CN106550172A - Video stabilization in assemble of the satellite based on subregion Gray Projection - Google Patents
Video stabilization in assemble of the satellite based on subregion Gray Projection Download PDFInfo
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Abstract
The present invention relates to the Video stabilization in a kind of assemble of the satellite based on subregion Gray Projection, technical characteristics are to comprise the following steps：(1) division of image region and delete choosing；(2) Gray Projection in ranks direction；(3) solve global motion vector；(4) calculating of motion compensation vector.The present invention is reasonable in design, and which is divided and deleted the low subregion of contrast in front and back's two field pictures, obtains local motion component by being spaced projection and computing cross-correlation；Then the subregion containing moving target is deleted using the iterative step based on mean error thresholding, global motion vector is solved using the local motion component of remaining area, it is thus able to fast and effectively eliminate the randomized jitter of assemble of the satellite picture, the stable coherent assemble of the satellite picture of output, it is ensured that robot system can smoothly complete the ground-mounted work of satellite.
Description
Technical field
The present invention relates in assemble of the satellite video pictures it is steady as technology, be based in specially a kind of assemble of the satellite
The Video stabilization of subregion Gray Projection.
Background technology
Various countries pay much attention to government utilities such as national security, social progress, in aerospace field, it is ensured that
The accurate real-time ground assembling of all kinds of satellite equipments has become the key that field of aerospace can be fast-developing.
According to the related request of satellite ground assembling, it is necessary to assure optical camera, experimental provision and instrument, communicate and
The stand-alone device fast and stables such as detecting devices, space telescope drive mechanism, support and antenna it is integrated.Mesh
Front assemble of the satellite technology development is relatively slow, and whole assembling process still relies primarily on the experience of engineering staff and carries out determining
Property judge, artificial operation certainly exist cannot be between digitized measurement accessory relative position, pose adjustment can not
Quantization, the visual not strong situation in crucial docking site.Adopt the mobile-robot system of assemble of the satellite can be with
The accurate real-time ground assembling of satellite is carried out, but generally to original input video picture in assembling process
Carry out simple pretreatment, and electronic image stabilizing is due to can preferably eliminate the randomized jitter of video pictures,
Thus it has been obviously improved the degree that stably links up of assembling picture, it is ensured that robot system smoothly completes the ground of satellite
Face assembly work.The defect for how overcoming conditional electronic Video stabilization to exist, it is still desirable to related innovatory algorithm
Solved.The robot system of assemble of the satellite work itself has higher practical value, if will be excellent
Electronic image stabilizing is applied to mobile-robot system, lifts the stability and continuity of assembling picture, will tool
There are higher real value and theory significance.
At present, classical Electronic Image Stabilization mainly has following several：
1) Gray Projection method；2) Block Matching Algorithm；3) bit plane method；4) Feature Points Matching method；
Stable coherent video pictures are obtained using electronic steady image correlation technique and belongs to Image semantic classification part, state
Gray Projection method and block matching algorithm is generally adopted to carry out stablizing for video pictures on border, but satellite robot
It is big to video pictures demand in assembling process, while the stability requirement to video pictures is high, tradition ash
Degree sciagraphy is steady as degree of accuracy is not enough and Block- matching related algorithm real-time performance in low contrast assembling picture
Difference, it is impossible to meet the demand of assemble of the satellite environment, therefore find one kind and rationally, efficiently improve steady picture technology
It is particularly important.
The content of the invention
For the deficiencies in the prior art, the technical problem that the present invention is intended to solve is to propose one kind based on subregion ash
The Electronic Image Stabilization of degree projection simultaneously applies to robot system, completes the real-time accurate ground-mounted of satellite,
The method is divided and is deleted the low subregion of contrast in front and back's two field pictures, is projected and mutual by being spaced
Related operation obtains local motion component；Then deleted using the iterative step based on mean error thresholding and contained
The subregion of moving target, solves global motion vector using the local motion component of remaining area；If it is determined that
There is low-frequency sweep component, mean filter process is also done to the global motion vector of multiple image, by the overall situation
Motion vector calculates high-frequency jitter component with mean filter result；Figure is completed using Contrary compensation technology finally
As filling, method are easy, it is easy to practical application.
The present invention solves the technical scheme of the technical problem, designs a kind of electricity based on subregion Gray Projection
Sub- Video stabilization.The method using the step of be：
(1) division of image region and delete choosing：By four rectangles that all sub-zone dividings are formed objects
Block, rectangular block circulation subtract each other and calculate the pixel absolute difference between adjacent rectangle block and, by result of calculation with it is pre-
The threshold value for first setting compares, and judges whether to proceed project.
(2) Gray Projection in ranks direction：Subregion to meeting threshold condition is carried out to ranks direction respectively
Projection, carries out the projection in subregion ranks direction using the zone line of curve.By way of interval takes a little,
The execution time of algorithm is reduced at double.
(3) solve global motion vector：Computing cross-correlation is adopted to Gray Projection result, ranks direction is determined
Local motion vector.Mean filter process is carried out to local motion vector set and obtains global motion vector.
Motion target area is eliminated to global motion vector estimated accuracy using the iterative algorithm based on mean error thresholding
Impact.
(4) calculating of motion compensation vector：Judge whether present image is moved containing normal scan.If not depositing
In scanning motion component, then current global motion vector is both required motion compensation vector；Otherwise, adopt
The low-frequency sweep component of multiple image is calculated first with the mode of gaussian filtering, and then successively global motion is sweared
Amount is poor with scanning motion component, obtains the jittering component of high frequency.
Compared with prior art, the Video stabilization in assemble of the satellite of the present invention based on subregion Gray Projection is because right
Two field pictures are divided and are deleted the low subregion of contrast in front and back, by being spaced projection and computing cross-correlation
Obtain local motion component；Then deleted using the iterative step based on mean error thresholding and contain moving target
Subregion, using remaining area local motion component solve global motion vector, it is thus possible to quickly have
The randomized jitter of the elimination assemble of the satellite picture of effect, the stable coherent assemble of the satellite picture of output, it is ensured that machine
Device people system can smoothly complete the ground-mounted work of satellite.
Description of the drawings
Fig. 1 is a kind of flow process of embodiment of Video stabilization in assemble of the satellite of the present invention based on subregion Gray Projection
Figure；
Fig. 2 is that a kind of embodiment of Video stabilization in assemble of the satellite of the present invention based on subregion Gray Projection takes throwing
Projection result of the shadow curved intermediate part point as subregion ranks direction, so as to improve estimating for local motion vector
The schematic diagram of meter precision；
Fig. 3 is a kind of utilization of embodiment of Video stabilization in assemble of the satellite of the present invention based on subregion Gray Projection
The trough of two correlation curves determines the schematic diagram of ranks direction local motion vector；
Fig. 4 is a kind of employing of embodiment of Video stabilization in assemble of the satellite of the present invention based on subregion Gray Projection
Iterative algorithm based on mean error thresholding eliminates shadow of the motion target area to global motion vector estimated accuracy
Loud flow chart.
Specific embodiment
The video assembling picture of one group of randomized jitter of robot system collection is embodiment, and combines which
Accompanying drawing, further describes to subregion gray projection algorithm of the present invention.
Video stabilization in assemble of the satellite according to the present invention based on subregion Gray Projection (abbreviation method, referring to
Fig. 1-4), be research satellite assembling float rule on the basis of, by way of subregion Gray Projection,
The steady as work of video pictures is completed, so as to ensure that robot system can complete target in smooth picture
Fast search work.Methods described is concretely comprised the following steps：
The division of 1 image region and delete choosing
Before and after selection successively of the invention, adjacent two frame assembles picture as benchmark image and present image, enters respectively
The division of row subregion.Before projection, it is necessary first to avoid not significantly causing local due to the region contrast
The larger problem of component motion estimated bias occurs.The solution of this paper be by all sub-zone dividings be phase
With four rectangular blocks of size, then circulation is subtracted each other successively, calculate pixel absolute difference between adjacent rectangle block and,
Result of calculation is compared with threshold value set in advance, only when all rectangular blocks absolute difference and be above threshold
During value, current sub-region just carries out project.
The Gray Projection in 2 ranks directions
The subregion of threshold condition is met to more than, is projected to ranks direction respectively.Either line direction
Or column direction, its marginal area have uniqueness, the drop shadow curve of subregion is caused to exist in edge poor
It is different, affect the local motion vector estimated accuracy of subregion.And the zone line of drop shadow curve have it is close
Crest and trough, simply position there is deviation, therefore the mid portion of this paper Zhi Qu drop shadow curves as subregion
The projection result in ranks direction, it is to avoid the interference of marginal area, so as to improve the estimation of local motion vector
Precision.In addition, mode a little is taken herein by interval, in the case where precision is had little influence on, at double
Reduce the execution time of algorithm.
3 solve global motion vector
Computing cross-correlation is adopted to Gray Projection result of the reference frame with current frame image corresponding sub-region, according to
The trough of two correlation curves, determines ranks of the current frame image subregion relative to reference frame image subregion
Direction local motion vector.Then the average result for calculating local motion vector set is obtained present frame phase
For the global motion vector of reference frame image；
But, it is contemplated that moving foreground object is there may be in the assembling picture of robot system, the subregion
Local motion component estimated result by the calculating of severe jamming global motion vector, in some instances it may even be possible to cause steady picture
Failure.Therefore, motion target area is eliminated to the overall situation using the iterative algorithm based on mean error thresholding herein
The impact of estimation of motion vectors precision.After the subregion containing foreground moving object is excluded, you can solve most
Whole global motion vector.
The calculating of 4 motion compensation vectors
When camera head secondary satellite robotic asssembly, the global motion vector of reference frame and current frame image
May be made up of the high fdrequency components of the low frequency component of normal scan and abnormal shake, it is also possible to only tremble comprising abnormal
Dynamic high fdrequency components.When motion compensation is carried out to image, it is only necessary to compensate the randomized jitter of camera head.
Therefore, first determine whether whether present image is moved containing normal scan.The foundation of judgement is：Robot system
Normal movement generally there is interim amplitude and orientation consistency, be smooth low frequency vector, and shake
The amplitude of component and direction have randomness.After judging to finish, if there is no scanning motion component, when
Front global motion vector is both required motion compensation vector；Otherwise, herein from real-time and effectiveness side
Face is considered, is calculated the low-frequency sweep component of multiple image first, then successively will by the way of mean filter
Global motion vector is poor with scanning motion component, obtains the jittering component of high frequency, is the fortune of every two field picture
Dynamic compensation vector.Finally, image completion can be completed using Contrary compensation technology.
Although a kind of video of the randomized jitter that the inventive method is gathered with robot system assembles picture to implement
Example has been described in detail, but its principle and process are applied to the steady of the rotation transformation video image of other occasions
As work.
The present invention does not address part and is applied to prior art.
Claims (1)
1. the Video stabilization in a kind of assemble of the satellite based on subregion Gray Projection, it is characterised in that including following step
Suddenly：
(1) division of image region and delete choosing：By four rectangles that all sub-zone dividings are formed objects
Block, rectangular block circulation subtract each other and calculate the pixel absolute difference between adjacent rectangle block and, by result of calculation with it is pre-
The threshold value for first setting compares, and judges whether to proceed project；
(2) Gray Projection in ranks direction：Subregion to meeting threshold condition is carried out to ranks direction respectively
Projection, carries out the projection in subregion ranks direction using the zone line of curve；By way of interval takes a little,
The execution time of algorithm is reduced at double；
(3) solve global motion vector：Computing cross-correlation is adopted to Gray Projection result, ranks direction is determined
Local motion vector；Mean filter process is carried out to local motion vector set and obtains global motion vector；
Motion target area is eliminated to global motion vector estimated accuracy using the iterative algorithm based on mean error thresholding
Impact；
(4) calculating of motion compensation vector：Judge whether present image is moved containing normal scan；If not depositing
In scanning motion component, then current global motion vector is both required motion compensation vector；Otherwise, adopt
The low-frequency sweep component of multiple image is calculated first with the mode of gaussian filtering, and then successively global motion is sweared
Amount is poor with scanning motion component, obtains the jittering component of high frequency.
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Cited By (4)
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| CN108805832A (en) * | 2018-05-29 | 2018-11-13 | 重庆大学 | Improvement Gray Projection digital image stabilization method suitable for tunnel environment characteristic |
| CN111010494A (en) * | 2019-10-28 | 2020-04-14 | 武汉大学 | Optical satellite video image stabilization method and system with geocoding function |
| CN112589401A (en) * | 2020-11-09 | 2021-04-02 | 苏州赛腾精密电子股份有限公司 | Assembling method and system based on machine vision |
| CN114061486A (en) * | 2021-11-19 | 2022-02-18 | 南京航空航天大学 | Automatic measuring device and method for large-scale skin curved surface of airplane |
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| CN102098440A (en) * | 2010-12-16 | 2011-06-15 | 北京交通大学 | Electronic image stabilizing method and electronic image stabilizing system aiming at moving object detection under camera shake |
| CN103955930A (en) * | 2014-04-28 | 2014-07-30 | 中国人民解放军理工大学 | Motion parameter estimation method based on gray integral projection cross-correlation function characteristics |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108805832A (en) * | 2018-05-29 | 2018-11-13 | 重庆大学 | Improvement Gray Projection digital image stabilization method suitable for tunnel environment characteristic |
| CN108805832B (en) * | 2018-05-29 | 2022-02-08 | 重庆大学 | Improved gray projection image stabilizing method suitable for tunnel environment characteristics |
| CN111010494A (en) * | 2019-10-28 | 2020-04-14 | 武汉大学 | Optical satellite video image stabilization method and system with geocoding function |
| CN111010494B (en) * | 2019-10-28 | 2020-11-03 | 武汉大学 | Optical satellite video image stabilization method and system with geocoding function |
| CN112589401A (en) * | 2020-11-09 | 2021-04-02 | 苏州赛腾精密电子股份有限公司 | Assembling method and system based on machine vision |
| CN112589401B (en) * | 2020-11-09 | 2021-12-31 | 苏州赛腾精密电子股份有限公司 | Assembling method and system based on machine vision |
| CN114061486A (en) * | 2021-11-19 | 2022-02-18 | 南京航空航天大学 | Automatic measuring device and method for large-scale skin curved surface of airplane |
| CN114061486B (en) * | 2021-11-19 | 2022-08-16 | 南京航空航天大学 | Automatic measuring device and method for large-scale skin curved surface of airplane |
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