CN110099267A - Trapezoidal correcting system, method and projector - Google Patents
Trapezoidal correcting system, method and projector Download PDFInfo
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- CN110099267A CN110099267A CN201910447410.3A CN201910447410A CN110099267A CN 110099267 A CN110099267 A CN 110099267A CN 201910447410 A CN201910447410 A CN 201910447410A CN 110099267 A CN110099267 A CN 110099267A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N9/00—Details of colour television systems
- H04N9/12—Picture reproducers
- H04N9/31—Projection devices for colour picture display, e.g. using electronic spatial light modulators [ESLM]
- H04N9/3179—Video signal processing therefor
- H04N9/3185—Geometric adjustment, e.g. keystone or convergence
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Abstract
The invention discloses a kind of trapezoidal correcting system, method and projector, the trapezoidal correcting system includes projection module, for projecting the predetermined pattern including multiple feature angle points；Camera module, for shooting image；Processor, for obtaining the calibrating parameters of the feature angle point in predetermined pattern and system, it controls camera module and shoots image, and the identification feature angle point from the image that the camera module is shot, the point pair for matching the feature angle point in image captured by predetermined pattern and camera module, then according to the point to and system calibrating parameter obtain the normal vector of projection metope；Normal vector is inputted in trained neural network, angle point correction parameter is obtained；The projected picture of projection module is corrected according to the angle point correction parameter.The present invention can realize that it is lower to save such as rangefinder component, cost compared with the existing technology for keystone by a camera.The present invention can be widely applied to keystone technical field.
Description
Technical field
The present invention relates to keystone technical field, especially a kind of trapezoidal correcting system, method and projector.
Background technique
Projector is a kind of equipment for projecting image onto wall or projection screen by optical projection.It is different with display
Sample is that the display area size of projector is not limited by projector volume, and the projector of very little can be projected out hundreds of cun
Display area.Under the scene for needing huge display area, projector becomes preferred option with the cost of relative moderate.
In traditional projector, projector needs face metope, just can guarantee that the picture projected is one normal
Rectangle.Once the projecting direction of projector is not vertical with metope, it will the picture projected is made to deform.At this time
People need to correct this deformation by the posture of the camera lens or projector itself that manually adjust projector.
And the keystone technology of projector is born with the development of technology, at present in the keystone technology of projector
In, mainly corrected with binocular based on, however binocular correction needs to use two cameras or needs range sensor to match
Conjunction camera, and additional camera or range sensor, will increase hardware cost for projector.
Summary of the invention
In order to solve the above technical problems, it is an object of the invention to: a kind of trapezoidal correcting system, method and projection are provided
Instrument reduces the hardware cost of trapezoidal correcting system or projector to realize keystone by single camera.
The first aspect of the embodiment of the present invention provides:
A kind of trapezoidal correcting system, comprising:
Projection module, for projecting the predetermined pattern including multiple feature angle points；
Camera module, for shooting image；
Processor controls camera module for obtaining the calibrating parameters of the feature angle point in predetermined pattern and system
Image, and the identification feature angle point from the image that the camera module is shot are shot, predetermined pattern and camera module are matched
The point pair of feature angle point in captured image, then according to the point to and system calibrating parameter obtain projection metope method
Vector；Normal vector is inputted in trained neural network, angle point correction parameter is obtained；It is repaired according to the angle point correction parameter
The projected picture of orthographic projection module.
Further, the point pair for matching the feature angle point in image captured by predetermined pattern and camera module, connects
According to the point to and system calibrating parameter obtain projection metope normal vector, specifically include:
The point pair for matching the feature angle point in image captured by predetermined pattern and camera module, obtains multiple characteristic angles
The point pair of point；
According to the point of each feature angle point to the depth coordinate for calculating each feature angle point, the 3D of each feature angle point is obtained
Coordinate；
The normal vector of projection metope is obtained according to the 3D coordinate of multiple feature angle points.
Further, from the image that camera module is shot when identification feature angle point, using SURF algorithm.
Further, using brute-force algorithm to the point of the feature angle point in image captured by predetermined pattern and camera module
To matching, the point pair of invalid feature angle point is then filtered out by RANSAC algorithm.
The second aspect of the embodiment of the present invention provides:
A kind of trapezoidal distortion correction method, comprising the following steps:
Obtain the calibrating parameters of the feature angle point and system in predetermined pattern；
It controls camera module and shoots image, and the identification feature angle point from the image that the camera module is shot；
Match the point pair of the feature angle point in image captured by predetermined pattern and camera module；
According to the point to and system calibrating parameter obtain projection metope normal vector；
Normal vector is inputted in trained neural network, angle point correction parameter is obtained；
The projected picture of projection module is corrected according to the angle point correction parameter.
Further, the point pair for matching the feature angle point in image captured by predetermined pattern and camera module, connects
According to the point to and system calibrating parameter obtain projection metope normal vector, specifically include:
The point pair for matching the feature angle point in image captured by predetermined pattern and camera module, obtains multiple characteristic angles
The point pair of point；
According to the point of each feature angle point to the depth coordinate for calculating each feature angle point, the 3D of each feature angle point is obtained
Coordinate；
The normal vector of projection metope is obtained according to the 3D coordinate of multiple feature angle points.
Further, from the image that camera module is shot when identification feature angle point, using SURF algorithm.
Further, using brute-force algorithm to the point of the feature angle point in image captured by predetermined pattern and camera module
To matching, the point pair of invalid feature angle point is then filtered out by RANSAC algorithm.
The third aspect of the embodiment of the present invention provides:
A kind of trapezoidal correcting system, comprising:
Memory, for storing program；
Processor executes the trapezoidal distortion correction method for loading described program.
The fourth aspect of the embodiment of the present invention provides:
A kind of projector, including shell and the trapezoidal correcting system being mounted among the shell.
The beneficial effects of the present invention are: the present invention among the image that camera module is shot by matching in predetermined pattern
Feature angle point, thus calculate projection metope normal vector, normal vector is then input to trained neural network, is obtained
The correction parameter of feature angle point carries out rectangle correction by single camera to realize, compared with prior art, the present invention
Required hardware component is less, therefore cost is lower.
Detailed description of the invention
Fig. 1 is the depth measurement schematic illustration of feature angle point in a kind of specific embodiment of the present invention；
Fig. 2 is the schematic illustration of keystone in a kind of specific embodiment of the present invention；
Fig. 3 is a kind of module frame chart of the trapezoidal correcting system of specific embodiment of the present invention；
Fig. 4 is a kind of flow chart of the trapezoidal distortion correction method of specific embodiment of the present invention.
Specific embodiment
The present invention is further detailed with specific embodiment with reference to the accompanying drawings of the specification.
Firstly, the present embodiment is illustrated the principle for the keystone that single camera is realized using neural network, this
Embodiment builds a RBF neural (a kind of three layers of radial base neural net), the wherein input of the neural network first
Parameter is the normal vector projected between metope and projector, i.e. normal vector of the projection metope in the coordinate system of projector, rear to claim
Make projection metope normal vector.Then what the neural network exported is the correct for focus offset amount of keystone, usually with four angles
For the adjusting parameter of point as indicating, the result of output is (dx0, dy0, dx1, dy1, dx2, dy2, dx3, dy3), projector according to
The output is as a result, adjust the projected picture of projector.So that projecting the picture of projection metope becomes identical as original picture ratio.
Certainly, not only RBF neural may be implemented, and can also use other neural fusions.
Based on the training of a large amount of training sample, above-mentioned neural network can be achieved on the keystone of projector.
It is discussed below and how to obtain by projector and a camera and project metope normal vector, referring to Fig.1, with point O1 table
Show the camera module of projector, the projection module of projector is indicated with point Or, in fact, projection module is considered as herein
What one reverse camera, and in Fig. 1, X1 and Xr were respectively indicated is that feature angle point P is shot in camera module respectively
The position in image that image and projection module are projected, feature angle point P is indicated in the image of camera module shooting with P1,
And feature angle point P is indicated in the image that projection module is projected with Pr, P1 and Pr is referred to as a point pair here, substantially
It is the display of an identical feature angle point in both images.It is assumed that the focal length of camera module and projector module is f,
The actual physics distance of the two is T, then the depth Z of point P can be calculated by the following formula out.
Wherein, Z=(T*f)/d；D=X1-Xr.
So after seeking the depth for calculating the different feature angle point in multiple positions, so that it may obtain camera module shooting
Then the three-dimensional coordinate of feature angle point in image is carried out the three-dimensional coordinate of all feature angle points using least square method flat
Face fitting, that is, fit the plane where all feature angle points, so that it may obtain the normal vector of projection metope.
That is, having feasibility by the normal vector that single camera measures projection metope.
And the acquisition about angle point correction parameter can take the photograph when obtaining training sample data by a panorama
As head, full-view camera is mounted on to the position of projection metope face, and full-view camera shooting direction is vertical with projection metope,
It means that image taken by full-view camera, does not distort.Therefore, on projector to projection metope
Pattern is the case where can immediately arriving at distortion from the image that full-view camera is shot in case of distortion.Namely
It says, according to the position of the feature angle point in the image of full-view camera, how counter can release is adjusted projected image
It is whole.As illustrated in fig. 2, it is assumed that the predetermined pattern 200a that projector is projected distorts after reaching projection metope 100 into a ladder
Shape 200b, in the present embodiment can by identification predetermined pattern in 4 feature angle point 201a, 201b, 201c and 201d come
Determine distortion situation.So when realizing angle point correction parameter, it is only necessary to adjust and throw according to predetermined pattern 200a original ratio
The output Aspect Ratio of shadow instrument can be using the adjusting parameter of feature angle point as indicating when adjusting ratio certainly.Such as Fig. 2
Shown, by ratio pattern 200c adjusted, pattern becomes and predetermined pattern 200a after projecting to projection metope 100 again
The identical pattern 200d of ratio.
So far, the present embodiment can obtain multiple groups training sample data, each group of instruction by adjusting the posture of projector
Practicing sample all includes one group of normal vector and corresponding feature angle point correction parameter.
So when manufacturing projector or trapezoidal correcting system, it can will be trained by a large amount of training sample data
Neural network afterwards is put among system or projector.
Referring to Fig. 3, the present embodiment provides a kind of trapezoidal correcting systems, realize rectangle correction with single camera, reduce ladder
Shape corrects the hardware cost of system or projector.
Projection module, for projecting the predetermined pattern including multiple feature angle points.Certainly, projection module is also projector
Basic module is also used to the picture that projection lenses instrument needs to project.
Camera module, for shooting image.In the present embodiment, camera module and projector are fixedly mounted on throwing
On shadow instrument, that is to say, that the relative position and relative angle of camera module and projector module are all determining.
Processor controls camera module for obtaining the calibrating parameters of the feature angle point in predetermined pattern and system
Image, and the identification feature angle point from the image that the camera module is shot are shot, predetermined pattern and camera module are matched
The point pair of feature angle point in captured image, then according to the point to and system calibrating parameter obtain projection metope method
Vector；Normal vector is inputted in trained neural network, angle point correction parameter is obtained；It is repaired according to the angle point correction parameter
The projected picture of orthographic projection module.
Specifically, the calibrating parameters include the intrinsic parameter and camera of the intrinsic parameter of camera module, projection module
Positional relationship between module and projection module.Since projector module here can be counted as a reverse camera,
Pin-hole model expression can be used.Intrinsic parameter is only related with itself, equal for the intrinsic parameter of camera module and projector module
Including parameter matrix (fx, fy, cx, cy) and distortion factor (three radial directions k1, k2, k3；Two tangential p1 and p2).
The processor is generally the arithmetic element among projector, and the processor can be by one or more chip
Composition.
According to combining discussion of the Fig. 1 and Fig. 2 to measuring principle, in the system of the present embodiment, due to camera module and
The distance between projection module is that preparatory calibration is good, therefore, by comparing figure captured by predetermined pattern and camera module
As in, the positional relationship of feature angle point, the i.e. positional relationship of the point pair of feature angle point, can determine feature angle point in projector
Coordinate system in depth, through the depth and these feature angle points of multiple feature angle points in the coordinate system of projector
Transverse and longitudinal coordinate can determine the normal vector of metope.Then normal vector is input to neural network, to obtain feature angle point
Correction parameter.
As for feature angle point identification and match, a variety of recognizers can be used.
For example, SURF algorithm can be used in the identification for realizing feature angle point；And when doing feature corners Matching, it can
To realize using BF algorithm and the cooperation of RANSAC algorithm, go out the point pair of some feature angle points by BF algorithmic match first, then
Using RANSAC algorithm to these by brute-force match come point to being filtered, remove invalid point pair.SURF is calculated
Method can also be substituted with other SIFT algorithms.
BF is the abbreviation of Brute Force, and BF algorithm, that is, storm wind algorithm is common pattern matching algorithm, BF algorithm
Thought is exactly to match the first character of target strings S with the first character of pattern string T, if equal, continues to compare S
Second character and T second character；If unequal, compare the first character of second character and T of S, successively
Compare down, until obtaining last matching result.BF algorithm is a kind of brute-force algorithm.
SURF is the abbreviation of Speeded Up Robust Features, it is a kind of calculation for SIFT algorithm improvement
Method.
SIFT is the abbreviation of Scale-invariant feature transform, it is for field of image processing
A kind of description.This description has scale invariability, can detect key point in the picture, is a kind of local feature description's.
RANSAC is the abbreviation of Random Sample Consensus, it is the sample according to one group comprising abnormal data
Data set calculates the mathematical model parameter of data, obtains the algorithm of effective sample data.
In the present embodiment, projection module can be constant in the predetermined pattern that whole process is projected, i.e., each trapezoidal school
Timing, predetermined pattern are all constant；It is of course also possible to when doing keystone every time, using different predetermined patterns, i.e., from more
It recycles in a predetermined pattern or randomly selects.
About the feature angle point in predetermined pattern, preferably at four or more, feature angle point is more, can more reduce individual spies
The influence generated after the identified mistake of angle point to the calculating of normal vector, therefore the quantity of appropriate lifting feature angle point are levied, can be improved
The accuracy of identification.And the feature of different feature angle points should be different, for example, shape is different or pattern is different.And
Feature angle point should have enough identification degrees under the environment such as anti-gray scale, colour.
Feature angle point as preferred embodiment, in image captured by the matching predetermined pattern and camera module
Point pair, then according to the point to and system calibrating parameter obtain projection metope normal vector, specifically include:
The point pair for matching the feature angle point in image captured by predetermined pattern and camera module, obtains multiple characteristic angles
The point pair of point；Shown by here, by a same characteristic features angle point in the image captured by predetermined pattern and camera module
Point is used as a point pair.
According to the point of each feature angle point to the depth coordinate for calculating each feature angle point, the 3D of each feature angle point is obtained
Coordinate；By analysis as shown in figure 1, each feature angle point can be calculated by the potential difference of the point pair of feature angle point and is being projected
Depth coordinate in instrument coordinate system, and the image according to captured by camera obtains each feature angle point in the camera coordinate system
Transverse and longitudinal coordinate, to obtain 3D coordinate of each feature angle point in projector coordinates system.
The normal vector of projection metope is obtained according to the 3D coordinate of multiple feature angle points.Specifically, existed according to multiple characteristic points
3D coordinate in projector coordinates system, so that it may smoothly seek the normal vector for calculating projection metope in projector coordinates system.
Certainly, in the case where feature angle point is more, the feature angle point that can use redundancy carrys out the characteristic angle of rejecting abnormalities
Point, to increase the validity of feature angle point.That is, theoretically the quantity of feature angle point is more, it is more accurate to identify.Certainly
, the next also efficient decline with the promotion of precision.Therefore, feature can be arranged according to the balance of performance and efficiency
The quantity of angle point.
As preferred embodiment, from the image that camera module is shot when identification feature angle point, using SURF algorithm.
The fast several times of SURF algorithm ratio SIFT algorithm, and it is more steady than SIFT in terms of different images transformation.Thus this implementation
Example helps to improve system performance using SURF algorithm.
As preferred embodiment, using brute-force algorithm to the spy in image captured by predetermined pattern and camera module
The point of angle point is levied to matching, the point pair of invalid feature angle point is then filtered out by RANSAC algorithm.The present embodiment energy
Enough it is accurately realized the matching of the point pair of feature angle point.
Referring to Fig. 4, present embodiment discloses a kind of trapezoidal distortion correction methods, are applied to the processor of the above system embodiment
Among, the present embodiment the following steps are included:
The calibrating parameters of S401, the feature angle point in acquisition predetermined pattern and system；
S402, control camera module shoot image, and the identification feature angle from the image that the camera module is shot
Point；
The point pair of feature angle point in image captured by S403, matching predetermined pattern and camera module；
S404, according to the point to and system calibrating parameter obtain projection metope normal vector；
S405, normal vector is inputted in trained neural network, obtains angle point correction parameter；
S406, the projected picture that projection module is corrected according to the angle point correction parameter.
Feature angle point as preferred embodiment, in image captured by the matching predetermined pattern and camera module
Point pair, then according to the point to and system calibrating parameter obtain projection metope normal vector, specifically include:
The point pair for matching the feature angle point in image captured by predetermined pattern and camera module, obtains multiple characteristic angles
The point pair of point；
According to the point of each feature angle point to the depth coordinate for calculating each feature angle point, the 3D of each feature angle point is obtained
Coordinate；
The normal vector of projection metope is obtained according to the 3D coordinate of multiple feature angle points.
As preferred embodiment, from the image that camera module is shot when identification feature angle point, using SURF algorithm.
As preferred embodiment, using brute-force algorithm to the spy in image captured by predetermined pattern and camera module
The point of angle point is levied to matching, the point pair of invalid feature angle point is then filtered out by RANSAC algorithm.
Present embodiment discloses a kind of trapezoidal correcting systems comprising:
Memory, for storing program；
Processor, for loading described program to execute the trapezoidal distortion correction method as described in above method embodiment.
Present embodiment discloses a kind of projectors comprising shell and is mounted among the shell such as above-described embodiment
The trapezoidal correcting system.
Feature corresponding or identical with the above system embodiment can be realized identical skill in above method embodiment
Art effect.
For the step number in above method embodiment, it is arranged only for the purposes of illustrating explanation, between step
Sequence do not do any restriction, the execution of each step in embodiment sequence can according to the understanding of those skilled in the art come into
Row is adaptively adjusted.
It is to be illustrated to preferable implementation of the invention, but the present invention is not limited to the embodiment above, it is ripe
Various equivalent deformation or replacement can also be made on the premise of without prejudice to spirit of the invention by knowing those skilled in the art, this
Equivalent deformation or replacement are all included in the scope defined by the claims of the present application a bit.
Claims (10)
1. a kind of trapezoidal correcting system, it is characterised in that: include:
Projection module, for projecting the predetermined pattern including multiple feature angle points；
Camera module, for shooting image；
Processor, for obtaining the calibrating parameters of the feature angle point in predetermined pattern and system, control camera module shooting
Image, and the identification feature angle point from the image that the camera module is shot, match predetermined pattern and camera module is clapped
The point pair of feature angle point in the image taken the photograph, then according to the point to and system calibrating parameter obtain projection metope normal direction
Amount；Normal vector is inputted in trained neural network, angle point correction parameter is obtained；It is corrected according to the angle point correction parameter
The projected picture of projection module.
2. a kind of trapezoidal correcting system according to claim 1, it is characterised in that: the matching predetermined pattern and camera
The point pair of feature angle point in image captured by module, then according to the point to and system calibrating parameter obtain projection metope
Normal vector, specifically include:
The point pair for matching the feature angle point in image captured by predetermined pattern and camera module, obtains multiple feature angle points
Point pair；
According to the point of each feature angle point to the depth coordinate for calculating each feature angle point, the 3D for obtaining each feature angle point is sat
Mark；
The normal vector of projection metope is obtained according to the 3D coordinate of multiple feature angle points.
3. a kind of trapezoidal correcting system according to claim 1, it is characterised in that: from the image that camera module is shot
When identification feature angle point, using SURF algorithm.
4. a kind of trapezoidal correcting system according to claim 1, it is characterised in that: using brute-force algorithm to predetermined pattern and
Then the point of feature angle point in image captured by camera module is filtered out in vain to matching by RANSAC algorithm
Feature angle point point pair.
5. a kind of trapezoidal distortion correction method, it is characterised in that: the following steps are included:
Obtain the calibrating parameters of the feature angle point and system in predetermined pattern；
It controls camera module and shoots image, and the identification feature angle point from the image that the camera module is shot；
The point pair for matching the feature angle point in image captured by predetermined pattern and camera module, then according to the point to
System calibrating parameter obtains the normal vector of projection metope；
Normal vector is inputted in trained neural network, angle point correction parameter is obtained；
The projected picture of projection module is corrected according to the angle point correction parameter.
6. a kind of trapezoidal distortion correction method according to claim 5, it is characterised in that: the matching predetermined pattern and camera
The point pair of feature angle point in image captured by module, then according to the point to and system calibrating parameter obtain projection metope
Normal vector, specifically include:
The point pair for matching the feature angle point in image captured by predetermined pattern and camera module, obtains multiple feature angle points
Point pair；
According to the point of each feature angle point to the depth coordinate for calculating each feature angle point, the 3D for obtaining each feature angle point is sat
Mark；
The normal vector of projection metope is obtained according to the 3D coordinate of multiple feature angle points.
7. a kind of trapezoidal distortion correction method according to claim 5, it is characterised in that: from the image that camera module is shot
When identification feature angle point, using SURF algorithm.
8. a kind of trapezoidal distortion correction method according to claim 5, it is characterised in that: using brute-force algorithm to predetermined pattern and
Then the point of feature angle point in image captured by camera module is filtered out in vain to matching by RANSAC algorithm
Feature angle point point pair.
9. a kind of trapezoidal correcting system, it is characterised in that: include:
Memory, for storing program；
Processor, for loading described program to execute such as the described in any item trapezoidal distortion correction methods of claim 5-8.
10. a kind of projector, it is characterised in that: including shell and be mounted among the shell as claim 1,2,3,
Trapezoidal correcting system described in 4 or 9.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110677634A (en) * | 2019-11-27 | 2020-01-10 | 成都极米科技股份有限公司 | Trapezoidal correction method, device and system for projector and readable storage medium |
| CN110769235A (en) * | 2019-09-30 | 2020-02-07 | 深圳市火乐科技发展有限公司 | Trapezoidal correction method and product |
| CN110933391A (en) * | 2019-12-20 | 2020-03-27 | 成都极米科技股份有限公司 | Calibration parameter compensation method and device for projection system and readable storage medium |
| CN112689135A (en) * | 2021-03-19 | 2021-04-20 | 深圳市火乐科技发展有限公司 | Projection correction method, projection correction device, storage medium and electronic equipment |
| CN112804507A (en) * | 2021-03-19 | 2021-05-14 | 深圳市火乐科技发展有限公司 | Projector correction method, projector correction system, storage medium, and electronic device |
| CN112804508A (en) * | 2021-03-19 | 2021-05-14 | 深圳市火乐科技发展有限公司 | Projector correction method, projector correction system, storage medium, and electronic device |
| WO2021120422A1 (en) * | 2019-12-19 | 2021-06-24 | 成都极米科技股份有限公司 | Keystone correction method and apparatus, electronic device and readable storage medium |
| CN113630588A (en) * | 2020-05-08 | 2021-11-09 | 精工爱普生株式会社 | Control method of image projection system and image projection system |
| US20220329765A1 (en) * | 2021-04-08 | 2022-10-13 | Sony Group Corporation | Depth-based projection of image-based content |
| CN115695756A (en) * | 2022-11-01 | 2023-02-03 | 深圳市东明炬创电子股份有限公司 | Geometric correction method and system for projected image |
| CN116033131A (en) * | 2022-12-29 | 2023-04-28 | 深圳创维数字技术有限公司 | Image correction method, device, electronic equipment and readable storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107749979A (en) * | 2017-09-20 | 2018-03-02 | 神画科技（深圳）有限公司 | A kind of projector or so trapezoidal distortion correction method |
| CN108111828A (en) * | 2017-12-12 | 2018-06-01 | 成都市极米科技有限公司 | Projector equipment bearing calibration, device and projector equipment |
| CN108307164A (en) * | 2017-12-30 | 2018-07-20 | 神画科技（深圳）有限公司 | A kind of control method and control system of projecting apparatus |
-
2019
- 2019-05-27 CN CN201910447410.3A patent/CN110099267B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107749979A (en) * | 2017-09-20 | 2018-03-02 | 神画科技（深圳）有限公司 | A kind of projector or so trapezoidal distortion correction method |
| CN108111828A (en) * | 2017-12-12 | 2018-06-01 | 成都市极米科技有限公司 | Projector equipment bearing calibration, device and projector equipment |
| CN108307164A (en) * | 2017-12-30 | 2018-07-20 | 神画科技（深圳）有限公司 | A kind of control method and control system of projecting apparatus |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110769235A (en) * | 2019-09-30 | 2020-02-07 | 深圳市火乐科技发展有限公司 | Trapezoidal correction method and product |
| CN110677634A (en) * | 2019-11-27 | 2020-01-10 | 成都极米科技股份有限公司 | Trapezoidal correction method, device and system for projector and readable storage medium |
| WO2021120422A1 (en) * | 2019-12-19 | 2021-06-24 | 成都极米科技股份有限公司 | Keystone correction method and apparatus, electronic device and readable storage medium |
| CN110933391B (en) * | 2019-12-20 | 2021-11-09 | 成都极米科技股份有限公司 | Calibration parameter compensation method and device for projection system and readable storage medium |
| CN110933391A (en) * | 2019-12-20 | 2020-03-27 | 成都极米科技股份有限公司 | Calibration parameter compensation method and device for projection system and readable storage medium |
| CN113630588B (en) * | 2020-05-08 | 2023-04-21 | 精工爱普生株式会社 | Control method of image projection system and image projection system |
| CN113630588A (en) * | 2020-05-08 | 2021-11-09 | 精工爱普生株式会社 | Control method of image projection system and image projection system |
| CN112804508A (en) * | 2021-03-19 | 2021-05-14 | 深圳市火乐科技发展有限公司 | Projector correction method, projector correction system, storage medium, and electronic device |
| CN112689135A (en) * | 2021-03-19 | 2021-04-20 | 深圳市火乐科技发展有限公司 | Projection correction method, projection correction device, storage medium and electronic equipment |
| CN112804508B (en) * | 2021-03-19 | 2021-08-31 | 深圳市火乐科技发展有限公司 | Projector correction method, projector correction system, storage medium, and electronic device |
| CN112689135B (en) * | 2021-03-19 | 2021-07-02 | 深圳市火乐科技发展有限公司 | Projection correction method, projection correction device, storage medium and electronic equipment |
| CN112804507A (en) * | 2021-03-19 | 2021-05-14 | 深圳市火乐科技发展有限公司 | Projector correction method, projector correction system, storage medium, and electronic device |
| WO2022193560A1 (en) * | 2021-03-19 | 2022-09-22 | 深圳市火乐科技发展有限公司 | Projector correction method and system, and storage medium and electronic device |
| WO2022193559A1 (en) * | 2021-03-19 | 2022-09-22 | 深圳市火乐科技发展有限公司 | Projection correction method and apparatus, storage medium, and electronic device |
| CN112804507B (en) * | 2021-03-19 | 2021-08-31 | 深圳市火乐科技发展有限公司 | Projector correction method, projector correction system, storage medium, and electronic device |
| US11575863B2 (en) * | 2021-04-08 | 2023-02-07 | Sony Group Corporation | Depth-based projection of image-based content |
| US20220329765A1 (en) * | 2021-04-08 | 2022-10-13 | Sony Group Corporation | Depth-based projection of image-based content |
| CN115695756A (en) * | 2022-11-01 | 2023-02-03 | 深圳市东明炬创电子股份有限公司 | Geometric correction method and system for projected image |
| CN115695756B (en) * | 2022-11-01 | 2024-05-14 | 深圳市东明炬创电子股份有限公司 | Geometric correction method and system for projection image |
| CN116033131A (en) * | 2022-12-29 | 2023-04-28 | 深圳创维数字技术有限公司 | Image correction method, device, electronic equipment and readable storage medium |
| CN116033131B (en) * | 2022-12-29 | 2024-05-17 | 深圳创维数字技术有限公司 | Image correction method, device, electronic equipment and readable storage medium |
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