the edge=
x0cosθ+y0sinθ；Wherein, the corresponding pixel in the edge is expressed as (x in theorem in Euclid space0,y0), it is empty in Hough parameters
Between in be expressed as (ρ, θ).
4. the method according to claim 3 based on limb recognition card, which is characterized in that described according to the peak point
Ballot poll determine that candidate region includes：
The peak point for being more than default poll threshold value according to the ballot poll determines directed line；
Candidate region is formed using the directed line.
5. the method according to claim 4 based on limb recognition card, which is characterized in that the formation candidate region
Condition includes：
The angle that the opposite directed line in direction is formed is within the scope of preset first angle；With
The distance between opposite directed line in direction is within the scope of pre-determined distance；With
Angle is within the scope of preset second angle between adjacent directed line；With
The direction of directed line should be all clockwise or be all counterclockwise；With
The candidate region size of formation is in default magnitude range.
6. the method according to claim 4 based on limb recognition card, which is characterized in that described to utilize the directed line
Line forms candidate region：
Marginal density is more than the candidate region of default marginal density threshold value as final candidate region；Wherein, marginal density
=candidate region efficient frontier point quantity/candidate region perimeter；Wherein, efficient frontier point in candidate region is marginal point gradient direction
Directed line corresponding with marginal point is consistent, and distance of the marginal point apart from the corresponding directed line of the marginal point is less than default
The marginal point of distance.
7. the method according to claim 6 based on limb recognition card, which is characterized in that described by the determining time
Favored area is matched with angle card template, is obtained card recognition result and is included：
Final candidate region is passed through into perspective transform, formation rule figure；
The regular figure of formation is normalized to consistent with angle card template size；
Regular figure after normalization is matched by difference of two squares matching process with angle card model, card recognition is obtained
As a result.
8. the method according to claim 7 based on limb recognition card, which is characterized in that the rule by after normalization
Then figure is matched by difference of two squares matching process with angle card model, is obtained card recognition result and is included：
Utilize formula R (x ", y ")=∑X ', y '(T (x ', y ')-I (x "+x ', y "+y '))2By after normalization regular figure and angle
Degree card model is matched；Wherein, R (x ", y ") indicates that matching score, (x ', y ') indicate that angle card model edge corresponds to
Pixel, (x ", y ") indicate normalization after the corresponding pixel in regular figure edge, T (x ', y ') indicate (x ', y ') as
The pixel value of vegetarian refreshments, I (x "+x ', y "+y ') indicate the pixel value of (x ", y ") pixel；
Using the minimum corresponding angle card model of matching score as card recognition result.
9. a kind of storage device, is stored thereon with program data, which is characterized in that described program data are for being executed by processor
The method based on limb recognition card described in any one of Shi Shixian claims 1-8.
10. a kind of card based on edge detects identification device, which is characterized in that the device includes：
Storage device, for storing program data；
Processor, the base for executing the program data in the storage device to realize described in any one of claim 1-8
In the method for limb recognition card.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810535672.0A CN108764344B (en) | 2018-05-29 | 2018-05-29 | Method and device for identifying card based on edge and storage equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810535672.0A CN108764344B (en) | 2018-05-29 | 2018-05-29 | Method and device for identifying card based on edge and storage equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108764344A true CN108764344A (en) | 2018-11-06 |
| CN108764344B CN108764344B (en) | 2021-08-24 |
Family
ID=64003898
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810535672.0A Active CN108764344B (en) | 2018-05-29 | 2018-05-29 | Method and device for identifying card based on edge and storage equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108764344B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109522901A (en) * | 2018-11-27 | 2019-03-26 | 中国计量大学 | A kind of tomato plant stalk method for identification of edge based on edge duality relation |
| CN110138999A (en) * | 2019-05-30 | 2019-08-16 | 苏宁金融服务(上海)有限公司 | A kind of papers-scanning method and device for mobile terminal |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101110100A (en) * | 2006-07-17 | 2008-01-23 | 松下电器产业株式会社 | Method and device for detecting geometric figure of image |
| CN101388077A (en) * | 2007-09-11 | 2009-03-18 | 松下电器产业株式会社 | Target shape detecting method and device |
| CN102156869A (en) * | 2006-07-17 | 2011-08-17 | 松下电器产业株式会社 | Method and device for detecting shapes formed by combining arbitrary line segments |
| JP2011186916A (en) * | 2010-03-10 | 2011-09-22 | Fuji Electric Co Ltd | Image recognition device, image recognition method and image recognition program |
| CN104268864A (en) * | 2014-09-18 | 2015-01-07 | 小米科技有限责任公司 | Card edge extracting method and device |
-
2018
- 2018-05-29 CN CN201810535672.0A patent/CN108764344B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101110100A (en) * | 2006-07-17 | 2008-01-23 | 松下电器产业株式会社 | Method and device for detecting geometric figure of image |
| CN102156869A (en) * | 2006-07-17 | 2011-08-17 | 松下电器产业株式会社 | Method and device for detecting shapes formed by combining arbitrary line segments |
| CN101388077A (en) * | 2007-09-11 | 2009-03-18 | 松下电器产业株式会社 | Target shape detecting method and device |
| CN101785028A (en) * | 2007-09-11 | 2010-07-21 | 松下电器产业株式会社 | Image processing device and image processing method |
| JP2011186916A (en) * | 2010-03-10 | 2011-09-22 | Fuji Electric Co Ltd | Image recognition device, image recognition method and image recognition program |
| CN104268864A (en) * | 2014-09-18 | 2015-01-07 | 小米科技有限责任公司 | Card edge extracting method and device |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109522901A (en) * | 2018-11-27 | 2019-03-26 | 中国计量大学 | A kind of tomato plant stalk method for identification of edge based on edge duality relation |
| CN109522901B (en) * | 2018-11-27 | 2020-11-03 | 中国计量大学 | Tomato plant stem edge identification method based on edge dual relation |
| CN110138999A (en) * | 2019-05-30 | 2019-08-16 | 苏宁金融服务(上海)有限公司 | A kind of papers-scanning method and device for mobile terminal |
| CN110138999B (en) * | 2019-05-30 | 2022-01-07 | 苏宁金融服务(上海)有限公司 | Certificate scanning method and device for mobile terminal |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108764344B (en) | 2021-08-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110689010B (en) | Certificate identification method and device | |
| CN109697414B (en) | Text positioning method and device | |
| CN113065536B (en) | Method of processing table, computing device, and computer-readable storage medium | |
| WO2021051939A1 (en) | Document area positioning method and device | |
| CN110992325A (en) | Target counting method, device and equipment based on deep learning | |
| CN111242124A (en) | Certificate classification method, device and equipment | |
| US11094049B2 (en) | Computing device and non-transitory storage medium implementing target object identification method | |
| CN112597940B (en) | Certificate image recognition method and device and storage medium | |
| CN104281831B (en) | A kind of method and apparatus of person's handwriting checking | |
| CN112001406A (en) | Text region detection method and device | |
| JP2012512478A (en) | Method, apparatus and computer program for providing face pose estimation | |
| CN109165657A (en) | A kind of image feature detection method and device based on improvement SIFT | |
| CN111461767B (en) | Deep learning-based Android deceptive advertisement detection method, device and equipment | |
| CN114022702A (en) | Intelligent warehouse management method and device, electronic equipment and storage medium | |
| CN108764344A (en) | A kind of method, apparatus and storage device based on limb recognition card | |
| CN107122093B (en) | Information frame display method and device | |
| CN110909816B (en) | Picture identification method and device | |
| CN105190689A (en) | Image processing including adjoin feature based object detection, and/or bilateral symmetric object segmentation | |
| CN113591884B (en) | Method, device, equipment and storage medium for determining character recognition model | |
| CN111931557A (en) | Specification identification method and device for bottled drink, terminal equipment and readable storage medium | |
| CN112396057A (en) | Character recognition method and device and electronic equipment | |
| CN114220103B (en) | Image recognition method, device, equipment and computer readable storage medium | |
| CN108734164A (en) | Card, identification card method, paint this reading machine people and storage device | |
| CN111695372A (en) | Click-to-read method and click-to-read data processing method | |
| CN112464753B (en) | Method and device for detecting key points in image and terminal equipment |
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 | ||
| CP03 | Change of name, title or address |
Address after: 100000 Room D529, No. 501, Floor 5, Building 2, Fourth District, Wangjing Dongyuan, Chaoyang District, Beijing Patentee after: Beijing Wuling Technology Co.,Ltd. Address before: 100102 room 3602, 36 / F, building 101, building 13, District 4, Wangjing East Garden, Chaoyang District, Beijing Patentee before: BEIJING LING TECHNOLOGY Co.,Ltd. |
|
| CP03 | Change of name, title or address | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20221223 Address after: 100000 Room 815, Floor 8, Building 6, Yard 33, Guangshun North Street, Chaoyang District, Beijing Patentee after: Luka (Beijing) Intelligent Technology Co.,Ltd. Address before: 100000 Room D529, No. 501, Floor 5, Building 2, Fourth District, Wangjing Dongyuan, Chaoyang District, Beijing Patentee before: Beijing Wuling Technology Co.,Ltd. |
|
| TR01 | Transfer of patent right |