CN107462173B - Micromotion platform displacement measurement method and system based on micro-vision - Google Patents
Micromotion platform displacement measurement method and system based on micro-vision Download PDFInfo
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- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
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Abstract
The present invention discloses micromotion platform displacement measurement method and system based on micro-vision, step: image sequence acquisition: acquiring one group of sequence of pictures by micro- vision system；Thick matching position obtains: utilizing improved particle swarm optimization algorithm fast search in entire region of search, obtains the thick matching position of image block；Best match position obtains: centered on thick matching position, being searched in small neighbourhood using Full-search block matching algorithm, obtains best match position；Micromotion platform displacement calculates: establishing image Jacobin matrix according to micro- vision system imaging model, the corresponding displacement of best match position in image space is converted to micromotion platform actual displacement.The present invention is combined using modified particle swarm optiziation and area coverage algorithm, is reduced computing resource consumption, is realized Rapid matching and displacement measurement；For this method relative to existing microdisplacement measurement technology, measuring device is at low cost, precision is high, can be used for the fine motion system of double freedom in measuring surface.
Description
Technical field
The present invention relates to micromotion platform displacement measurement methods and system based on micro-vision.
Background technique
Modern science and technology are just rapidly to small, ultraprecise field is fast-developing.The rise of micron, nanotechnology,
The major transformation in the fields such as manufacture, information, material, biology, medical treatment and national defence is caused.Meanwhile the development of micro & nano technology is also right
More stringent requirements are proposed for ultra precise measurement technology.Since noncontact optical measurement method has measurement accuracy height, response speed
Fastly, the advantages that measurement freedom degree is more in micro-nano field to be widely used.It is concentrated currently, research both domestic and external is main
General survey amount, micro-interference measurement etc. are strangled Computer go measurement, stroboscopic micrometering, the laser more.Wherein, the micro- view of computer
Feel that measurement is using micro- vision system, by the analysis to motion vector in micro- visual pattern, to judge to transport in fine motion system
Dynamic partial dislocation situation, the system cost is lower, and it is more to can measure freedom degree.But image procossing is often adopted in micro-vision
It is displaced with common image block matching method or Image Feature Matching estimation micromotion platform, thus efficiency is lower, it is difficult to meet and surveys
The quick response requirement of amount system.
Summary of the invention
The above-mentioned prior art and there are aiming at the problem that, the object of the present invention is to provide the micromotion platforms based on micro-vision
Displacement measurement method and system may be directly applied to micromotion platform measurement, solve the technology in current Micro and nano manipulation platform measuring
Problem.
The present invention is achieved by the following technical solutions:
Micromotion platform displacement measurement method based on micro-vision, steps are as follows:
Step (1): one group of image sequence image sequence acquisition: is acquired by micro- vision system；
Step (2): thick matching position obtains: quickly being searched in entire region of search using improved particle swarm optimization algorithm
Rope obtains the thick matching position of image block；
Step (3): best match position obtains: centered on thick matching position, utilizing area coverage matching algorithm
It is searched in small neighbourhood, obtains best match position；
Step (4): micromotion platform displacement calculates: image Jacobin matrix is established according to micro- vision system imaging model, it will
The corresponding displacement of best match position is converted to micromotion platform actual displacement in image space.
In the step (1), micro- vision system, comprising: microscope, CCD camera is installed at the microscope top, described
CCD camera is connect with terminal, and the XY two degrees of freedom micromotion platform is fixed on microscope carrier, and the XY bis- is certainly
It is controlled and is driven by PZT by degree micromotion platform, marker, marker surface are posted in the XY two degrees of freedom micromotion platform upper surface
It is smooth；Axis light is incident to marker back reflection and is imaged by microscopes optical path in CCD camera target plane, and microscopes optical axis is vertical
In marker upper surface, meanwhile, microscopes optical axis is also perpendicularly to CCD camera target plane, and CCD target plane is parallel to table on marker
Face.
In the step (2), thick matching position acquisition includes:
Step (2-1): determining image ROI region, and using ROI region as the matched solution space of image block, ROI region is determined
According to the position stroke x of XY two degrees of freedom micromotion platformR, microscope magnification k, camera pixel dimension p and image block size
Size [X, Y] determines that the ROI region size of selection is minimum are as follows:
Step (2-2): select accumulative absolute error (summed absolute difference, SAD) quasi- as matching
Then, accumulative absolute error is also the objective function of particle swarm optimization algorithm, to pixel [x+u, y+v] each in each imageT,
Corresponding adaptive value fit are as follows:
Wherein, x is image slices vegetarian refreshments abscissa, and y is image slices vegetarian refreshments ordinate, and u is image block X direction amount of exercise,
V is y direction amount of exercise.
Step (2-3): particle initialization: being evenly distributed on solution space for I primary, is calculated using formula (2) each
ParticleCorresponding adaptive value；
Step (2-4): by i-th of particleAdaptive value [fit is obtained in n times iterative processi,1,
fiti,2,...fiti,n] locally optimal solution pbest [i] of the smallest adaptive value in the inside as each particle:
Pbest [i]=min { fiti,1,fiti,2,...fiti,n}；
Select all particles in n times iterative process the smallest particle of adaptive value as globally optimal solution gbest [n], it may be assumed that
Gbest [n]=min { pbest1,pbest2,...pbestI}；
Each particle updates the position and speed of oneself with formula (3):
In formula, n indicates nth iteration, C1,C2For Studying factors, it is set as 2, R1,R2For random number, R1,R2∈[0,1]。
Indicate speed of i-th of particle in (n+1)th iterative process,
Indicate position of i-th of particle in (n+1)th iterative process,
Indicate speed of i-th of particle during nth iteration,
Indicate position of i-th of particle during nth iteration；
Step (2-5): after calculating nth iteration, the distance between the particle of particle i and current globally optimal solution
rgbest,i:
Calculate particle i and entire population by position minimum distance rnearest,i:
Wherein, p ∈ [1,2 ... I], q ∈ [1,2 ... n-1]；
After each particle updates oneself position, particle is according to the adaptive value fit for setting Policy Updates oneselfi,n, the particle
According to the adaptive value fit of setting Policy Updates oneselfi,nIt is as follows:
A. if rgbest,i< r0, r in formula0For given threshold, then new particle i updates its adaptive value according to formula (2)；
B. if rgbest,i> r0And rnearest,i> r0, then new particle i updates its adaptive value according to formula (2)；
C. if rgbest,i> r0And rnearest,i< r0, then nearest by position apart from entire population with new particle i
The adaptive value of point replaces the adaptive value of new particle, fiti,n=SADnearest；
Step (2-6): step (2-4)-(2-5) is repeated, until meeting maximum number of iterations, and calculated global optimum
Corresponding coordinate is solved as thick matching position
In the step (3), for thick matching positionCentered on, select a small region of search, the small region of search
Size be set as 7 × 7 pixels, with Full-search block matching algorithm search in small region of search, calculate the small field of search
The adaptive value of 49 pixels in domain selects wherein adaptive value is the smallest to put as final matching results
In the step (4), according to step (1), CCD target plane is parallel to marker upper surface, and mould will be imaged
Type is reduced to pin-hole model；The Jacobin matrix of image space and micromotion platform is as follows:
X in formulaimg, yimgFor image space coordinate, x0, y0For micromotion platform position coordinates,It is a parameter
For the Jacobin matrix of constant.
According to final matching results required in step (3)Calculate position corresponding to micromotion platform:
Micromotion platform displacement measurement system based on micro-vision, comprising: memory, processor and storage are on a memory
And the computer instruction run on a processor, the computer instruction complete following steps when running on a processor:
Step (1): one group of image sequence image sequence acquisition: is acquired by micro- vision system；
Step (2): thick matching position obtains: quickly being searched in entire region of search using improved particle swarm optimization algorithm
Rope obtains the thick matching position of image block；
Step (3): best match position obtains: centered on thick matching position, utilizing area coverage matching algorithm
It is searched in small neighbourhood, obtains best match position；
Step (4): micromotion platform displacement calculates: image Jacobin matrix is established according to micro- vision system imaging model, it will
The corresponding displacement of best match position is converted to micromotion platform actual displacement in image space.
A kind of computer readable storage medium is stored thereon with computer instruction, and the computer instruction is on a processor
Following steps are completed when operation:
Step (1): one group of image sequence image sequence acquisition: is acquired by micro- vision system；
Step (2): thick matching position obtains: quickly being searched in entire region of search using improved particle swarm optimization algorithm
Rope obtains the thick matching position of image block；
Step (3): best match position obtains: centered on thick matching position, utilizing area coverage matching algorithm
It is searched in small neighbourhood, obtains best match position；
Step (4): micromotion platform displacement calculates: image Jacobin matrix is established according to micro- vision system imaging model, it will
The corresponding displacement of best match position is converted to micromotion platform actual displacement in image space.
The beneficial effects of the present invention are:
The present invention is combined using modified particle swarm optiziation and area coverage algorithm, reduces computing resource consumption,
Realize Rapid matching and displacement measurement；Meanwhile this method has measuring device cost relative to existing microdisplacement measurement technology
It is low, precision is high, can be used for the features such as fine motion system of double freedom in measuring surface (X-Y).
Detailed description of the invention
The accompanying drawings constituting a part of this application is used to provide further understanding of the present application, and the application's shows
Meaning property embodiment and its explanation are not constituted an undue limitation on the present application for explaining the application.
Fig. 1 is hardware annexation figure of the invention；
Fig. 2 is the schematic diagram for improving particle in particle swarm algorithm and updating adaptive value rule；
Fig. 3 be withCentered on, select a small region of search, and the signal accurately solved with Full-search block matching algorithm search
Figure；
Fig. 4 is that imaging model is reduced to pin-hole model schematic diagram；
Fig. 5 is flow chart of the method for the present invention.
Specific embodiment
It is noted that following detailed description is all illustrative, it is intended to provide further instruction to the application.Unless another
It indicates, all technical and scientific terms used herein has usual with the application person of an ordinary skill in the technical field
The identical meanings of understanding.
It should be noted that term used herein above is merely to describe specific embodiment, and be not intended to restricted root
According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singular
Also it is intended to include plural form, additionally, it should be understood that, when in the present specification using term "comprising" and/or " packet
Include " when, indicate existing characteristics, step, operation, device, component and/or their combination.
As shown in figure 5, the micromotion platform displacement measurement method based on micro-vision, steps are as follows:
Step (1): image sequence acquisition: by being made of marker characteristic point, micromotion platform, Stereo microscope, CCD camera
Micro- vision system acquire one group of sequence of pictures；
Step (2): thick matching position obtains: quickly being searched in entire region of search using improved particle swarm optimization algorithm
Rope obtains the thick matching position of image block；As shown in Figure 2；
Step (3): best match position obtains: centered on thick matching position, being existed using Full-search block matching algorithm
Search in small neighbourhood, obtains best match position；
Step (4): micromotion platform displacement calculates: image Jacobin matrix is established according to micro- vision system imaging model, it will
The corresponding displacement of best match position is converted to micromotion platform actual displacement in image space.
In the step (1), the principle of image sequence acquisition are as follows: Image Acquisition principle is as shown in Figure 1, PZT drives XY bis-
Degree-of-freedom micro platform execution part, wherein marker is posted on surface, and marker surface is smooth.After the labeled object reflection of axis light
It is imaged by microscopes optical path in CCD target plane.Wherein, microscopes optical axis is perpendicular to marker upper surface, while microscope light
Axis is also perpendicularly to camera target plane.
In the step (2), thick matching position acquisition includes:
Step (2-1) determines image ROI region, using entire ROI region as the matched solution space of image block, entire ROI
Region determination can be according to micromotion platform position stroke xRWith microscope magnification k, camera pixel dimension p and tile size
[X, Y] is determined, the ROI region of selection is minimum are as follows:
Step (2-2) selects accumulative absolute error (summed absolute difference, SAD) quasi- as matching
It then, is also the objective function of particle swarm optimization algorithm, to pixel [x+u, y+v] each in each imageT, corresponding suitable
It should be worth are as follows:
The initialization of step (2-3) particle: I primary is evenly distributed on entire solution space, each particleUtilize its corresponding adaptive value of (2) formula meter.
Step (2-4) i-th of particleThe adaptive value [fit in n times iterative processi,1,
fiti,2,...fiti,n] locally optimal solution of the smallest value in the inside as each particle, i.e. pbest [i]=min
{fiti,1,fiti,2,...fiti,n}.Select all particles in n times iterative process the smallest particle of adaptive value as it is global most
Excellent solution, i.e.,
Gbest [n]=min { pbest1,pbest2,...pbestI}。
Each particle following formula updates the position and speed of oneself:
N indicates the n-th iteration, C in formula1,C2For Studying factors, it is set as 2, R1,R2For random number, R1,R2∈[0,1]。
After step (2-5) calculates nth iteration, the distance between the particle of particle i and current globally optimal solutionCalculate particle i and entire population by position minimum distance, i.e.,Wherein p ∈ [1,2 ... I], q ∈ [1,2 ... n-1].Each particle updates oneself position
It postpones, particle is according to rule as described below with oneself new adaptive value fiti,n, it is specific as follows:
a.rgbest,i< r0, r in formula0For threshold value, new particle i updates its adaptive value according to formula (2)；
b.rgbest,i> r0And rnearest,i> r0, new particle i updates its adaptive value according to formula (2)；
c.rgbest,i> r0And rnearest,i< r0, with new particle i apart from entire population by the suitable of position closest approach
The adaptive value instead of new particle, i.e. fit should be worthi,n=SADnearest。
Wherein, r0For the threshold value of setting, can be determined according to search window, number of particles by empirical equation.
Step (2-6) answers step (2-4), (2-5), until meeting maximum number of iterations, and it is in the above way calculated
The corresponding coordinate of globally optimal solution is as thick matching position
In the step (3), with forCenter selects a small region of search, is sized to 7 × 7pixel, such as Fig. 3
It is shown.With Full-search block matching algorithm search in whole region, the adaptive value of 49 pixels in the region is calculated, is selected
Wherein the smallest point of adaptive value is used as final matching results
In the step (4), according to step (1), CCD target plane is parallel to marker upper surface, thus can incite somebody to action
Imaging model is reduced to pin-hole model, specific as shown in Figure 4.According to diagram geometrical relationship, image space and micro- can be derived
The Jacobin matrix of moving platform is as follows:
X in formulaimg, yimgFor image space coordinate, x0, y0For micromotion platform position coordinates,It is that a parameter is
The Jacobin matrix of constant.Required by step (3), position corresponding to micromotion platform can be released:
The foregoing is merely preferred embodiment of the present application, are not intended to limit this application, for the skill of this field
For art personnel, various changes and changes are possible in this application.Within the spirit and principles of this application, made any to repair
Change, equivalent replacement, improvement etc., should be included within the scope of protection of this application.
Claims (8)
1. the micromotion platform displacement measurement method based on micro-vision, characterized in that steps are as follows:
Step (1): one group of image sequence image sequence acquisition: is acquired by micro- vision system；
Step (2): thick matching position obtains: utilizing improved particle swarm optimization algorithm fast search in entire region of search, obtains
Obtain the thick matching position of image block；
Step (3): best match position obtains: again centered on thick matching position, using area coverage matching algorithm small
Search in neighborhood, obtains best match position；
Step (4): micromotion platform displacement calculates: image Jacobin matrix is established according to micro- vision system imaging model, by image
The corresponding displacement of best match position is converted to micromotion platform actual displacement in space；
In the step (2), thick matching position acquisition includes:
Step (2-1): determining image ROI region, and using ROI region as the matched solution space of image block, ROI region determines basis
The position stroke x of XY two degrees of freedom micromotion platformR, microscope magnification k, camera pixel dimension p and image block size size
[X, Y] is determined, the ROI region size of selection is minimum are as follows:
Step (2-2): select accumulative absolute error as matching criterior, accumulative absolute error is also the mesh of particle swarm optimization algorithm
Scalar functions, to pixel [x+u, y+v] each in each imageT, corresponding adaptive value fit are as follows:
Wherein, x is image slices vegetarian refreshments abscissa, and y is image slices vegetarian refreshments ordinate, and u is image block X direction amount of exercise, and v is
Y direction amount of exercise；fj(x, y) represents the position corresponding to each pixel in j moment each image；
Step (2-3): particle initialization: being evenly distributed on solution space for I primary, calculates each particle using formula (2)Corresponding adaptive value；
Step (2-4): by i-th of particleAdaptive value [fit is obtained in n times iterative processi,1,fiti,2,
...fiti,n] locally optimal solution pbest [i] of the smallest adaptive value in the inside as each particle:
Pbest [i]=min { fiti,1,fiti,2,...fiti,n}；
Select all particles in n times iterative process the smallest particle of adaptive value as globally optimal solution gbest [n], it may be assumed that
Gbest [n]=min { pbest1,pbest2,...pbestI}；
Each particle updates the position and speed of oneself with formula (3):
In formula, n indicates nth iteration, C1,C2For Studying factors, it is set as 2, R1,R2For random number, R1,R2∈[0,1]；
Indicate speed of i-th of particle in (n+1)th iterative process,
Indicate position of i-th of particle in (n+1)th iterative process,
Indicate speed of i-th of particle during nth iteration,
Indicate position of i-th of particle during nth iteration；
Step (2-5): after calculating nth iteration, the distance between particle of particle i and current globally optimal solution rgbest,i:
Calculate particle i and entire population by position minimum distance rnearest,i:
Wherein, p ∈ [1,2 ... I], q ∈ [1,2 ... n-1]；
After each particle updates oneself position, particle is according to the adaptive value fit for setting Policy Updates oneselfi,n；
Step (2-6): step (2-4)-(2-5) is repeated, until meeting maximum number of iterations, and calculated globally optimal solution pair
The coordinate answered is as thick matching position
The particle is according to the adaptive value fit for setting Policy Updates oneselfi,nIt is as follows:
A. if rgbest,i<r0, r in formula0For given threshold, then new particle i updates its adaptive value according to formula (2)；
B. if rgbest,i>r0And rnearest,i>r0, then new particle i updates its adaptive value according to formula (2)；
C. if rgbest,i>r0And rnearest,i<r0, then with new particle i apart from entire population institute by position closest approach fitting
The adaptive value instead of new particle, fit should be worthi,n=SADnearest。
2. the micromotion platform displacement measurement method based on micro-vision as described in claim 1, characterized in that the step
(1) in, micro- vision system, comprising: CCD camera is installed at microscope, the microscope top, and the CCD camera and computer are whole
End connection, XY two degrees of freedom micromotion platform are fixed on microscope carrier, and the XY two degrees of freedom micromotion platform is controlled by PZT
Marker is posted in device drive control processed, the XY two degrees of freedom micromotion platform upper surface, and marker surface is smooth；Axis light is incident
Be imaged to marker back reflection by microscopes optical path in CCD camera target plane, microscopes optical axis perpendicular to marker upper surface,
Meanwhile microscopes optical axis is also perpendicularly to CCD camera target plane, CCD target plane is parallel to marker upper surface.
3. the micromotion platform displacement measurement method based on micro-vision as described in claim 1, characterized in that the step
(3) in, with thick matching positionCentered on, a small region of search is selected, the size of the small region of search is set as 7
× 7 pixels calculate in small region of search 49 pixels with the search of area coverage matching algorithm in small region of search
Adaptive value selects wherein adaptive value is the smallest to put as final matching results
4. the micromotion platform displacement measurement method based on micro-vision as claimed in claim 3, characterized in that the step
(4) in, according to step (1), CCD target plane is parallel to marker upper surface, and imaging model is reduced to pin-hole model；
The Jacobin matrix of image space and micromotion platform is as follows:
X in formulaimg, yimgFor image space coordinate, x0, y0For micromotion platform position coordinates,Be a parameter be constant
Jacobin matrix.
5. the micromotion platform displacement measurement method based on micro-vision as claimed in claim 4, characterized in that
According to final matching results required in step (3)Calculate position corresponding to micromotion platform:
6. the micromotion platform displacement measurement system based on micro-vision, characterized in that include: memory, processor and be stored in
The computer instruction run on memory and on a processor completes such as right when the computer instruction is run on a processor
It is required that the step of micromotion platform displacement measurement method described in 1 based on micro-vision:
Step (1): one group of image sequence image sequence acquisition: is acquired by micro- vision system；
Step (2): thick matching position obtains: utilizing improved particle swarm optimization algorithm fast search in entire region of search, obtains
Obtain the thick matching position of image block；
Step (3): best match position obtains: again centered on thick matching position, using area coverage matching algorithm small
Search in neighborhood, obtains best match position；
Step (4): micromotion platform displacement calculates: image Jacobin matrix is established according to micro- vision system imaging model, by image
The corresponding displacement of best match position is converted to micromotion platform actual displacement in space.
7. the micromotion platform displacement measurement system based on micro-vision as claimed in claim 6, characterized in that the step
(1) in, micro- vision system, comprising: CCD camera is installed at microscope, the microscope top, and the CCD camera and computer are whole
End connection, XY two degrees of freedom micromotion platform are fixed on microscope carrier, and the XY two degrees of freedom micromotion platform is driven by PZT
Marker is posted in dynamic control, the XY two degrees of freedom micromotion platform upper surface, and marker surface is smooth；Axis light is incident to label
Object back reflection by microscopes optical path CCD camera target plane be imaged, microscopes optical axis perpendicular to marker upper surface, meanwhile,
Microscopes optical axis is also perpendicularly to CCD camera target plane, and CCD target plane is parallel to marker upper surface.
8. a kind of computer readable storage medium, is stored thereon with computer instruction, characterized in that the computer instruction is being located
Manage the step of micromotion platform displacement measurement method based on micro-vision as described in claim 1 is completed when running on device:
Step (1): one group of image sequence image sequence acquisition: is acquired by micro- vision system；
Step (2): thick matching position obtains: utilizing improved particle swarm optimization algorithm fast search in entire region of search, obtains
Obtain the thick matching position of image block；
Step (3): best match position obtains: again centered on thick matching position, using area coverage matching algorithm small
Search in neighborhood, obtains best match position；
Step (4): micromotion platform displacement calculates: image Jacobin matrix is established according to micro- vision system imaging model, by image
The corresponding displacement of best match position is converted to micromotion platform actual displacement in space.
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| CN108534683B (en) * | 2018-03-06 | 2019-08-27 | 山东大学 | The micro-nano platform movement measurement system and method for view-based access control model image procossing |
| CN109596053B (en) * | 2019-01-14 | 2019-10-01 | 中山大学 | A method of measurement high-speed rail bridge vertically moves degree of disturbing |
| CN114394843B (en) * | 2021-12-03 | 2022-09-20 | 广东中旗新材料股份有限公司 | Porous ceramic prepared from quartz stone waste and preparation method thereof |
| CN115063451B (en) * | 2022-06-10 | 2024-06-04 | 华南理工大学 | High-speed micro-vision tracking method and system for planar three-degree-of-freedom pose measurement |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1546943A (en) * | 2003-11-28 | 2004-11-17 | 天津大学 | Micro-electromechanical system testing device and method based on micro-interference technique |
| CN1654923A (en) * | 2005-02-28 | 2005-08-17 | 天津大学 | System and method for testing three-dimensional motion of microstructure by image matching and phase shift interference |
| KR20100126941A (en) * | 2009-05-25 | 2010-12-03 | 주식회사 쓰리비 시스템 | Micro vision inspection apparatus for surface of printed circuit board |
| CN102506710A (en) * | 2011-10-25 | 2012-06-20 | 天津大学 | Device for detecting in-plane error in micro/nano device out-of-plane motion test and compensating method |
| CN105180806A (en) * | 2015-08-25 | 2015-12-23 | 大连理工大学 | Trans-scale geometrical parameter measurement method based on microscopic visual sense |
| CN106018415A (en) * | 2016-05-20 | 2016-10-12 | 哈尔滨理工大学 | System for detecting quality of small parts based on micro-vision |
-
2017
- 2017-09-25 CN CN201710874305.9A patent/CN107462173B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1546943A (en) * | 2003-11-28 | 2004-11-17 | 天津大学 | Micro-electromechanical system testing device and method based on micro-interference technique |
| CN1654923A (en) * | 2005-02-28 | 2005-08-17 | 天津大学 | System and method for testing three-dimensional motion of microstructure by image matching and phase shift interference |
| KR20100126941A (en) * | 2009-05-25 | 2010-12-03 | 주식회사 쓰리비 시스템 | Micro vision inspection apparatus for surface of printed circuit board |
| CN102506710A (en) * | 2011-10-25 | 2012-06-20 | 天津大学 | Device for detecting in-plane error in micro/nano device out-of-plane motion test and compensating method |
| CN105180806A (en) * | 2015-08-25 | 2015-12-23 | 大连理工大学 | Trans-scale geometrical parameter measurement method based on microscopic visual sense |
| CN106018415A (en) * | 2016-05-20 | 2016-10-12 | 哈尔滨理工大学 | System for detecting quality of small parts based on micro-vision |
Non-Patent Citations (2)
| Title |
|---|
| Displacement measurement system for inverters using computer micro-vision;Heng Wu, et al;《Optics and Lasers in Engineering》;20160212;正文第2-3节及图1-图3 |
| 基于多种群粒子群算法和布谷鸟搜索的联合寻优算法;高云龙等;《控制与决策》;20160430;第31卷(第4期);正文第1-3节 |
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