CN113436207B - Method for rapidly and accurately extracting line structure light stripe center of regular surface - Google Patents
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Abstract
A method for quickly and accurately extracting the line structure light stripe center of a regular surface includes such steps as extracting the ROI of an image, processing the image in the ROI, calculating the normal direction of the line structure light stripe by using principal component analysis, threshold segmentation and binarization, extracting the line structure light stripe outline to form two approximately parallel outline lines, and extracting the middle point in the normal direction between two outline lines as the line structure light stripe center. The ROI of the image is extracted, so that the image area to be processed is reduced; the Principal Component Analysis (PCA) is used for solving the normal line of the stripe, the Hessian matrix in the Steger algorithm is replaced, the calculation complexity is reduced, the traditional method is replaced by a method for solving the midpoint between the contours in the normal direction, the calculated amount is greatly reduced aiming at the linear structure light stripe on the regular surface, and the real-time accurate effect can be achieved.
Description
Technical Field
The invention belongs to the field of image processing, and particularly relates to a method for rapidly and accurately extracting a line structure light stripe center of a regular surface.
Background
The line structured light measurement method plays an increasingly important role in a plurality of fields along with the development of the measurement technology, and has a very important role in the fields of surface measurement detection, medical diagnosis, three-dimensional reconstruction, industrial automation and the like. In the rod piece welding line system, the extraction of the welding line center line is required to take the extraction of the line structure light stripe center as a precondition, and the accuracy and the real-time performance of the line structure light stripe center extraction play a key role in the performance of the welding line identification and tracking system. There are various extraction methods of the fringe center, and the conventional methods include gaussian approximation, linear interpolation, parabolic estimation, gravity center method, etc., and the extraction accuracy of these methods is not very high. The applicability to industrial measurement requiring high accuracy and real-time is not high.
The common extraction method of the structured light stripe center is a threshold value method, a gray level gravity center method, a curve fitting method and a Hessian (Hessian) matrix method. The thresholding method is a skeleton extraction method, and has high speed but poor positioning accuracy. Liu and the like can accurately acquire the pixel coordinates of the center of the structural light stripe by adopting a gray level gravity center method, and the method is relatively suitable for light stripes with smaller light stripe dispersibility; zhang Xiaoyan, and the like, proposes an improved gray-scale gravity center method, wherein the binarization is utilized to process the light band before the gray-scale gravity center method is adopted, so that the influence of the uneven width of the light band on the extraction result is reduced, and the extraction precision is higher. According to the characteristics of the gray level of the light section of the line structure, liu Tao and the like, an improved curve fitting method is proposed, but the extraction speed is slower; liu Zhen and the like, extracting the initial light stripe center by using a cross-correlation maximum value before curve fitting, and accurately positioning the stripe center by using a curve fitting method, wherein the method has strong interference capability and good robustness, but the calculated amount of curve fitting is still larger; jiang Yongfu and the like apply curve fitting in light bars with broken line defects, improve the light bar defects, but the extraction accuracy can only reach the pixel level. Steger obtains the normal direction of the center of a line structure light stripe by using a Hessian matrix, and performs second-order Taylor expansion on the gray distribution function of each pixel of the structure light stripe along the normal direction to obtain the center point of the stripe; li Taotao and the like splice small-view images acquired by a plurality of cameras by utilizing an overlapping view field (OFOV) image, so as to obtain the center pixel coordinate of the light bar, and the method has higher cost; cai Huaiyu and the like, on the basis, the Principal Component Analysis (PCA) is used for solving the normal line of the stripe, the Hessian matrix in the Steger algorithm is replaced, and the calculation complexity is reduced.
The populus euphratica in a linear structured light extraction method proposes an extraction method combining a principal component analysis method and a gray level gravity center method, carries out Gaussian convolution on an image and initially extracts effective light stripe information in the image by using a threshold segmentation method; calculating gradient distribution of the light stripe image and selecting a point with zero amplitude as an initial point; obtaining the normal direction of the point by PCA; and taking two points with the largest amplitude values as boundary points on two sides of the initial point along the normal direction, solving the center of the light stripe in the boundary by using a gray level gravity center method, determining the next initial point, and extracting the center of the light stripe through iteration. The method combines the principal component analysis method and the gray level gravity center method, has the accuracy smaller than 0.05 pixel compared with the Steger method, does not improve the accuracy effect, has no obvious advantage relative to the processing time, and is insensitive to the surface of an object with stronger optical property.
Disclosure of Invention
The invention provides a method for rapidly and accurately extracting line structure light stripe centers on a regular surface, which comprises the steps of firstly extracting an ROI of an image, processing the image in the ROI, calculating the normal direction of the line structure light stripe by using a principal component analysis method, then carrying out threshold segmentation and binarization processing on the image, extracting the line structure light stripe outlines to form two approximately parallel outline lines, and extracting a middle point in the normal direction between the two outline lines as the line structure light stripe centers.
A method for rapidly and accurately extracting the center of a linear structured light stripe on a regular surface comprises the following steps:
step 1, extracting an initial point; extracting a region of interest (ROI) in the line structured light image by using an adaptive threshold method;
step 2, calculating the normal line and tangential direction of the initial point; calculating a covariance matrix according to gradient vectors of pixels in the initial point field, and obtaining a normal direction and a tangential direction of a light stripe by solving eigenvectors of the covariance matrix according to a principal component analysis method;
step 3, binarization processing; selecting 255 pixels to perform threshold segmentation to obtain a processed binarized image;
step 4, extracting the outline; deleting redundant points by a hollow internal point method to obtain a contour line of the strip line structured light;
step 5, extracting the center of the linear structured light stripe; and calculating a linear function, an intersection contour and an intersection contour of the initial point in the normal direction according to the normal direction, and then repeating the operation on the next point of the initial point in the tangential direction to finally obtain the complete structured light stripe center.
Further, in step 1, the image f (x, y) is convolved with the Gaussian function g (x, y), i.e.To reduce the effect of image noise points; calculating the gray gradient (G) x ,G y ) And magnitude |g (x, y) |, the calculation process is:
within the ROI of the extracted line structured light, M i (i=1, 2, 3.) represents the i-th row in the light bar, and the point P with zero amplitude is searched row by row 0 (i 0 ,j 0 ) This point is taken as an initial point.
Further, in step 2, the selected domain size is W, and a covariance matrix C is established:
solving a eigenvector v corresponding to the eigenvalue lambda of the matrix:
the feature vector v corresponding to λ is the normal direction of the initial point.
In step 4, if one point in the original image is black and all 8 adjacent points are black, deleting the point, and finally obtaining the contour line of the line structure light.
Further, in step 5, let the normal direction v= (a, b) calculated above be set for the initial point P 0 (i 0 ,j 0 ) Calculating the linear function in the normal direction of the initial point as The intersection outline is P 1 (i 1 ,j 1 ) The intersection contour is P 2 (i 2 ,j 2 ) The center of the reserved structural light stripe is as follows: />Then for P 0 (i 0 ,j 0 ) The above operation is repeated at the next point in the tangential direction, thereby obtaining the complete structured-light stripe center.
The beneficial effects of the invention are as follows:
the ROI of the image is extracted, so that the image area to be processed is reduced; the Principal Component Analysis (PCA) is used for solving the normal line of the stripe, the Hessian matrix in the Steger algorithm is replaced, the calculation complexity is reduced, the traditional method is replaced by a method for solving the midpoint between the contours in the normal direction, the calculated amount is greatly reduced aiming at the linear structure light stripe on the regular surface, and the real-time accurate effect can be achieved.
Drawings
Fig. 1 is a flowchart of steps of a method for rapidly and accurately extracting a line structured light stripe center according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further described in detail below with reference to the attached drawings.
The invention provides a method for rapidly and accurately extracting line structure light stripe centers on a regular surface, which comprises the steps of firstly extracting an ROI of an image, processing the image in the ROI, calculating the normal direction of the line structure light stripe by using a principal component analysis method, then carrying out threshold segmentation and binarization processing on the image, extracting the line structure light stripe outlines to form two approximately parallel outline lines, and extracting a middle point in the normal direction between the two outline lines as the line structure light stripe centers.
The following basic theory and definition are presented first:
ROI: ROI (region ofinterest), a region of interest. In machine vision and image processing, a region to be processed, called a region of interest, ROI, is outlined from a processed image in the form of a square, a circle, an ellipse, an irregular polygon, or the like.
Principal Component Analysis (PCA): PCA (Principal ComponentAnalysis), the principal component analysis method, is one of the most widely used data dimension reduction algorithms. The main idea of PCA is to map n-dimensional features onto k-dimensions, which are completely new orthogonal features, also called principal components, and are k-dimensional features reconstructed on the basis of the original n-dimensional features. PCA works by sequentially finding a set of mutually orthogonal axes from the original space, the selection of which is closely related to the data itself. The first new coordinate axis is selected to be the direction with the maximum variance in the original data, the second new coordinate axis is selected to be the plane orthogonal to the first coordinate axis so as to make the variance maximum, and the third axis is selected to be the plane orthogonal to the 1 st and 2 nd axes so as to make the variance maximum. By analogy, n such coordinate axes may be obtained. The new axes obtained in this way have a majority of the variances contained in the first k axes, and the latter axes have a variance of almost 0. Thus, the remaining axes can be ignored, leaving only the first k axes with the vast majority of variances. In fact, this amounts to retaining only dimensional features containing a substantial portion of variance, while ignoring feature dimensions containing variances of almost 0, achieving dimension reduction of the data features.
The method in the embodiment of the invention specifically comprises the following steps:
and 1, extracting an initial point.
The structured light stripe image has high contrast and strong light stripe directivity. The region of interest (ROI) in the line structured light image is extracted by using the self-adaptive threshold method, so that the influence of noise of a measured object on the extraction of the subsequent stripe center can be effectively reduced, and the extraction speed of the stripe center is greatly improved.
Convolving the image f (x, y) with a gaussian function g (x, y), i.e To reduce the effect of image noise points. Calculating the gray gradient G of an image x ,G y ) And magnitude |g (x, y) |, the calculation process is:
within the ROI of the extracted line structured light, M i (i=1, 2, 3.) represents the i-th row in the light bar, and the point P with zero amplitude is searched row by row 0 (i 0 ,j 0 ) This point is taken as an initial point.
And 2, calculating the normal line and tangential direction of the initial point.
And calculating a covariance matrix according to the gradient vector of the pixels in the initial point field, and obtaining the normal direction and the tangential direction of the light fringes by solving the eigenvectors of the covariance matrix according to a principal component analysis method. The size of the selected field is W, and a covariance matrix C is established:
solving eigenvalues lambda of matrix 1 、λ 2 And corresponding feature vector v 1 、v 2 ：
Wherein, the eigenvector corresponding to the eigenvalue with large absolute value is the normal direction of the initial point, and lambda can be known from the above formula 1 >λ 2 Therefore lambda is 1 Corresponding feature vector v 1 Is the normal direction of the initial point lambda 2 Corresponding feature vector v 2 Is the tangential direction of the initial point.
And 3, binarizing.
As the red line laser is adopted to irradiate the surface of the workpiece, the pixels are selected as 255 for threshold segmentation, and the processed binarized image is obtained.
And 4, extracting the outline.
The method of taking out the internal points is adopted: if one point in the original image is black and all 8 adjacent points are black, deleting the point and carrying out line structured light contour lines.
And 5, extracting the center of the linear structured light stripe.
Let the normal direction v calculated above 1 = (a, b), for the initial point P 0 (i 0 ,j 0 ) Calculating the linear function in the normal direction of the initial point asThe intersection outline is P 1 (i 1 ,j 1 ) The intersection contour is P 2 (i 2 ,j 2 ) The center of the reserved structural light stripe is as follows: />
The above description is merely of preferred embodiments of the present invention, and the scope of the present invention is not limited to the above embodiments, but all equivalent modifications or variations according to the present disclosure will be within the scope of the claims.
Claims (4)
1. A method for rapidly and accurately extracting the center of a linear structured light stripe on a regular surface is characterized by comprising the following steps: the method comprises the following steps:
step 1, extracting an initial point; extracting a region of interest (ROI) in the line structured light image by using an adaptive threshold method;
step 2, calculating the normal direction of the initial point; calculating a covariance matrix according to the gradient vector of the pixels in the initial point field, and obtaining the normal direction of the light stripes by solving the eigenvector of the covariance matrix according to a principal component analysis method;
step 3, binarization processing; selecting 255 pixels to perform threshold segmentation to obtain a processed binarized image;
step 4, extracting the outline; deleting redundant points by a hollow internal point method to obtain a contour line of the strip line structured light;
step 5, extracting the center of the linear structured light stripe; calculating a linear function, an intersection contour and an intersection contour of the initial point in the normal direction according to the normal direction, and then repeating the operation on the next point of the initial point in the tangential direction to finally obtain the complete structured light stripe center;
in step 5, let the normal direction v= (a, b) calculated above be set for the initial point P 0 (i 0 ,j 0 ) Calculating the linear function in the normal direction of the initial point asThe intersection outline is P 1 (i 1 ,j 1 ) The intersection contour is P 2 (i 2 ,j 2 ) The center of the reserved structural light stripe is as follows: />Then for P 0 (i 0 ,j 0 ) Repeating the above operation at the next point in the tangential direction to finally obtain the complete structured light stripe center.
2. The method for rapidly and accurately extracting the center of the linear structured light stripe on the regular surface according to claim 1, wherein the method comprises the following steps: in step 1, the image f (x, y) is convolved with the Gaussian function g (x, y), i.eTo reduce the effect of image noise points; calculating the gray gradient (G) x ,G y ) And magnitude |g (x, y) |, the calculation process is:
within the ROI of the extracted line structured light, M i (i=1, 2, 3.) represents the i-th row in the light bar, and the point P with zero amplitude is searched row by row 0 (i 0 ,j 0 ) This point is taken as an initial point.
3. The method for rapidly and accurately extracting the center of the linear structured light stripe on the regular surface according to claim 1, wherein the method comprises the following steps: in step 2, the selected domain size is W, and a covariance matrix C is established:
solving a eigenvector v corresponding to the eigenvalue lambda of the matrix:
the feature vector v corresponding to λ is the normal direction of the initial point.
4. The method for rapidly and accurately extracting the center of the linear structured light stripe on the regular surface according to claim 1, wherein the method comprises the following steps: in step 4, if one point in the original image is black and all 8 adjacent points are black, deleting the point, and finally obtaining the contour line of the line structure light.
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| CN113989363A (en) * | 2021-11-25 | 2022-01-28 | 江苏科技大学 | Linear regression-based line structure striation center extraction method |
| CN114763699B (en) * | 2022-05-23 | 2022-11-08 | 中建四局安装工程有限公司 | Embedded bolt positioning method, embedded bolt auxiliary fixing device and using method thereof |
| CN115953459B (en) * | 2023-03-10 | 2023-07-25 | 齐鲁工业大学(山东省科学院) | Method for extracting central line of laser stripe under complex illumination condition |
| CN116433707B (en) * | 2023-06-14 | 2023-08-11 | 武汉工程大学 | Accurate extraction method and system for optical center sub-pixels of line structure under complex background |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003035514A (en) * | 2001-07-19 | 2003-02-07 | Fuji Photo Optical Co Ltd | Area extraction method for fringe analysis |
| CN101770641A (en) * | 2008-12-26 | 2010-07-07 | 中国科学院沈阳自动化研究所 | Rapid extracting method for structure light welding seam image characteristic points |
| CN104568978A (en) * | 2013-10-22 | 2015-04-29 | 镇江胡氏光电科技有限公司 | Lamp holder optical head defect detection method |
| CN106897724A (en) * | 2015-12-18 | 2017-06-27 | 南京财经大学 | A kind of plant leaf identification method based on contour line shape facility |
| CN106982357A (en) * | 2017-04-11 | 2017-07-25 | 广州市奥威亚电子科技有限公司 | A kind of intelligent camera system based on distribution clouds |
| CN110044292A (en) * | 2018-01-16 | 2019-07-23 | 郑州宇通客车股份有限公司 | A kind of method for three-dimensional measurement and system based on line-structured light |
| CN110111424A (en) * | 2019-05-07 | 2019-08-09 | 易思维（杭州）科技有限公司 | The three-dimensional rebuilding method of arc-shaped object based on line-structured light measurement |
| CN112629409A (en) * | 2020-11-30 | 2021-04-09 | 江苏科技大学 | Method for extracting line structure light stripe center |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109226967B (en) * | 2018-07-25 | 2021-03-09 | 同高先进制造科技（太仓）有限公司 | Active laser vision steady weld joint tracking system for laser-arc hybrid welding |
-
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Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003035514A (en) * | 2001-07-19 | 2003-02-07 | Fuji Photo Optical Co Ltd | Area extraction method for fringe analysis |
| CN101770641A (en) * | 2008-12-26 | 2010-07-07 | 中国科学院沈阳自动化研究所 | Rapid extracting method for structure light welding seam image characteristic points |
| CN104568978A (en) * | 2013-10-22 | 2015-04-29 | 镇江胡氏光电科技有限公司 | Lamp holder optical head defect detection method |
| CN106897724A (en) * | 2015-12-18 | 2017-06-27 | 南京财经大学 | A kind of plant leaf identification method based on contour line shape facility |
| CN106982357A (en) * | 2017-04-11 | 2017-07-25 | 广州市奥威亚电子科技有限公司 | A kind of intelligent camera system based on distribution clouds |
| CN110044292A (en) * | 2018-01-16 | 2019-07-23 | 郑州宇通客车股份有限公司 | A kind of method for three-dimensional measurement and system based on line-structured light |
| CN110111424A (en) * | 2019-05-07 | 2019-08-09 | 易思维（杭州）科技有限公司 | The three-dimensional rebuilding method of arc-shaped object based on line-structured light measurement |
| CN112629409A (en) * | 2020-11-30 | 2021-04-09 | 江苏科技大学 | Method for extracting line structure light stripe center |
Non-Patent Citations (1)
| Title |
|---|
| 线结构光条纹中心提取方法;胡杨、方素平;《激光与光电子学进展》;第196-200页 * |
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