CN103954238A - Method for carrying out bias light compensation on optical fiber interference fringe image based on Gaussian function - Google Patents
Method for carrying out bias light compensation on optical fiber interference fringe image based on Gaussian function Download PDFInfo
- Publication number
- CN103954238A CN103954238A CN201410152884.2A CN201410152884A CN103954238A CN 103954238 A CN103954238 A CN 103954238A CN 201410152884 A CN201410152884 A CN 201410152884A CN 103954238 A CN103954238 A CN 103954238A
- Authority
- CN
- China
- Prior art keywords
- interference fringe
- fringe image
- fiber interference
- gaussian function
- optical fiber
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Landscapes
- Image Processing (AREA)
Abstract
The invention discloses a method for carrying out bias light compensation on an optical fiber interference fringe image based on the Gaussian function. The method mainly overcomes the defect of inconformity of bias light in the interference fringe image in a traditional method and phase distortion of fringes. The implementation process of the method comprises the steps: (1) carrying out grey level transformation on the optical fiber interference fringe image through the single component method, wherein the optical fiber interference fringe image is collected by an area-array camera; (2) extracting a pixel grey level distribution curve in the tangential direction of the central fringe in the interference fringe image; (3) solving a Gaussian function mathematical model of the distribution curve through the curve fitting method; (4) solving a corresponding inverse Gaussian function mathematical model; (5) establishing a two-dimensional inverse Gaussian function transformation surface based on the size of the optical fiber interference fringe image; (6) carrying out compensation on the optical fiber interference fringe image after grey level transformation through the two-dimensional inverse Gaussian function transformation surface. The method has significant application value in the field of small spaces based on optical fiber interference fringe projection and the field of precision measurement of three-dimensional shapes of small-sized objects.
Description
Technical field
The invention belongs to technical field of image processing; Relate to a kind of fiber interference fringe image background light compensation method based on Gaussian function; The fiber interference fringe image based on Young's double pinhole interference that can be used for area array cameras to collect carries out bias light compensation.
Background technology
Along with the development of computer vision and optical measuring technique, the dimensional visual measurement based on active structured light projection becomes current three-dimensional measurement main method, because traditional structured light projection adopts physical grating, digital projector, there is density and the limitation such as resolution is lower in it, thereby developed a kind of method for three-dimensional measurement based on fiber interference fringe projection: utilize the good one-wavelength laser of two bundle dryness, by building fibre optic interferometer to realize the projection of striated structure light figure, with respect to traditional structure light projecting method is high in equal area intra-striate density, cosine is good, thereby be suitable for the accurate measurement to small-size object three-dimensional appearance, fibre-optical probe is small and exquisite flexibly simultaneously, is directed to the complex object surface being positioned under small space or rugged surroundings that is not easy to directly measure and more has advantage, yet while realizing interference fringe structured light projection by building fibre optic interferometer, the light field being radiated to world space from two fiber end faces is Gaussian distribution light beam, the bias light amplitude of the interference fringe therefore generating can not present consistance in projection field range and distribute, thereby on projection screen optical fiber interference fringe pattern by show as bias light amplitude by the feature that reduces gradually in mind-set surrounding expansion process, by the bias light skewness of the view picture fiber interference fringe image that causes collecting by area array cameras, especially in the peripheral regions of image, reduce very fast, make the marginal portion contrast of image not high, the more difficult resolution of details, because the three-dimensional measurement based on striated structure light is realized the three-dimensional appearance of object in its projection visual field is recovered by extraction stripe pattern phase information, the bias light skewness of the stripe pattern of actual acquisition will cause lost part content in fringe phase information extraction process, to the three-dimensional appearance of testee accurately be recovered to band rice difficulty, for image background light compensation deals method, be mainly divided into linearity and non-linear transformation method at present, and after adopting above two kinds of algorithms that bias light amplitude is fiber interference fringe image that Gaussian function distributes and is processed, although enhancing can be expanded in low grey scale pixel value region in image, yet also changed the grey scale pixel value of middle part, picture centre region fractional fringes, in image, bias light situation pockety does not improve, also destroyed the cosine distribution feature of interference fringe simultaneously, therefore finding suitable fiber interference fringe image background light compensation method is difficult point.
Summary of the invention
This method outstanding advantages is the even compensation deals of bias light that can realize the fiber interference fringe image that area array cameras is collected, and avoided subregion in fiber interference fringe image that existing figure image intensifying compensation method causes to exist bias light amplitude too high, too low, and the deficiency such as interference fringe phase distortion; The technical solution used in the present invention is a kind of fiber interference fringe image background light compensation method based on Gaussian function, comprises the following steps:
(1) adopt single component method to by area array cameras actual acquisition to fiber interference fringe image carry out greyscale transformation;
(2) extract by single component method and carry out grey scale pixel value distribution curve in the center striped tangential direction of the fiber interference fringe image after greyscale transformation;
(3) by curve-fitting method, solve the Gaussian function mathematical model of the grey scale pixel value distribution curve obtaining in step (2);
(4) the Gaussian function mathematical model based on obtaining in step (3) solves the anti-Gaussian function mathematical model of its correspondence;
(5) size of the fiber interference fringe image gathering based on area array cameras builds the anti-Gaussian function conversion of two dimension curved surface corresponding to anti-Gaussian function mathematical model being obtained by step (4);
(6) the fiber interference fringe image background light by the conversion curved surface building in step (5), area array cameras being collected after greyscale transformation compensates processing;
In step (1), by weighted average method to actual acquisition to the colored optical fiber interference fringe image based on red, green, blue three primary colours be converted to single grayscale bar print image, its corresponding mathematics conversion formula is expressed as: I=W
ri
r+ W
gi
g+ W
bi
b; Wherein I is a certain locational grey scale pixel value in fiber interference fringe image after greyscale transformation; I
r, I
g, I
bbe respectively the red, green, blue component value in colored optical fiber interference fringe image; W
r, W
g, W
bbe respectively red, green, blue component proportion weights, wherein W in mathematics conversion formula
r+ W
g+ W
b=1; If the ratio weights of a certain component are set to 1, other are set to 0, are single component method;
In step (2), owing to being radiated to from two fiber end faces world space, be Gaussian distribution light beam; Therefore by area array cameras actual acquisition to the fiber interference fringe image after greyscale transformation in the striped tangential direction of center grey scale pixel value high in the middle of being rendered as, the Gaussian function characteristic distributions that two ends are low;
In step (3), the curve-fitting method of utilization based on Gaussian function, the mathematical model of trying to achieve the grey scale pixel value distribution curve in the striped tangential direction of center in the fiber interference fringe image after greyscale transformation being collected by area array cameras is: I*=aexp[-x
2/ c
2]; Wherein I* is the grey scale pixel value on a certain coordinate position in the striped tangential direction of center in fiber interference fringe image; A is fiber interference fringe image background light amplitude constant; X is the pixel coordinate position in fiber interference fringe tangential direction; The waist radius constant of c Gaussian function;
In step (4), solve the anti-Gaussian function model tormulation formula obtaining and be:
the inverse that wherein I** is I*;
In step (5), mathematical model expression formula corresponding to the anti-Gaussian function conversion curved surface of the two dimension of structure is:
wherein K is the transformed value of conversion curved surface; Y is the pixel coordinate position in centre normal direction in fiber interference fringe image;
In step (6), the pointwise that the fiber interference fringe image after greyscale transformation of the anti-Gaussian function of the two dimension of structure conversion curved surface and area array cameras collection is carried out to the respective pixel processing of multiplying each other, can realize the compensation to bias light in image; The present invention compared with prior art tool has the following advantages:
1. the present invention has avoided adopting the skimble-scamble phenomenon of bias light of peripheral regions and central area in the fiber interference fringe image that traditional bias light compensation algorithm causes; After fiber interference fringe image area array cameras being collected by method in the present invention is processed, the bias light amplitude coincidence of view picture fiber interference fringe image is better, thereby lays the foundation for the accurate extraction of follow-up fiber interference fringe phase information;
2. the present invention adopts the respective pixel of the anti-Gaussian function conversion curved surface of the two dimension of trying to achieve and the fiber interference fringe image after greyscale transformation is carried out to the method that pointwise is multiplied each other, striped phase characteristic in interference fringe image is not damaged, and is also the key that follow-up interference fringe phase information is accurately extracted.
Accompanying drawing explanation
Fig. 1 is algorithm flow chart of the present invention;
The result that Fig. 2 carries out greyscale transformation for the fiber interference fringe image that area array cameras is collected; (a) be colored optical fiber interference fringe image; (b) the fiber interference fringe image gray-scale transformation result for obtaining through weighted average method; (c) the fiber interference fringe image gray-scale transformation result for obtaining through single component method;
Fig. 3 is grey scale pixel value distribution curve in center striped tangent line, normal direction in the fiber interference fringe image after greyscale transformation; (a) grey scale pixel value distribution curve in striped tangential direction centered by; (b) grey scale pixel value distribution curve in striped normal direction centered by;
Fig. 4 is two-dimentional anti-Gaussian function conversion curved surface distribution plan;
Fig. 5 is that the fiber interference fringe image background light through greyscale transformation that adopts the inventive method, linear transformation method, non-linear transformation method to collect area array cameras compensates the result after processing; (a) be the fiber interference fringe image after greyscale transformation before not compensation; (b) be the result after linear transform method compensation; (c) result after non-linear transformation method's compensation; (d) result after the inventive method compensation.
Embodiment
As shown in Figure 1, the colored optical fiber interference fringe image first by single component method, area array cameras being collected is converted to gray scale optical fiber interference fringe image to algorithm flow chart of the present invention; Then extract the grey scale pixel value distribution curve in the striped tangential direction of center in fiber interference fringe gray level image; Then the curve-fitting method based on Gaussian function is asked for mathematical model corresponding to grey scale pixel value distribution curve in center striped tangential direction in image; Solve anti-Gaussian function model corresponding to this mathematical model, model is reciprocal; Then the fiber interference fringe picture size collecting according to area array cameras builds two-dimentional anti-Gaussian function conversion curved surface; Finally by this conversion curved surface, the fiber interference fringe image background light after greyscale transformation is compensated to processing, obtain bias light for unified equally distributed fiber interference fringe image.Below in conjunction with accompanying drawing, the specific implementation process of technical solution of the present invention is described in detail.
1. the colored optical fiber interference fringe image that pair area array cameras collects carries out greyscale transformation
The colored optical fiber interference fringe image that the area array cameras of input as shown in Fig. 2 (a) collects, extracts respectively the red, green, blue three primary colours component value in this image, is expressed as I
r, I
g, I
b; And above component value is arranged respectively to different proportion weights W
r, W
g, W
b, wherein require W
r+ W
g+ W
b=1, the transformation for mula according to coloured image in weighted average method to gray level image:
I＝W
RI
R+W
GI
G+W
BI
B
Wherein I is a certain locational grey scale pixel value in fiber interference fringe image after greyscale transformation; Due at W
r=0.299, W
g=0.587, W
bunder=0.114 condition, the rear gray level image of conversion meets the visual characteristic of human eye most, and transformation results is as shown in Fig. 2 (b); Due to the light wavelength lambda=632.8nm of fiber optic interferometric projected light, there is higher red component monochromaticity, therefore when greyscale transformation, only need to consider red component part, be about to W
rbe set to 1, W
g, W
bcomponent is set to 0, and single component method, obtains greyscale transformation result as shown in Fig. 2 (c), and owing to having improved the proportion of red component in greyscale transformation process, the contrast of view picture fiber interference fringe image is higher with respect to Fig. 2 (b);
2. extract grey scale pixel value distribution curve in the striped tangential direction of the interior center of interference fringe image
Owing to being radiated to from two fiber end faces world space as Gaussian distribution light beam, the bias light amplitude of the fiber interference fringe image after the greyscale transformation of single component method with Gaussian function distribution form by mind-set surrounding reduce gradually; The tangential direction and the normal direction grey scale pixel value distribution curve that extract respectively center striped in fiber interference fringe gray level image, after normalization, in the striped tangential direction of center, grey scale pixel value distribution curve is as Fig. 3 (a); In the striped normal direction of center, grey scale pixel value distribution curve is as shown in Fig. 3 (b); Center striped tangential direction grey scale pixel value distribution curve has reflected the bias light amplitude distribution situation of view picture fiber interference fringe gray level image, and it is rendered as the Gaussian function characteristic distributions that centre is high and reduce gradually to two ends;
3. utilize curve-fitting method to solve the Gaussian function mathematical model of distribution curve
Grey scale pixel value distribution curve in the fiber interference fringe image after greyscale transformation collecting due to area array cameras in the striped tangential direction of center is Gaussian function and distributes, therefore by the pixel grey scale Value Data in center striped tangential direction in image, the curve-fitting method of utilization based on Gaussian function solves the Gaussian function mathematical model of grey scale pixel value distribution curve, that is:
I*＝aexp[-x
2／c
2]
Wherein I* is the grey scale pixel value on a certain coordinate position in the striped tangential direction of center in fiber interference fringe image; A is fiber interference fringe image background light amplitude constant; X is the pixel coordinate position in fiber interference fringe tangential direction; The waist radius constant of c Gaussian function;
4. solve anti-Gaussian function mathematical model
Because the fiber interference fringe image background light amplitude collecting by area array cameras is the Gaussian function distribution form that zone line is high and reduce gradually to peripheral regions; For making view picture fiber interference fringe image background light amplitude keep unified, therefore demand solves the anti-Gaussian function mathematical model of its correspondence, be that mathematical model is reciprocal, fiber interference fringe image background light is compensated, make bias light amplitude in view picture interference fringe image be consistance distribution; Due to by having obtained the Gaussian function mathematical model of fiber interference fringe image background light amplitude after greyscale transformation in step 3, its anti-Gaussian function curve mathematic model is:
The inverse that wherein I** is I*;
5. based on fiber interference fringe picture size, build two-dimentional anti-Gaussian function conversion curved surface
The fiber interference fringe image collecting by area array cameras, it after the greyscale transformation of single component method, in computing machine, is two-dimensional mathematics matrix form, therefore build with area array cameras and collect the anti-Gaussian function conversion of the two dimension curved surface that fiber interference fringe picture size is consistent, as shown in Figure 4, the mathematical model of the conversion curved surface of its correspondence is:
Wherein K is the transformed value of conversion curved surface; Y is the pixel coordinate position in centre normal direction in fiber interference fringe image;
Two-dimentional anti-Gaussian function conversion curved surface to greyscale transformation after fiber interference fringe image background light compensate processing
By the pointwise that builds fiber interference fringe image after greyscale transformation that the anti-Gaussian function conversion curved surface of complete two dimension and area array cameras collect and the carry out respective pixel processing of multiplying each other, to eliminate the Gaussian function distribution characteristics of fiber interference fringe image background light amplitude, Fig. 5 is that the fiber interference fringe image background light through greyscale transformation that adopts the inventive method, linear transformation method, non-linear transformation method to collect area array cameras compensates the result after processing; Fig. 5 (a) is the fiber interference fringe image after greyscale transformation before not compensation; Fig. 5 (b) is the result after linear transform method compensation; The result of Fig. 5 (c) after non-linear transformation method's compensation; The result of Fig. 5 (d) after the inventive method compensation, the realization of visible employing the inventive method the uniformity compensation of view picture fiber interference fringe image background light is processed, thereby accurately accurately extracting and laying a good foundation for follow-up fiber interference fringe phase information.
Claims (1)
1. the fiber interference fringe image background light compensation method based on Gaussian function, comprises the following steps:
(1) adopt single component method to by area array cameras actual acquisition to fiber interference fringe image carry out greyscale transformation;
(2) extract by single component method and carry out grey scale pixel value distribution curve in the center striped tangential direction of the fiber interference fringe image after greyscale transformation;
(3) by curve-fitting method, solve the Gaussian function mathematical model of the grey scale pixel value distribution curve obtaining in step (2);
(4) the Gaussian function mathematical model based on obtaining in step (3) solves the anti-Gaussian function mathematical model of its correspondence;
(5) size of the fiber interference fringe image gathering based on area array cameras builds the anti-Gaussian function conversion of two dimension curved surface corresponding to anti-Gaussian function mathematical model being obtained by step (4);
(6) the fiber interference fringe image background light by the conversion curved surface building in step (5), area array cameras being collected after greyscale transformation compensates processing;
In step (1), by weighted average method to actual acquisition to the colored optical fiber interference fringe image based on red, green, blue three primary colours be converted to single grayscale bar print image, its corresponding mathematics conversion formula is expressed as: I=W
ri
r+ W
gi
g+ W
bi
b; Wherein I is a certain locational grey scale pixel value in fiber interference fringe image after greyscale transformation; I
r, I
g, I
bbe respectively the red, green, blue component value in colored optical fiber interference fringe image; W
r, W
g, W
bbe respectively red, green, blue component proportion weights, wherein W in mathematics conversion formula
r+ W
g+ W
b=1; If the ratio weights of a certain component are set to 1, other are set to 0, are single component method;
In step (2), owing to being radiated to from two fiber end faces world space, be Gaussian distribution light beam; Therefore by area array cameras actual acquisition to the fiber interference fringe image after greyscale transformation in the striped tangential direction of center grey scale pixel value high in the middle of being rendered as, the Gaussian function characteristic distributions that two ends are low;
In step (3), the curve-fitting method of utilization based on Gaussian function, the mathematical model of trying to achieve the grey scale pixel value distribution curve in the striped tangential direction of center in the fiber interference fringe image after greyscale transformation being collected by area array cameras is: I*=aexp[-x
2/ c
2]; Wherein I* is the grey scale pixel value on a certain coordinate position in the striped tangential direction of center in fiber interference fringe image; A is fiber interference fringe image background light amplitude constant; X is the pixel coordinate position in fiber interference fringe tangential direction; The waist radius constant of c Gaussian function;
In step (4), solve the anti-Gaussian function model tormulation formula obtaining and be:
the inverse that wherein I** is I*;
In step (5), mathematical model expression formula corresponding to the anti-Gaussian function conversion curved surface of the two dimension of structure is:
wherein K is the transformed value of conversion curved surface; Y is the pixel coordinate position in centre normal direction in fiber interference fringe image;
In step (6), the pointwise that the fiber interference fringe image after greyscale transformation of the anti-Gaussian function of the two dimension of structure conversion curved surface and area array cameras collection is carried out to the respective pixel processing of multiplying each other, can realize the compensation to bias light in image.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410152884.2A CN103954238B (en) | 2014-04-17 | 2014-04-17 | A kind of fiber interference fringe image background light compensation method based on Gaussian function |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410152884.2A CN103954238B (en) | 2014-04-17 | 2014-04-17 | A kind of fiber interference fringe image background light compensation method based on Gaussian function |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103954238A true CN103954238A (en) | 2014-07-30 |
| CN103954238B CN103954238B (en) | 2016-07-06 |
Family
ID=51331548
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410152884.2A Expired - Fee Related CN103954238B (en) | 2014-04-17 | 2014-04-17 | A kind of fiber interference fringe image background light compensation method based on Gaussian function |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103954238B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110081817A (en) * | 2019-04-24 | 2019-08-02 | 杭州光粒科技有限公司 | Eliminate method, apparatus, computer equipment and the storage medium of bias light |
| CN110230996A (en) * | 2019-05-30 | 2019-09-13 | 西安理工大学 | Three dimension profile measurement method based on the sparse S-transformation fast frequency-domain solution phase of two dimension |
| CN111079893A (en) * | 2019-11-05 | 2020-04-28 | 深圳大学 | Method and device for obtaining generator network for interference fringe pattern filtering |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011226871A (en) * | 2010-04-18 | 2011-11-10 | Utsunomiya Univ | Shape measurement method, shape measurement device, distortion measurement method, and distortion measurement device |
| CN102679908A (en) * | 2012-05-10 | 2012-09-19 | 天津大学 | Dynamic measurement method of three-dimensional shape projected by dual-wavelength fiber interference fringe |
| CN103528542A (en) * | 2013-10-08 | 2014-01-22 | 天津大学 | Real-time three-dimensional shape measurement system based on internal modulation optical fiber interference fringe projection |
-
2014
- 2014-04-17 CN CN201410152884.2A patent/CN103954238B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011226871A (en) * | 2010-04-18 | 2011-11-10 | Utsunomiya Univ | Shape measurement method, shape measurement device, distortion measurement method, and distortion measurement device |
| CN102679908A (en) * | 2012-05-10 | 2012-09-19 | 天津大学 | Dynamic measurement method of three-dimensional shape projected by dual-wavelength fiber interference fringe |
| CN103528542A (en) * | 2013-10-08 | 2014-01-22 | 天津大学 | Real-time three-dimensional shape measurement system based on internal modulation optical fiber interference fringe projection |
Non-Patent Citations (1)
| Title |
|---|
| 刘德连等: "背景高斯化的遥感图像目标检测", 《光学学报》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110081817A (en) * | 2019-04-24 | 2019-08-02 | 杭州光粒科技有限公司 | Eliminate method, apparatus, computer equipment and the storage medium of bias light |
| CN110230996A (en) * | 2019-05-30 | 2019-09-13 | 西安理工大学 | Three dimension profile measurement method based on the sparse S-transformation fast frequency-domain solution phase of two dimension |
| CN110230996B (en) * | 2019-05-30 | 2020-10-27 | 西安理工大学 | Three-dimensional surface shape measuring method based on two-dimensional sparse S-transform rapid frequency domain dephasing |
| CN111079893A (en) * | 2019-11-05 | 2020-04-28 | 深圳大学 | Method and device for obtaining generator network for interference fringe pattern filtering |
| CN111079893B (en) * | 2019-11-05 | 2023-05-09 | 深圳大学 | Acquisition method and device for generator network for interference fringe pattern filtering |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103954238B (en) | 2016-07-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN100489446C (en) | Method for measuring three-dimensional contour based on phase method | |
| CN103530880B (en) | Based on the camera marking method of projection Gaussian network pattern | |
| CN102564347B (en) | Object three-dimensional outline measuring device and method based on Dammann grating | |
| Huang et al. | 3D fingerprint imaging system based on full-field fringe projection profilometry | |
| Guo et al. | A fast reconstruction method for three-dimensional shape measurement using dual-frequency grating projection and phase-to-height lookup table | |
| Sun et al. | A vision measurement model of laser displacement sensor and its calibration method | |
| CN103292740B (en) | A kind of 3-D scanning instrument measurement method and device thereof | |
| Song et al. | A new phase unwrapping algorithm based on three wavelength phase shift profilometry method | |
| CN105066906B (en) | A kind of quick high dynamic range method for three-dimensional measurement | |
| CN102607455B (en) | Three-dimensional measurement method for microstructure based on optical microscope and variable illumination | |
| CN104376558A (en) | Cuboid-based intrinsic parameter calibration method for Kinect depth camera | |
| CN103292733B (en) | A kind of corresponding point lookup method based on phase shift and trifocal tensor | |
| CN103729849A (en) | Method for calculating digital image morphing initial value | |
| CN112945141B (en) | Structured light rapid imaging method and system based on micro-lens array | |
| CN109029739A (en) | A kind of fiber interference fringe image phase extracting method based on Hilbert transform | |
| CN102243103A (en) | Method for quickly measuring colors and three-dimensional profile of object | |
| Zhang et al. | Robust color and shape measurement of full color artifacts by RGB fringe projection | |
| CN108303038A (en) | Reflection-type surface shape measurement method and device based on two-dimension optical dot matrix | |
| Miao et al. | Surface profile and stress field evaluation using digital gradient sensing method | |
| CN103954238A (en) | Method for carrying out bias light compensation on optical fiber interference fringe image based on Gaussian function | |
| Yang et al. | A dual-platform laser scanner for 3D reconstruction of dental pieces | |
| CN102865810B (en) | Orthogonal double-grating based detecting device for synchronous phase shift common-light path interference and detecting method therefor | |
| CN102831414B (en) | Method and device for non-contact collection of three-dimensional palm print and hand shape | |
| CN205718873U (en) | A kind of double frequency phase shift tripleplane measuring instrument | |
| CN104318234A (en) | Three-dimensional extraction method of human face wrinkles shown in point cloud data and device thereof |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20160706 Termination date: 20200417 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |