CN102252611B - Geometric positioning method - Google Patents
Geometric positioning method Download PDFInfo
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- CN102252611B CN102252611B CN201110118014XA CN201110118014A CN102252611B CN 102252611 B CN102252611 B CN 102252611B CN 201110118014X A CN201110118014X A CN 201110118014XA CN 201110118014 A CN201110118014 A CN 201110118014A CN 102252611 B CN102252611 B CN 102252611B
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Abstract
The invention discloses a method for accurately positioning an object at high speed based on optical geometrical characteristics of an image. The method comprises the following steps of: collecting a sample, performing edge extraction on the shape of a standard sample by a sobel operator, establishing a mathematical model by using seven invariants of Hu and describing the sample, collecting the pattern of the sample to be tested by an industrial camera, comparing the standard sample and the sample to be tested, and calculating and positioning. The algorithm is to compare the contours of images and is not related to a gray value of a pixel, so that the method is not influenced by the change of radiance; and the seven invariants of Hu can calculate object scaling and an angle, so that the seven invariants are not influenced by scaling and the change of angle. During calculation, a pyramid algorithm and an multi media extension (MMX) acceleration algorithm are used, so that the algorithms are high in computation speed, and low in error and are suitable for positioning of automatic production equipment, such as surface mount technologies (SMT), light-emitting diodes (LED), printed circuit boards (PCB), solar plate diving machines and the like.
Description
Technical field
The present invention relates to the automated production equipment of high-accuracy processing and manufacturing industry, relate in particular to the location of the automated production equipments such as SMT, LED, PCB, sun power board separator.
Background technology
In the modern production manufacturing industry, manufacturing electronics industry is a ring important in industrial chain, along with electronic product technology development, electronic product is constantly compact and lightening, and function is more and more, from strength to strength, technically, smaller volume, chip integration is more and more higher on the one hand with aspect, to be structural design, the density of chip significantly improves, this accuracy requirement to automated production equipment just starts to improve, simultaneously, along with electronic product, LCD, the price of LED product is more and more lower, production efficiency becomes the height that determines the enterprise getting profit ability, this speed to automated production equipment and degree of reliability require also very high, and for the instrument of chip mounter and board separator and so on, most important is exactly the accuracy of location.
Method for location on the industrial automation production equipment is a lot, and modal have a mechanical clamp location, and photoelectric sensor location, pressure transducer location, optical imagery location comprise the location of intensity-based and based on the location of how much.In early days because computer speed is slow, data-handling capacity a little less than, the localization method generally adopted is mechanical clamp location and sensor localization.The main shortcoming of these two kinds of localization methods is that positioning precision is low, and speed is slow, and partly also needs to rely on manually-operated, can only be semi-automatic.Later enhancing along with computer process ability in 2000, and image algorithm is constantly perfect, increasing industrial automation equipment has adopted the method for optical imagery location, especially abroad on high-end automation equipment, for example, the semiconductor crystal wafer production equipment, the PCB production equipment, the SMT production equipment, LCD panel manufacturing equipment, solar panels production equipment etc.Differentiation on this automatic technology is consistent with mankind's obtaining information, the information of the mankind more than 90% obtains by eyes, and the information obtained by sense of touch is less than 1%, and the collection of optical imagery is like being machine with " eyes " obtaining information, location algorithm is exactly that machine is in the process with " brain " thinking.And the optical imagery localization method is divided into gray scale location and geometry location, the location algorithm of intensity-based is by the size of the correlativity of the gray-scale value of the gray-scale value of analyzing template image and object under test image, to come the position of judgment object.Because the gray-scale value close relation of this algorithm and each picture element of image, so it is very responsive for the brightness variation of illumination.And the absorption of workpiece to light in the middle of actual production, reflection can not be in full accord, therefore can affect accuracy rate and the percent of pass of location; The gray scale algorithm is by autocorrelation function, to carry out the similarity of calculation template and object under test in addition, so if object under test has dimensionally certain convergent-divergent or certain angular deflection is arranged, coefficient of autocorrelation can sharply reduce.And in practical application in industry, the size of workpiece can not be in full accord, the angle of putting also can not be always zero, and this also can affect precision and the percent of pass of location.
Summary of the invention
The present invention is directed to that above problem has proposed especially a kind ofly to be applicable to automation equipment, overcomes object under test is difficult to the convergent-divergent of avoiding or certain angle on object under test deflection, thereby the location that solves intensity-based is on the very responsive a kind of geometry location method that affects the problem of precision of light.This method can reach suitable device for location technology high speed, efficient, high-precision requirement.
In order to realize above effect, the technical scheme that the present invention takes is: whole flow processs of described geometry location method, and comprise and set localization criteria, collection testing data, contrast, calculating link, it is characterized in that, concrete disposal route comprises the following steps,
(1), with industrial digital camera, gather the image of master sample;
(2), do edge extracting by the shape of sobel operator extraction master sample, this shape can be arbitrarily;
(3), with seven invariants of Hu, the shape of sample, angle and locus are described out by following mathematical model:
I(A,B)=sum
i=1..7abs(1/m
A i?-?1/m
B i)，
M wherein
A i=sign (h
A i) log (h
A i);
m
B i=sign(h
B i)·log(h
B i)；
H
A i, h
B iThe Hu square of-A and B; This model can be used as the geometric properties of sample;
(4), the cross correlation of the geometric properties of all samples is extracted, as template;
(5), with industrial digital camera, gather the image of testing sample;
(6), with seven invariants of Hu, the shape of sample to be tested, angle and locus are described out by aforementioned mathematical model;
(7), the template of master sample is compared with the geometric properties of the template of sample to be tested, and by pyramid algorith and MMX software acceleration algorithm, draw volume coordinate, the anglec of rotation and the scaling of the geometric properties that similarity is the highest, the result of gained is exactly the required result in location
Described pyramid algorith refers to 1980, Peter and the hand-held proposition laplacian pyramid of Ted Adelson algorithm, the compression performance that this algorithm is resolved thought and sale due to many resolutions of its advanced person is widely used in image compression encoding, described MMX software acceleration algorithm is actual is a multimedia extension to the Pentium instruction set, its core concept is to adapt to the low precision of multimedia era, the data flow characteristics of big data quantity, utilize fully the data path of Pentium 64 bit widths, a plurality of data of parallel processing within an instruction cycle, draw the volume coordinate of the geometric properties that similarity is the highest, the anglec of rotation and scaling, the result of gained is exactly the required result in location.
Described sobel operator is the mutual relationship of geometric properties, or common feature on master sample and sample to be tested, such as mark or the distance between center of gravity and the angle of geometric properties, location.
Beneficial effect of the present invention: described sobel operator itself is exactly one of operator during image is processed, and mainly as rim detection, technically, it is a discreteness difference operator, is used for the approximate value of gradient of arithmograph image brightness function.In any point of image, use this operator, will produce corresponding gradient vector or its method vector, by the edge extracting of masterplate image out, then edge image is converted into to geometric profile image set (Contour), and according to the HU invariant, the profile collection is rearranged by the weight size, by mathematical model, template is described out, finally by pyramid algorith, template and image outline are carried out to correlativity scanning and compare, and rearrange by the zone in large young pathbreaker's image of correlativity score.The position of score maximum is exactly the result of location.Because this algorithm is that the profile of image is compared, thus with it doesn't matter its impact that not changed by luminance brightness of the gray-scale value of pixel; So seven invariants of Hu can calculate the impact that it is not changed by scaling and angle to object convergent-divergent and angle.
The accompanying drawing explanation
Fig. 1 is the process flow diagram of geometry location method of the present invention.
Embodiment
In order to make purpose of the present invention, technical scheme and advantage clearer, below in conjunction with drawings and Examples, the present invention is further elaborated.
Whole flow processs of described geometry location method comprise and set localization criteria, collection testing data, contrast, calculating link that it is characterized in that, concrete disposal route comprises the following steps,
(1), with industrial digital camera, gather the image of master sample;
(2), do edge extracting by the shape of sobel operator extraction master sample, this shape can be arbitrarily;
(3), with seven invariants of Hu, the shape of sample, angle and locus are described out by following mathematical model:
I(A,B)=sum
i=1..7abs(1/m
A i?-?1/m
B i)，
M wherein
A i=sign (h
A i) log (h
A i);
m
B i=sign(h
B i)·log(h
B i)；
H
A i, h
B iThe Hu square of-A and B; This model can be used as the geometric properties of sample;
(4), the cross correlation of the geometric properties of all samples is extracted, as template;
(5), with industrial digital camera, gather the image of testing sample;
(6), with seven invariants of Hu, the shape of sample to be tested, angle and locus are described out by aforementioned mathematical model;
(7), the template of master sample is compared with the geometric properties of the template of sample to be tested, and by pyramid algorith and MMX software acceleration algorithm, draw volume coordinate, the anglec of rotation and the scaling of the geometric properties that similarity is the highest, the result of gained is exactly the required result in location
Described sobel operator is the mutual relationship of geometric properties, or common feature on master sample and sample to be tested, such as mark or the distance between center of gravity and the angle of geometric properties, location.
As the flow process in Fig. 1, with industrial digital camera, take the image of master sample, basis as later contrast, utilize the sobel operator master sample to be extracted to the edge of master sample, and calculate the Hu invariant of master sample as template, then for determinand, also carry out industrial digital and take the image that head is taken determinand, utilize equally the sobel operator to extract the determinand edge, for seven invariants of determinand edge calculations determinand Hu that extract as template, finally compare link, that the Hu invariant data that obtain under two group models are contrasted, draw in testing image in the highest Geometry edge coordinate of template matches degree.
In concrete practical operation, we for the cruciform MARK point on a pcb board as master sample, at first selected object as master sample is taken, extract on it criss-cross geometric properties as template, with the model that the Hu invariant is set up, it is calculated, in next the process of feature is extracted in the shooting of several objects under test, take following scheme: object under test one: change the brightness to its illumination; Object under test two: rotation pcb board; Object under test three: add impurity on pcb board; Then gather respectively the pcb board image of above-mentioned situation as sample to be tested, utilize above-mentioned algorithm to calculate the locus that cruciform mark is ordered, the anglec of rotation and scaling, by the coordinate contrast that the result calculated and actual cruciform mark are ordered, error is in a pixel.
Experimental results show that the repetitive positioning accuracy of this algorithm is in 1 pixel.Because used pyramid and MMX accelerating algorithm, the non-product of the computing velocity of this algorithm is fast.Use the computing machine of 2.8GHz dominant frequency, between 10ms～15ms, for the non-constant of picture quality, also can be controlled in 30ms its positioning time its positioning time, and arranging the impact of its speed of template size is very micro-.
In this technology is incorporated into to board separator able to programme, described board separator is the optical, mechanical and electronic integration automatic equipment, it is defined in CONFIG.SYS by the cutting route of the pcb board that the imageing sensor be arranged on three-dimensional platform will cut, and the guiding milling cutter cuts accurately to plate.Be the automation equipment of cutting yoke plate PCB on the SMT production line, be mainly used in cutting apart and boring of circuit board, the mobile phone of high integration especially, the cutting apart of the yoke plates such as MP3, it has replaced people's work point plate, improves the quality of products, and reduces scrappage.This equipment is mainly used on SMT electronics paster production line.
Claims (2)
1. geometry location method, its whole flow processs comprise to be set localization criteria, gathers testing data, contrast, calculating link, it is characterized in that, concrete disposal route comprises the following steps,
(1), with industrial digital camera, gather the image of master sample;
(2), do edge extracting by the shape of sobel operator extraction master sample, this shape can be arbitrarily;
(3), with seven invariants of Hu, the shape of sample, angle and locus are described out by following mathematical model:
I(A,B)=sum
i=1..7abs(1/m
A i?-?1/m
B i)，
M wherein
A i=sign (h
A i) log (h
A i);
m
B i=sign(h
B i)·log(h
B i)；
H
A i, h
B iThe Hu square of-A and B; This model can be used as the geometric properties of sample;
(4), the cross correlation of the geometric properties of all samples is extracted, as template;
(5), with industrial digital camera, gather the image of testing sample;
(6), with seven invariants of Hu, the shape of sample to be tested, angle and locus are described out by aforementioned mathematical model;
(7), the template of master sample is compared with the geometric properties of the template of sample to be tested, and by pyramid algorith and MMX software acceleration algorithm, draw volume coordinate, the anglec of rotation and the scaling of the geometric properties that similarity is the highest, the result of gained is exactly the required result in location.
2. geometry location method according to claim 1, it is characterized in that, described sobel operator is the mutual relationship of geometric properties, or common feature on master sample and sample to be tested, distance and angle between the geometric properties of sample to be tested, the mark of location or center of gravity.
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Families Citing this family (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103217431A (en) * | 2012-01-19 | 2013-07-24 | 昆山思拓机器有限公司 | Image detection method of SMT screen plate |
| CN102607421A (en) * | 2012-04-12 | 2012-07-25 | 苏州天准精密技术有限公司 | Large-view-field image measuring method and equipment |
| CN105744824A (en) * | 2014-12-10 | 2016-07-06 | 安徽海创自动控制设备有限公司 | Automatic paster mounting device |
| WO2016157290A1 (en) * | 2015-03-27 | 2016-10-06 | 三菱電機株式会社 | Detector |
| CN108458655A (en) * | 2017-02-22 | 2018-08-28 | 上海理工大学 | Support the data configurableization monitoring system and method for vision measurement |
| CN107578431A (en) * | 2017-07-31 | 2018-01-12 | 深圳市海思科自动化技术有限公司 | A kind of Mark points visual identity method |
| CN107944494B (en) * | 2017-11-30 | 2020-09-18 | 华中科技大学 | Mark point visual identification and positioning method based on invariant moment |
| CN109631803A (en) * | 2019-02-01 | 2019-04-16 | 东莞中科蓝海智能视觉科技有限公司 | Part angle detects adjusting method |
| CN109631763A (en) * | 2019-02-01 | 2019-04-16 | 东莞中科蓝海智能视觉科技有限公司 | Irregular part detects localization method |
| CN109919199A (en) * | 2019-02-13 | 2019-06-21 | 东南大学 | The detection method of Wind turbines abnormal data based on image procossing |
| CN112579810B (en) * | 2019-09-30 | 2023-10-27 | 深圳市嘉立创科技发展有限公司 | Printed circuit board classification method, device, computer equipment and storage medium |
| CN110826627A (en) * | 2019-11-06 | 2020-02-21 | 广东三维家信息科技有限公司 | Image similarity measuring method and device and electronic equipment |
| CN111430289B (en) * | 2020-05-07 | 2023-04-18 | 上海果纳半导体技术有限公司 | Wafer positioning and calibrating device and wafer positioning and calibrating method |
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| US6603563B1 (en) * | 2000-04-05 | 2003-08-05 | Accu-Sort Systems, Inc. | Apparatus for determining measurements of an object utilizing negative imaging |
| EP1519144A1 (en) * | 2003-09-29 | 2005-03-30 | Nederlandse Organisatie voor toegepast-natuurwetenschappelijk onderzoek TNO | Free-form optical surface measuring apparatus and method |
| CN100504288C (en) * | 2007-10-15 | 2009-06-24 | 陕西科技大学 | Article geometrical size measuring device and method based on multi-source image fusion |
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