CN111768445A - Micro-operation platform error self-correction algorithm based on machine vision - Google Patents
Micro-operation platform error self-correction algorithm based on machine vision Download PDFInfo
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- CN111768445A CN111768445A CN202010387435.1A CN202010387435A CN111768445A CN 111768445 A CN111768445 A CN 111768445A CN 202010387435 A CN202010387435 A CN 202010387435A CN 111768445 A CN111768445 A CN 111768445A
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- G06—COMPUTING OR CALCULATING; COUNTING
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- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
- G06T7/74—Determining position or orientation of objects or cameras using feature-based methods involving reference images or patches
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Abstract
The invention discloses a microscopic operation platform error self-correction algorithm based on machine vision, which comprises the following steps: placing the scale on a micro-operation platform, respectively moving the micro-operation platform along a fixed direction in a fixed step manner to obtain scale images, and ensuring that the front image and the rear image are partially overlapped; splicing the front image and the rear image in the direction; calculating the systematic error of the micro-operation platform in the direction; and autonomously compensating the system error in the direction, and correcting the system error in the direction. The invention integrates and unifies the mechanical error, CCD installation error, pixel/micron conversion and other errors which affect the microinjection precision at present based on the image splicing technology, does not need manual assistance, can realize autonomous compensation and correction, and can control the errors at the pixel level. In addition, the algorithm provided by the invention is not only suitable for a micro-operation system, but also suitable for error correction of other mobile platforms, is simple to operate, and has the characteristics of high efficiency, high precision and the like.
Description
Technical Field
The invention relates to the technical field of limiting operation calculation, in particular to an error autonomous correction algorithm for a micro-operation platform based on machine vision.
Background
Micromanipulation techniques are important technical means of modern bioengineering in the field of bioscience. With the development of modern medical technology, microinjection technology is widely applied to cell injection, cell segmentation, assisted reproduction and the like.
No matter cell injection or cell segmentation is carried out, when microscopic operation is carried out, a target cell needs to be found and accurately positioned in a microscope visual field quickly, and then the subsequent operation is carried out. At present, the precision of micro-operation mainly relies on mechanical precision, but because mechanical equipment can't avoid can have certain self error and installation error, all can reduce the precision of micro-operation, directly influence the result of micro-operation even, it is thus visible, correct the precision to micro-operation to the compensation of system's error and play an important role.
For the compensation of system errors, the accuracy of installation errors is reduced by adopting mechanical auxiliary instruments for auxiliary installation at present, the error compensation mode needs repeated measurement, and is accompanied with a large amount of data processing, so that the workload is large, the working efficiency is low, and even extra errors are added in the detection process. Therefore, as no effective error compensation method exists, the current microscopic operation is dependent on pure manual or semi-automatic operation without exception, but obviously, the requirements of high efficiency and high quality operation of modern intelligent medical technology cannot be met.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide the self-correcting algorithm for the errors of the micro-operation platform based on the machine vision, which has wide applicability, can efficiently and accurately make up the errors and improve the precision of a micro-operation system. The technical scheme is as follows:
a machine vision based autonomous correction algorithm for errors of a micromanipulation platform, comprising:
placing the scale on a micro-operation platform, respectively moving the micro-operation platform along a fixed direction in a fixed step manner to obtain scale images, and ensuring that the front image and the rear image are partially overlapped;
splicing the front image and the rear image in the direction;
calculating the systematic error of the micro-operation platform in the direction;
and autonomously compensating the system error in the direction, and correcting the system error in the direction.
As a further improvement of the invention, the method also comprises the following steps:
and respectively moving the micro-operation platform along other directions by fixed stepping to respectively acquire images, calculating the system errors of the micro-operation platform in other directions, and performing autonomous compensation on the system errors in other directions to finish the system error correction in all directions.
As a further improvement of the invention, all directions comprise positive X-axis direction, negative X-axis direction, positive Y-axis direction and negative Y-axis direction.
As a further improvement of the present invention, the calculating the systematic error of the micromanipulation platform in the direction specifically includes:
calculating a pixel pitch;
calculating the actual displacement distance of the front image and the rear image in the direction according to the pixel distance;
and obtaining the system error of the direction according to the actual displacement distance.
As a further improvement of the present invention, the calculating the pixel pitch specifically includes:
calculating the pixel pitch by adopting a formula S as M/N; wherein S is the pixel pitch, M is the length of the scale, and N is the number of pixels within the length of M.
As a further improvement of the present invention, the calculating the actual displacement distance between the front image and the rear image in the direction according to the pixel pitch specifically includes:
using the formula AA1 practice of＝S*AA1Calculating an actual displacement distance; wherein, AA1 practice ofFor actual displacement distance, AA1The number of pixels of the relative displacement of the front and the back images in the direction is shown.
As a further improvement of the present invention, the obtaining of the systematic error in the direction according to the actual displacement distance specifically includes:
according to formula AA1 realitycos (. theta.) and AA1 realitysin (θ) obtains two components of the systematic error in that direction; and theta is a deflection angle between the image coordinate system and the coordinate system of the micro-operation platform.
As a further improvement of the present invention, the autonomously compensating the systematic error of the direction and correcting the systematic error of the direction specifically includes:
and performing compensation calculation through closed-loop feedback of a computer, performing autonomous compensation on the system error in the direction, and correcting the system error in the direction.
As a further improvement of the invention, the method also comprises the following steps: and shooting a plurality of groups of images, and calculating for a plurality of times to obtain the average value of the systematic errors of the micro-operation platform in the direction.
As a further improvement of the invention, the scale is a two-dimensional planar scale.
The invention has the beneficial effects that:
the invention discloses a micro-operation platform error self-correction algorithm based on machine vision, which abandons the traditional mode that the data acquisition and calibration are respectively carried out on all factors (such as a translational motion part, a rotary motion part and a rolling motion part) which possibly have errors by manual work, and the error correction is carried out by manual compensation.
In addition, the system error autonomous compensation algorithm provided by the invention is not only suitable for a micro-operation system, but also suitable for error correction of other mobile platforms, is simple to operate, and has the characteristics of high efficiency, high precision and the like.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood, the present invention may be implemented in accordance with the content of the description, and in order to make the above and other objects, features, and advantages of the present invention more clearly understood, the following preferred embodiments are described in detail with reference to the accompanying drawings.
Drawings
FIG. 1 is a flow chart of an error self-correction algorithm for a micro-operation platform based on machine vision in an embodiment of the invention;
FIG. 2 is a schematic diagram of two front and back images in an embodiment of the invention;
FIG. 3 is a schematic diagram of splicing two front and back images according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of stitching two forward and backward X-axis images according to an embodiment of the present invention.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
As shown in fig. 1, the autonomous error correction algorithm for a micro-manipulation platform based on machine vision in the embodiment of the present invention includes the following steps:
and step S10, placing the ruler on the micromanipulation platform, respectively moving the micromanipulation platform along a fixed direction by fixed stepping, acquiring the image of the ruler, and ensuring that the front image and the rear image are partially overlapped.
In this embodiment, the scale is a two-dimensional planar scale. The two images before and after the acquisition are shown in fig. 2, i.e., the front frame and the rear frame.
And step S20, splicing the front and rear images in the direction. The stitched image is shown in fig. 3.
And step S30, calculating the systematic error of the micromanipulation platform in the direction. The method specifically comprises the following steps:
step S31, calculating the pixel pitch; the method specifically comprises the following steps:
calculating the pixel pitch by adopting a formula S as M/N; wherein S is the pixel pitch, M is the length of the scale, and N is the number of pixels within the length of M.
Step S32, calculating the actual displacement distance of the front image and the rear image in the direction according to the pixel distance; the method specifically comprises the following steps:
using the formula AA1 practice of＝S*AA1Calculating an actual displacement distance; wherein, AA1 practice ofFor actual displacement distance, AA1The number of pixels of the relative displacement of the front and the back images in the direction is shown.
And step S33, obtaining the system error of the direction according to the actual displacement distance. The method specifically comprises the following steps:
according to formula AA1 realitycos (. theta.) and AA1 realitysin (θ) obtains two components of the systematic error in that direction; and theta is a deflection angle between the image coordinate system and the coordinate system of the micro-operation platform.
As shown in FIG. 4, when the micro-manipulation platform is moving in the X-axis forward direction, the systematic error in the X-axis forward direction is + X Δ, which can be divided into + X ΔX，+XΔyTwo components, satisfying the following formula:
+XΔX＝AA1 practice of*cos(θ)；
+XΔy＝AA1 practice of*sin(θ)；
+XΔXAnd + X.DELTA.yI.e. the compensation value to be compensated when the X-axis moves in the positive direction. The compensation value of the motion in other directions can be obtained in the same way.
And step S40, automatically compensating the system error in the direction and correcting the system error in the direction. The method specifically comprises the following steps:
and performing compensation calculation through closed-loop feedback of a computer, performing autonomous compensation on the system error in the direction, and correcting the system error in the direction.
In this embodiment, the algorithm further includes: and respectively moving the micro-operation platform along other directions by fixed stepping to respectively acquire images, calculating the system errors of the micro-operation platform in other directions, and performing autonomous compensation on the system errors in other directions to finish the system error correction in all directions. Wherein, all directions comprise X-axis positive direction, X-axis negative direction, Y-axis positive direction and Y-axis negative direction.
In this embodiment, the algorithm further includes: and shooting a plurality of groups of images, and calculating for a plurality of times to obtain the average value of the systematic errors of the micro-operation platform in the direction. The system error calculation precision can be improved, and finally the error correction precision is improved.
The invention discloses a micro-operation platform error self-correction algorithm based on machine vision, which abandons the traditional mode that the data acquisition and calibration are respectively carried out on all factors (such as a translational motion part, a rotary motion part and a rolling motion part) which possibly have errors by manual work, and the error correction is carried out by manual compensation.
In addition, the system error autonomous compensation algorithm provided by the invention is not only suitable for a micro-operation system, but also suitable for error correction of other mobile platforms, is simple to operate, and has the characteristics of high efficiency, high precision and the like.
The above embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.
Claims (10)
1. An error self-correction algorithm of a micro-operation platform based on machine vision is characterized by comprising the following steps:
placing the scale on a micro-operation platform, respectively moving the micro-operation platform along a fixed direction in a fixed step manner to obtain scale images, and ensuring that the front image and the rear image are partially overlapped;
splicing the front image and the rear image in the direction;
calculating the systematic error of the micro-operation platform in the direction;
and autonomously compensating the system error in the direction, and correcting the system error in the direction.
2. The machine vision based autonomous correction algorithm for errors of a micromanipulation platform of claim 1, further comprising:
and respectively moving the micro-operation platform along other directions by fixed stepping to respectively acquire images, calculating the system errors of the micro-operation platform in other directions, and performing autonomous compensation on the system errors in other directions to finish the system error correction in all directions.
3. The machine vision-based autonomous error correction algorithm for a micromanipulation platform of claim 2 wherein said all directions comprise positive X-axis, negative X-axis, positive Y-axis, and negative Y-axis.
4. The machine vision-based autonomous error correction algorithm for the micromanipulation platform according to claim 1, wherein the calculating the systematic error of the micromanipulation platform in the direction specifically comprises:
calculating a pixel pitch;
calculating the actual displacement distance of the front image and the rear image in the direction according to the pixel distance;
and obtaining the system error of the direction according to the actual displacement distance.
5. The machine vision-based autonomous correction algorithm for errors of the micro-manipulation platform according to claim 4, wherein the calculating the pixel pitch specifically comprises:
calculating the pixel pitch by adopting a formula S as M/N; wherein S is the pixel pitch, M is the length of the scale, and N is the number of pixels within the length of M.
6. The machine vision-based autonomous error correction algorithm for the micromanipulation platform according to claim 5, wherein the calculating of the actual displacement distance between the two images in front and back in the direction according to the pixel pitch specifically comprises:
using the formula AA1 practice of＝S*AA1Calculating an actual displacement distance; wherein, AA1 practice ofFor actual displacement distance, AA1The number of pixels of the relative displacement of the front and the back images in the direction is shown.
7. The machine vision-based autonomous error correction algorithm for the micromanipulation platform according to claim 6, wherein the obtaining of the systematic error in the direction according to the actual displacement distance specifically comprises:
according to formula AA1 realitycos (. theta.) and AA1 realitysin (θ) obtains two components of the systematic error in that direction; and theta is a deflection angle between the image coordinate system and the coordinate system of the micro-operation platform.
8. The machine vision-based autonomous error correction algorithm for the micromanipulation platform according to claim 1, wherein the autonomously compensating the systematic error in the direction to correct the systematic error in the direction comprises:
and performing compensation calculation through closed-loop feedback of a computer, performing autonomous compensation on the system error in the direction, and correcting the system error in the direction.
9. The machine vision based autonomous correction algorithm for errors of a micromanipulation platform of claim 1, further comprising: and shooting a plurality of groups of images, and calculating for a plurality of times to obtain the average value of the systematic errors of the micro-operation platform in the direction.
10. The machine vision-based autonomous error correction algorithm for a micromanipulation platform of claim 1, wherein said scale is a two-dimensional planar scale.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202010387435.1A CN111768445A (en) | 2020-05-09 | 2020-05-09 | Micro-operation platform error self-correction algorithm based on machine vision |
| PCT/CN2020/096364 WO2021227189A1 (en) | 2020-05-09 | 2020-06-16 | Micromanipulation platform autonomous error correction algorithm based on machine vision |
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| CN202010387435.1A CN111768445A (en) | 2020-05-09 | 2020-05-09 | Micro-operation platform error self-correction algorithm based on machine vision |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112433355A (en) * | 2020-11-13 | 2021-03-02 | 江苏集萃微纳自动化系统与装备技术研究所有限公司 | Micro-operation system with automatic error correction function |
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| CN119693404B (en) * | 2024-12-19 | 2025-09-26 | 哈尔滨理工大学 | A micro-operation target segmentation method based on improved Otsu and edge operator |
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| CN103455993A (en) * | 2013-09-30 | 2013-12-18 | 电子科技大学 | Two-dimensional image splicing method based on grating positioning in automatic visual inspection |
| CN105444699A (en) * | 2015-11-11 | 2016-03-30 | 苏州大学附属儿童医院 | Coordinate and displacement error detection and compensation method for microscope operating system |
| CN109848986A (en) * | 2019-01-08 | 2019-06-07 | 北京市城市管理研究院（北京市环境卫生监测中心） | Localization method when target object height based on machine vision is uncertain |
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| US6912435B2 (en) * | 2002-08-28 | 2005-06-28 | Inficon Lt Inc. | Methods and systems for controlling reticle-induced errors |
| CN102842117B (en) * | 2012-07-13 | 2015-02-25 | 浙江工业大学 | Method for correcting kinematic errors in microscopic vision system |
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- 2020-05-09 CN CN202010387435.1A patent/CN111768445A/en active Pending
- 2020-06-16 WO PCT/CN2020/096364 patent/WO2021227189A1/en not_active Ceased
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103455993A (en) * | 2013-09-30 | 2013-12-18 | 电子科技大学 | Two-dimensional image splicing method based on grating positioning in automatic visual inspection |
| CN105444699A (en) * | 2015-11-11 | 2016-03-30 | 苏州大学附属儿童医院 | Coordinate and displacement error detection and compensation method for microscope operating system |
| CN109848986A (en) * | 2019-01-08 | 2019-06-07 | 北京市城市管理研究院（北京市环境卫生监测中心） | Localization method when target object height based on machine vision is uncertain |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112433355A (en) * | 2020-11-13 | 2021-03-02 | 江苏集萃微纳自动化系统与装备技术研究所有限公司 | Micro-operation system with automatic error correction function |
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Application publication date: 20201013 |