CN106570511A - Keyboard character defect online detection system based on computer vision and keyboard character defect online detection method thereof - Google Patents

Keyboard character defect online detection system based on computer vision and keyboard character defect online detection method thereof Download PDF

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Publication number
CN106570511A
CN106570511A CN201610943910.2A CN201610943910A CN106570511A CN 106570511 A CN106570511 A CN 106570511A CN 201610943910 A CN201610943910 A CN 201610943910A CN 106570511 A CN106570511 A CN 106570511A
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character
keyboard
image
computer vision
defect
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CN106570511B (en
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曾丹
卿杨春
张之江
包怡欣
李剑
沈为
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University of Shanghai for Science and Technology
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/24—Aligning, centring, orientation detection or correction of the image
    • G06V10/243—Aligning, centring, orientation detection or correction of the image by compensating for image skew or non-uniform image deformations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/06—Recognition of objects for industrial automation

Abstract

The invention relates to a keyboard character online detection system based on computer vision and a keyboard character online detection method thereof. The system comprises an industrial camera, a computer, a single-chip microcomputer system, a frequency converter, a motor and a conveying belt. The method comprises the operation steps that (1) the image quality is enhanced by using an image gray processing and median filtering method; (2) image matching is performed by using surf feature points; (3) the character positions are determined; (4) sequence numbering is performed on the characters; and (5) defect character separation is performed. An SVM classification method is used by the detection algorithm, the normal characters are marked as positive samples, the wrong characters are marked as negative samples, and the size, the center and the moment features of the samples are extracted to be trained. The system is easy to install and high in detection efficiency and plays an important role in the keyboard production process.

Description

Keypad character defect on-line detecting system and method based on computer vision
Technical field
The present invention relates to a kind of method based on support vector machine, a kind of keyboard word based on computer vision of specific design Symbol defect on-line detecting system and method, detect to keyboard defect, by being automatically positioned keypad character, extract the big of them Little, center of gravity, girth, the feature such as square is trained, and realizes the detection to character defect, construct complete set by computer, Industrial camera, light source, the keypad character on-line detecting system of the composition of single-chip microcomputer and method.
Background technology
In keyboard production process, need that button on keyboard is printed and installed, but in printing and installation process In, it is easy to occur printing dirty, the phenomenon such as setup error.For the quality of the keyboard production that is rigid in checking up, monitor in real time is needed The quality of leaflet on keyboard production line, in time removes underproof product.
Past, printing enterprise was all completing the quality testing of product with control, a veteran technology by workman Workman is exactly a detecting system, but manual detection does not ensure that steady quality, usually occurs that flase drop, missing inspection show As, and manual detection speed is too slow, cost is too high, examination criteria differs.Under the pressure of the shortcoming and defect of traditional detection, seek one Plant the trend of the times that objective efficient keyboard printing defects online measuring technique is printing industry development.
At present, computer vision quickly grows, and with visual processes technology defects detection is done, and highlights in industrial quarters Big advantage, reduces production cost, improves production efficiency.Therefore computer vision technique is applied in the inspection of keyboard defect Big advantage will be played in survey.
The content of the invention
Present invention aims to the defect that prior art is present, there is provided a kind of keyboard based on computer vision lacks Sunken on-line detecting system and method, by it in combination with automation control system, the keyboard defect for having built complete set is online Detecting system and method, improve the efficiency in production process, it is ensured that the quality of production.
To reach above-mentioned purpose, idea of the invention is that：
1. the detecting system with computer system as core is set up, the letter of transmission tape running speed will be controlled by computer Single-chip microcomputer number is sent to, single-chip microcomputer is converted into analogue signal and gives frequency changer through D/A, and then controls the operation speed of conveyer belt Degree, simultaneous computer controls the exposure time interval of CCD camera.
2. image capturing system, by internetwork connection mode the image that industry camera is collected is obtained.
3. Image semantic classification, to the picture that shoots, we need to carry out gray processing, binaryzation and noise reduction, remove unrelated letter Breath, it is ensured that substrate processing time.
4. keyboard is extracted, and the picture for photographing finds position of the keyboard in picture by template matching method, is easy to the later stage Keyboard is processed.
5. keycap is extracted, and to keyboard by binary conversion treatment, the character being partitioned on keyboard, facilitates later stage character to contrast.
6. slant correction, keyboard does not ensure that on a production line its each position, all in horizontality, may be located In heeling condition, by carrying out Feature Points Matching with template picture, obtain its transformation matrix, so can by keyboard transform to Template picture position identical state, improves the accuracy of later stage contrast.
7. feature extraction, by extracting character girth on keyboard, area, center of gravity, moment characteristics, as the spy of character on keyboard Levy.
8. Defect Comparison, the feature of said extracted is input in support vector machine classifier and is trained, and obtains one The grader for training, when detection when, by it is to be checked measure picture be input to grader just can be by defect keyboard detection out.
Conceived according to foregoing invention, the present invention adopts following technical proposals, a kind of keypad character based on computer vision Defect on-line detecting system, including industry camera (1), computer (2), Single Chip Microcomputer (SCM) system (3), converter (4), motor (5) With conveyer belt (6), it is characterised in that：Keyboard (7) on the industry camera (1) alignment conveyer belt (6) shoots, and it is exported It is connected to computer (2), computer (2) connection Single Chip Microcomputer (SCM) system (3), output Jing converters (4) the connection motor of single-chip microcomputer (3) (5), motor (5) drives conveyer belt (6) to carry out；Keypad character image is obtained using industry camera (1), using computer vision Svm classifier method detected image deflects, judges the quality of production of keyboard in process of production.Comprise the following steps that：
1. using computer (2) and single-chip microcomputer msp430 serial communications, the speed of conveyer belt (6) is controlled；
2. image gray processing, the method for medium filtering is used to improve the quality of image；
3. images match is carried out using surf characteristic points, inclined image rotation is same into being in template picture
One level；
4. contours extract is carried out to character using eight adjacent methods, by obtaining its profile, calculates these character outlines
Minimum enclosed rectangle, determine the position of character；
5. the coordinate of boundary rectangle is calculated, order label is carried out to character；
6. the size of character is extracted, center, moment characteristics are trained using SVM classifier, by normal key
Disk character and defect character separation.
Conceived according to foregoing invention, the present invention adopts following technical proposals：
A kind of keypad character defect on-line detecting system based on computer vision, including industry camera, computer, list Piece machine system, converter, motor and conveyer belt, it is characterised in that：Keyboard on the industry camera alignment conveyer belt shoots, And its output is connected to computer, computer connection Single Chip Microcomputer (SCM) system, the output Jing converters connection motor of single-chip microcomputer, motor belt motor Dynamic conveyer belt operation；Keypad character image is obtained using industry camera, is lacked image using computer vision svm classifier method It is trapped into going detection, judges the quality of production of keyboard in process of production；Using computer and single-chip microcomputer msp430 serial communications, control The speed of conveyer belt processed.
A kind of keypad character defect online detection method based on computer vision, is operated using said system, its It is characterised by that concrete operation step is as follows：
1. image gray processing, the method for medium filtering is used to improve the quality of image；
2. images match is carried out using surf characteristic points, inclined image rotation is in into same water into template picture It is flat；
3. contours extract is carried out to character using eight adjacent methods, by obtaining its profile, calculates these character outlines most Little boundary rectangle, determines the position of character；
4. the coordinate of boundary rectangle is calculated, order label is carried out to character；
5. extract the size of character, center, moment characteristics are trained using SVM classifier, by normal keyboard character with lack Sunken character separation.
The step (2) carries out images match using surf characteristic points, and its purpose is in order to by the inclination picture on conveyer belt The angle of template picture is rotated into, concrete grammar is as follows：
1. respectively to template image and conveyer belt photographs to keyboard picture extract its surf feature, the spy for obtaining respectively It is a little one 64 vector tieed up to levy.
2. the surf characteristic points on two width images are traveled through respectively, their Euclidean distance is calculated, and find the minimum point of distance, It is exactly the good point of two width images match, using the point for matching, obtains single mapping matrix H.
3. using single mapping matrix H, the keyboard picture on conveyer belt is carried out into affine change, obtains the image for having corrected.
The step (5) is trained using SVM classifier, and it is positive sample that 2000 normal picture indicias are taken respectively, 2000 defective picture indicias are negative sample, and their size, center of gravity, girth, moment characteristics, as SVM's are extracted respectively Input, is trained, and using the SVM classifier for obtaining defects detection is carried out.
The present invention compared with prior art, enters with following obvious prominent substantive distinguishing features and notable technology Step：
1. when image rectification is carried out, characteristic matching is carried out using surf features, obtain picture to be measured with template picture Mapping relations, using surf features ensure it is stable in the case of, real-time also can be guaranteed, can be quick, it is accurately complete Into the correction of testing image.
2. in order to obtain the position of testing image and template image each character, herein by Mathematical Morphology method and eight neighborhood Contour detecting method, obtains the character zone of primary segmentation.And using the sort algorithm for designing, obtain region of interest Domain, meeting has the index of a determination on a button, it is ensured that detection every time can be by the character of accurately contrast same position.
3. when character is contrasted, by the size for extracting character, center, moment characteristics can effectively represent the spy of keypad character Levy, and be trained by using SVM classifier, it is not enough to successfully manage sample, most normal keyboard character and defect at last Character separation.
Description of the drawings
Fig. 1 is overall structure diagram of the present invention.
Fig. 2 is global procedures block diagram of the present invention.
Fig. 3 is image correction program block diagram.
Fig. 4 is character outline tracing program block diagram.
Fig. 5 is the flow chart of software in the embodiment of the present invention three.
Fig. 6 is to build SVM classifier flow chart.
Fig. 7 is present invention correction matching schematic diagram.
Fig. 8 is the schematic diagram after correction.
Fig. 9 is the schematic diagram of Morphological scale-space.
Figure 10 is character locating schematic diagram.
Figure 11 is character sequence schematic diagram.
Specific embodiment
The preferred embodiments of the present invention combine accompanying drawing it is shallow state it is as follows：
Embodiment one：
Referring to Fig. 1, based on the keypad character defect on-line detecting system of computer vision, including industry camera (1), meter Calculation machine (2), Single Chip Microcomputer (SCM) system (3), converter (4), motor (5) and conveyer belt (6), it is characterised in that：The industry camera (1) keyboard (7) being aligned on conveyer belt (6) shoots, and its output is connected to computer (2), computer (2) connection single-chip microcomputer system System (3), output Jing converters (4) connection motor (5) of single-chip microcomputer (3), motor (5) drives conveyer belt (6) operation；Using industry Photographic head (1) obtains keypad character image, is detected image deflects using computer vision svm classifier method, judges The quality of production of keyboard in production process；Using computer (2) and single-chip microcomputer msp430 serial communications, conveyer belt (6) is controlled Speed.
Embodiment two：
Referring to Fig. 2, Figure 11, based on the keypad character defect on-line checking detection method of computer vision, using according to power Profit requires being operated based on the keypad character defect on-line detecting system of computer vision described in 1, it is characterised in that concrete Operating procedure is as follows：
6. image gray processing, the method for medium filtering is used to improve the quality of image；
7. images match is carried out using surf characteristic points, inclined image rotation is in into same water into template picture It is flat；
8. contours extract is carried out to character using eight adjacent methods, by obtaining its profile, calculates these character outlines most Little boundary rectangle, determines the position of character；
9. the coordinate of boundary rectangle is calculated, order label is carried out to character；
The size of character is extracted, center, moment characteristics are trained using SVM classifier, by normal keyboard character and defect Character separation.
The described keypad character defect online detection method based on computer vision, it is characterised in that the step (2) Carry out images match using surf characteristic points, its purpose in order to will on conveyer belt incline picture rotation into template picture angle Degree, concrete grammar is as follows：
4. respectively to template image and conveyer belt photographs to keyboard picture extract its surf feature, the spy for obtaining respectively It is a little one 64 vector tieed up to levy.
5. the surf characteristic points on two width images are traveled through respectively, their Euclidean distance is calculated, and find the minimum point of distance, It is exactly the good point of two width images match, using the point for matching, obtains single mapping matrix H.
6. using single mapping matrix H, the keyboard picture on conveyer belt is carried out into affine change, obtains the image for having corrected.
The step (5) is trained using SVM classifier, and it is positive sample that 2000 normal picture indicias are taken respectively, 2000 defective picture indicias are negative sample, and their size, center of gravity, girth, moment characteristics, as SVM's are extracted respectively Input, is trained, and using the SVM classifier for obtaining defects detection is carried out.
Embodiment three：
Not only Detection accuracy is high but also detection speed is fast for the method for keyboard defect on-line checking based on computer vision. The present embodiment can be divided into two stages of hardware designs and software design, and detailed step is as follows：
1. hardware designs (referring to Fig. 1)
Fig. 1 for the system a hardware frame, detecting system mainly by a computer, msp430 single-chip microcomputers, one The industry camera composition of 5000000 pixels.Because CCD camera and conveyer belt are all to work independently, it must be determined that CCD camera Time of exposure so that every time camera can photograph on keyboard, so time of exposure must be calculated.
If the distance between two neighboring keyboard is Δ s, transmission tape running speed is v, and time of exposure is Δ t, by Δ s= Δ t × v can show that the time of exposure of camera, so only needing computer and msp430 microcomputer series interface communications, sends control life Order, it is ensured that keyboard can be shot complete by camera every time.
In order to control to transmit tape running speed v, single-chip microcomputer is converted into analogue signal and send a frequency changer, enters by D/A modules And control speed of service v of conveyer belt.
2. software design (referring to Fig. 5)
2.1 Image semantic classification
(1) gray proces
Mainly cromogram is transformed into into gray-scale maps, coloured picture is made up of each RGB channel, and gray proces are mainly by the every of coloured picture Individual pixel rgb value is calculated as gray value.Formula is as follows：
Gray=0.11R+0.59G+0.30B (2.1)
(2) Filtering Processing
The purpose of image filtering is that, in order to reduce the noise of keyboard image, these noises are from many-sided reason.In keyboard During production, due to reflective stronger, these local images easily whiten, so as to be formed for keyboard recess and corner It is miscellaneous, the normal cutting procedure of interferogram picture, it is necessary to filtered.We are carried out respectively using mean filter and medium filtering Experiment, wave filter size adopts 3*3,5*5,7*7.Can effectively be filtered using the medium filtering mode of 7*7 through Experimental comparison Miscellaneous point in except image.
(3) binaryzation
Binary image can in the background project object pixel so that the contrast of image strengthens, moreover it is possible to reduce follow-up The complexity of process and time.Because illumination condition is almost fixation during keyboard detection, so in conversion process, What a threshold value we only need to arrange, and the pixel of the point is 1 more than this threshold value, is 0 less than this threshold value.Binaryzation formula is such as Under：
2.2 image slant corrections
When producing on keyboard production line, do not ensure that their position is horizontally oriented forever, can cause to incline, such as Fruit angle of inclination is larger, then can have a strong impact on the identification to defect character, so we will be according to the practical situation of production to figure As being corrected.We are corrected using the matching algorithm based on surf, and matching is as shown in Fig. 2 the figure such as Fig. 3 institutes after correction Show.
If p=is (x1,y1) coordinate of certain point, q=(x in image to be corrected2,y2) be correction after image midpoint coordinate, Then there is affine transformation relationship：
Then need to obtain affine matrix, make the affine matrix be：
In order to obtain above-mentioned affine matrix, need to obtain the p in image, the position of q, extract respectively image to be rotated and The surf features of template image, the matching that the method matched with arest neighbors obtains two width images is right, in conjunction with RANSAC and minimum Square law, obtains the affine matrix between image.
2.3 keycaps are extracted
In order to contrast whether keypad character mistake occurs, it would be desirable to which keypad character is extracted from image.Because A very big part is unrelated background in keyboard image, and carrying out defect character recognition to us does not have any help.
(1) Morphological scale-space
A region interested is only existed in order to allow on a button, it would be desirable at morphologic method Reason.Its basic thought is to go to measure and extract the correspondingly-shaped in figure using the structural element with certain form, to reach Graphical analyses and the purpose of identification.Its elementary operation has 4：They are carried out group by expansion, burn into opening operation and closed operation Conjunction can reach and a region interested is only existed on a button.We pass through the such a processing stream of experimental design Journey：Expansion, closure, opening operation, expansion.As a result as shown in figure 4, thus obtaining area-of-interest, while ensure that same Only one of which area-of-interest on button.
(2) contours extract
By morphologic method, the size of character can be primarily determined that, but the position of character zone can't be determined. By the profile for extracting region, their minimum enclosed rectangle is asked to be assured that the position of character, minimum enclosed rectangle is surrounded Region be exactly area-of-interest.
Here we take eight adjacent methods to carry out the extraction of profile.Algorithm is as follows：For any one pixel P, its 8 pixels of surrounding are respectively P1, P2, P3, P4, P5, P6, P7, P8.On bianry image, from P points, search for around it 8 pixels whether be 1, if all 1, P points are not profiles, not all 1, then P points be profile, now from P point up times Pin sets out, and finds the pixel for 1, determines whether profile, and always recurrence is gone down, until entire image it is searched complete, so Just can be by the contours extract of target area out.
(3) character sequence
By the method for contours extract, the position in picture of each character of keyboard is obtained, as shown in Figure 5.But What the order of these positions was not to determine, such as character ' A ' not can determine which position this character is, so we The character position for extracting must be ranked up, determine the position of each character.Sort algorithm step is described as follows：
A. travel through all of character outline set, choose first character in set, calculate it minimum external square The vertical coordinate of shape, continues to travel through remaining character outline, and whether judge the vertical coordinate of their minimum enclosed rectangles has intersecting.If sat Mark scope has intersecting, then these characters are in same a line on keyboard, thus the rower of character can be determined.
B. to each character row, the center of calculating character is ranked up according to the abscissa of its center, completes Sequence from left to right.Each character is set to have a label to be determined, as a result as shown in Figure 6.
2.4 character features are extracted
Image characteristics extraction is that the result figure after the completion of image procossing is extracted into alphabetical feature, the first foundation of feature There is certain principle, be characterised by can be good at description letter.For keypad character only can not be effective using a feature The difference for finding out character, so multiple features must be extracted to achieve the goal.
(1) character boundary
In character defects detection, when defect occurs in character, such as character is long or shorter, then can pass through character Length and height are reflected.The profile of character is obtained by contours extract algorithm, the minimum enclosed rectangle of profile is obtained, then it is minimum The length and width of boundary rectangle are exactly the size of character.
(2) character center of gravity
Alphabetical center of gravity be in binary picture own ' 1 ' a statistical value, each character correspondence position gray scale Value regards its quality in the position as, it is possible to their oeverall quality and position of centre of gravity is calculated, if character is defective Words, can pass through the reacting condition of its position of centre of gravity out.Assume piece image M*N binary image, f (x, y)=0 or F (x, y)=1, if pixel number is k in region, then institute position a little and average as focus point, formula in statistics set P It is as follows：
(3) character moment characteristics
In view of keyboard production process can cause testing image and template image in the region of interest misalignment of pixels or The problem that person's scale size differs, in order to obtain character translation invariant shape, rotational invariance and constant rate, using character Central moment can be with this problem of effectively solving.For width digital picture I (x, y), the central moment of the p+q of region R is：
Although Central Moment Feature will not change after image occurs translation and change of scale, after rotation still Can change, so must be completed by combination center square.These central moment combinations are as follows：
H1=μ20+μ02 (2.7)
H2=(μ20-μ02)2+4μ11 2 (2.8)
H3=(μ30-3μ12)2+(3μ21-μ03)2 (2.9)
H4=(μ30+μ12)2+(μ21+μ03)2 (2.10)
H6=(μ20-μ02)·[(μ30+μ12)2-(μ21+μ03)2]+4μ11·(μ30+μ12)·(μ21+μ03) (2.12)
When character occurs defect, bending moment will numerically not produce difference in Hu for testing image and template image, lead to Experiment is crossed, defect character H is found1And H2Differ greatly with normal character, therefore we only use H1And H2As feature.
2.5 support vector machine classifiers are designed
Support vector machine are built upon on the VC of Statistical Learning Theory dimensions theory and Structural risk minization basis, it It is, to find a kind of efficient machine learning method of the optimal classification surface as target, to solve small sample, non-linear and higher-dimension mould Formula identification convexity shows many advantages.Therefore SVM is selected to carry out training sample.
Normal keypad character is labeled as into positive sample, defective keypad character is labeled as negative sample, and it is extracted respectively Size, center of gravity, girth, the feature such as square puts it in SVM classifier and is trained.Finally obtain one to train Model, then use it for keyboard defects detection.

Claims (6)

1. a kind of keypad character defect on-line detecting system based on computer vision, including industry camera（1）, computer （2）, Single Chip Microcomputer (SCM) system（3）, converter（4）, motor（5）And conveyer belt（6）, it is characterised in that：The industry camera（1）It is right Quasi- conveyer belt（6）On keyboard（7）Shoot, and its output is connected to computer（2）, computer（2）Connection Single Chip Microcomputer (SCM) system （3）, single-chip microcomputer（3）Output Jing converters（4）Connection motor（5）, motor（5）Drive conveyer belt（6）Operation；Taken the photograph using industry As head（1）Keypad character image is obtained, is detected image deflects using computer vision svm classifier method, judge giving birth to The quality of production of keyboard during product；Using computer（2）With single-chip microcomputer msp430 serial communications, conveyer belt is controlled（6）Speed Degree.
2. a kind of keypad character defect on-line checking detection method based on computer vision, adopts according to claim 1 Operated based on the keypad character defect on-line detecting system of computer vision, it is characterised in that concrete operation step is such as Under：
1. image gray processing, the method for medium filtering is used to improve the quality of image；
2. images match is carried out using surf characteristic points, inclined image rotation is in into same level into template picture；
3. contours extract is carried out to character using eight adjacent methods, by obtaining its profile, the minimum for calculating these character outlines is outer Rectangle is connect, the position of character is determined；
4. the coordinate of boundary rectangle is calculated, order label is carried out to character；
5. the size of character is extracted, center, moment characteristics are trained using SVM classifier, by normal keyboard character and defect word Symbol is separated.
3. the keypad character defect online detection method based on computer vision according to claim 2, it is characterised in that The step（2）Images match is carried out using surf characteristic points, its purpose is in order to by the inclination picture rotation Cheng Mo on conveyer belt The angle of plate picture, concrete grammar is as follows：
1. respectively to template image and conveyer belt photographs to keyboard picture extract its surf feature, the characteristic point for obtaining respectively It is the vector of one 64 dimension；
2. the surf characteristic points on two width images are traveled through respectively, their Euclidean distance is calculated, the minimum point of distance is found, and are exactly The good point of two width images match, using the point for matching, obtains single mapping matrix H；
3. using single mapping matrix H, the keyboard picture on conveyer belt is carried out into affine change, obtains the image for having corrected.
4. the keypad character defect online detection method based on computer vision according to claim 2, it is characterised in that The step（3）Contours extract is carried out to character using eight adjacent methods, method is as follows：A foreground pixel is hit every time, is scanned Position returns to the upper background pixel for finding the pixel, and town of then taking advantage of a situation checks 8 pixels around foreground pixel, until finding Next foreground pixel, algorithm terminates when starting point pixel is found for the second time, then the foreground pixel passed through in tracking Just constitute profile.
5. the keypad character defect online detection method based on computer vision according to claim 2, it is characterised in that The step（4）The coordinate of boundary rectangle is calculated, order label is carried out to character, it is main by all of character outline collection of traversal Close, choose first character in set, calculate it minimum enclosed rectangle vertical coordinate, continue to travel through remaining character wheel Exterior feature, whether have intersecting, if coordinate range has intersecting, these characters are in keyboard if judging the vertical coordinate of their minimum enclosed rectangles It is upper to be in same a line, thus the rower of character can be determined.
6. the keypad character defect online detection method based on computer vision according to claim 2, it is characterised in that The step（5）It is trained using SVM classifier, it is positive sample that 2000 normal picture indicias are taken respectively, and 2000 have The picture indicia of defect is negative sample, and their size is extracted respectively, and center of gravity, girth, moment characteristics, as the input of SVM, are carried out Training, using the SVM classifier for obtaining defects detection is carried out.
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CN109087286A (en) * 2018-07-17 2018-12-25 江西财经大学 A kind of detection method and application based on Computer Image Processing and pattern-recognition
CN109211136A (en) * 2018-08-31 2019-01-15 广州大学 A kind of Watch glass cover board profile tolerance detection method
CN109738450A (en) * 2019-01-09 2019-05-10 合肥联宝信息技术有限公司 The detection method and device of keyboard of notebook computer
CN109871895A (en) * 2019-02-22 2019-06-11 北京百度网讯科技有限公司 The defect inspection method and device of circuit board
CN110363250A (en) * 2019-07-23 2019-10-22 北京隆普智能科技有限公司 A kind of method and its system of 3-D image intelligent Matching
CN110596121A (en) * 2019-09-12 2019-12-20 南京旷云科技有限公司 Keyboard appearance detection method and device and electronic system
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CN108548820A (en) * 2018-03-28 2018-09-18 浙江理工大学 Cosmetics paper labels defect inspection method
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CN109871895A (en) * 2019-02-22 2019-06-11 北京百度网讯科技有限公司 The defect inspection method and device of circuit board
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CN110596121A (en) * 2019-09-12 2019-12-20 南京旷云科技有限公司 Keyboard appearance detection method and device and electronic system
CN110674876A (en) * 2019-09-25 2020-01-10 北京猎户星空科技有限公司 Character detection method and device, electronic equipment and computer readable medium
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WO2021109011A1 (en) * 2019-12-04 2021-06-10 电子科技大学 Intelligent capacitor internal defect detection method based on ultrasound image
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TWI779836B (en) * 2021-09-15 2022-10-01 英業達股份有限公司 Keyboard file verification method based on image processing
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