CN113436207A - Method for quickly 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 centers of linear structured light stripes on a regular surface comprises the steps of extracting an ROI of an image, processing the image in the ROI, calculating the normal direction of the linear structured light stripes by using a principal component analysis method, then carrying out threshold segmentation and binarization processing on the image, extracting the outline of the linear structured light stripes to form two approximately parallel contour lines, and extracting a middle point in the normal direction between the two contour lines to be used as the center of the linear structured light stripes. The ROI of the image is extracted, so that the area of the image to be processed is reduced; the stripe normal is solved by Principal Component Analysis (PCA), a Hessian matrix in a 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 stripe of the regular surface, and the real-time and accurate effect can be achieved.
Description
Technical Field
The invention belongs to the field of image processing, and particularly relates to a method for quickly and accurately extracting the centers of linear striations of a line structure on a regular surface.
Background
The line structured light measurement method plays an increasingly important role in many fields along with the development of the measurement technology, and has a very important position 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 central line needs to be based on the extraction of the center of the linear structure light stripe, and the accuracy and the real-time performance of the extraction of the center of the linear structure light stripe play a key role in the performance of the welding line identification and tracking system. There are many extraction methods for the fringe center, and the traditional methods include gaussian approximation, linear interpolation, parabolic estimation, gravity center, etc., and the extraction accuracy of these methods is not very high. The applicability to industrial measurements requiring high accuracy and real-time is not high.
Common methods for extracting the centers of the structured light fringes include a threshold value method, a gray scale gravity center method, a curve fitting method and a Hessian matrix method. The threshold value method is a framework extraction method, and has high speed but poor positioning accuracy. Liu bin and the like adopt a gray scale gravity center method, the pixel coordinates of the centers of the texture of the structured light can be accurately acquired, and the method is more suitable for light bars with smaller light band dispersity; an improved gray scale gravity center method is provided by Zhaobayan and the like, a light band is processed by binarization before the gray scale gravity center method is adopted, the influence of non-uniform width of the light band on an extraction result is reduced, and the extraction precision is higher. According to the characteristics of the gray scale of the light section of the line structure, an improved curve fitting method is provided for Liutao and the like, but the extraction speed is low; based on the Liu Ji and the like, the initial light stripe center is extracted by the maximum value of the cross correlation before curve fitting, and then the stripe center is accurately positioned by a curve fitting method, so that the method has strong interference capability and good robustness, but the calculated amount of curve fitting is still large; the line fitting is applied in the light bars with broken line defects, such as Jiangyanpay, and the like, so that the light bar defects are improved, but the extraction precision can only reach the pixel level. Steger utilizes Hessian matrix to obtain the normal direction of the center of the line-structured light stripe, and carries out second-order Taylor expansion on the gray distribution function of each point pixel of the structured light stripe along the normal direction to obtain the central point of the stripe, the method has high precision, but at least 5 times of Gaussian convolution operation is required, the calculated amount is large, and the real-time performance is poor; the small field images acquired by a plurality of cameras are spliced by utilizing overlapped field of view (OFOV) images, such as the billows and the like, so that the pixel coordinates of the centers of light bars are acquired, and the method is high in cost; on the basis, the Chua Huayu and the like use Principal Component Analysis (PCA) to obtain the normal line of the stripe to replace a Hessian matrix in a Steger algorithm, so that the complexity of calculation is reduced.
Populus diversifolia proposes an extraction method combining a principal component analysis method and a gray scale gravity center method in a linear structured light extraction method, and performs Gaussian convolution on an image and preliminarily extracts effective striation information in the image by using a threshold segmentation method; calculating the gradient distribution and amplitude of the light stripe image, and selecting a point with zero amplitude as an initial point; obtaining the normal direction of the point by utilizing PCA; two points with the maximum amplitude values on two sides of the initial point along the normal direction are used as boundary points, the light stripe centers in the boundary are calculated by utilizing a gray scale gravity center method, the next initial point is determined, and the light stripe centers are extracted through iteration. The method combines a principal component analysis method with a gray scale gravity center method, the precision is less than 0.05 pixel compared with a Steger method, the precision effect is not improved, the processing time has no obvious advantage, and the method is insensitive to the surface of an object with stronger optical property.
Disclosure of Invention
The invention provides a method for quickly and accurately extracting the centers of linear structured stripes on a regular surface.
A method for quickly and accurately extracting the centers of linear striations of a line structure 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 a self-adaptive threshold method;
step 2, calculating the normal and the tangential direction of the initial point; calculating a covariance matrix from gradient vectors of pixels in the initial point field, and obtaining a normal direction and a tangential direction of the light striations by solving eigenvectors of the covariance matrix through a principal component analysis method;
step 3, binarization processing; selecting 255 pixels for threshold segmentation to obtain a processed binary image;
step 4, contour extraction; deleting redundant points by a method of hollowing out internal points to obtain a contour line of the structured light with lines;
step 5, extracting the centers of the linear striations of the line structure; and calculating a linear function of the initial point in the normal direction, an upper crossing profile and a lower crossing profile 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 structural light stripe center.
Further, in step 1, the image f (x, y) is convolved with the gaussian function g (x, y), i.e. the image f (x, y) is convolved with the gaussian function g (x, y)
To reduce the influence of image noise points; calculating the gray gradient (G) of the imagex,Gy) And the magnitude | G (x, y) |, the calculation process is:
on the extracted lineWithin ROI of structured light, Mi(i 1,2, 3.) denotes the ith row of the light bar, and the row-by-row search is performed for a point P with zero amplitude0(i0,j0) This point is taken as the initial point.
Further, in step 2, the selected field size is W, and a covariance matrix C is established:
solving an eigenvector v corresponding to the eigenvalue lambda of the matrix:
and the characteristic vector v corresponding to the lambda is the normal direction of the initial point.
Further, in step 4, the method of hollowing out the internal points is adopted, if a point in the original image is black and all 8 adjacent points are black, the point is deleted, and finally the contour line of the line structured light is obtained.
Further, in step 5, the normal direction v calculated as above is set to (a, b), and the initial point P is set to0(i0,j0) Calculating a linear function in the normal direction through the initial point as
The upper contour is P1(i1,j1) The cross-over profile is P2(i2,j2) The remaining centers of the structured light stripes are:
then for P0(i0,j0) The above operation is repeated at the next point in the tangential direction, resulting in a complete structured light stripe center.
The invention has the beneficial effects that:
the ROI of the image is extracted, so that the area of the image to be processed is reduced; the stripe normal is solved by Principal Component Analysis (PCA), a Hessian matrix in a 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 stripe of the regular surface, and the real-time and accurate effect can be achieved.
Drawings
Fig. 1 is a flowchart illustrating steps of a method for fast and accurately extracting the centers of linear striations of the line structure according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
The invention provides a method for quickly and accurately extracting the centers of linear structured stripes on a regular surface.
The following basic theories and definitions are first proposed:
ROI: roi, (region of interest). In machine vision and image processing, a region to be processed, called a region of interest, ROI, is delineated from a processed image in the form of a box, circle, ellipse, irregular polygon, or the like.
Principal Component Analysis (PCA): pca (principal component analysis), a 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 k-dimensional features reconstructed on the basis of the original n-dimensional features. The task of PCA is to sequentially find a set of mutually orthogonal axes from the original space, the selection of new axes being strongly dependent on the data itself. The first new coordinate axis is selected to be the direction with the largest square difference in the original data, the second new coordinate axis is selected to be the plane which is orthogonal to the first coordinate axis and enables the square difference to be the largest, and the third axis is the plane which is orthogonal to the 1 st axis and the 2 nd axis and enables the square difference to be the largest. By analogy, n such coordinate axes can be obtained. In the new coordinate axes obtained in this way, most of the variances are contained in the preceding k coordinate axes, and the variance contained in the following coordinate axes is almost 0. The remaining axes can then be ignored, leaving only the first k axes with the most variance. In fact, this is equivalent to only retaining the dimension feature containing most of the variance, and neglecting the feature dimension containing the variance of almost 0, so as to implement the dimension reduction processing on the data feature.
The method in the embodiment of the invention specifically comprises the following steps:
step 1, extracting an initial point.
The structured light stripe has high image contrast and strong light stripe directivity. A region of interest (ROI) in the line structured light image is extracted by using a self-adaptive threshold method, so that the influence of noise of a measured object on the subsequent extraction of the center of the stripe can be effectively reduced, and the extraction speed of the center of the stripe 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 a gray gradient G of an imagex,Gy) And the magnitude | G (x, y) |, the calculation process is:
within ROI of the extracted line structured light, Mi(i 1,2, 3.) denotes the ith row of the light bar, and the row-by-row search is performed for a point P with zero amplitude0(i0,j0) This point is taken as the initial point.
And 2, calculating the normal and the tangential direction of the initial point.
And calculating a covariance matrix from gradient vectors of pixels in the initial point field, and obtaining the normal direction and the tangential direction of the light striations by solving eigenvectors of the covariance matrix by a principal component analysis method. The selected field size is W, and a covariance matrix C is established:
solving the eigenvalues λ of the matrix1、λ2And corresponding feature vectors v1、v2：
Wherein, the eigenvector corresponding to the eigenvalue with large absolute value is the normal direction of the initial point, and λ can be known from the above formula1>λ2So that λ1Corresponding feature vector v1Normal to the initial point, λ2Corresponding feature vector v2Is tangential to the initial point.
And step 3, binarization processing.
Because a red line laser is adopted to irradiate the surface of the workpiece, the threshold segmentation is carried out on the selected pixel of 255, and a processed binary image is obtained.
And 4, extracting the contour.
Adopting a method of hollowing out internal points: if a point in the original image is black and all its 8 neighboring points are black, the point is deleted, and the contour line of the structured light with lines is obtained.
And step 5, extracting the centers of the linear and linear stripes.
Let us assume the normal direction v calculated above1For an initial point P ═ a, b0(i0,j0) Calculating a linear function in the normal direction through the initial point as
The upper contour is P1(i1,j1) The cross-over profile is P2(i2,j2) The remaining centers of the structured light stripes are:
the above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above embodiment, but equivalent modifications or changes made by those skilled in the art according to the present disclosure should be included in the scope of the present invention as set forth in the appended claims.
Claims (5)
1. A method for quickly and accurately extracting the centers of linear striations of a regular surface is characterized by comprising the following steps of: 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 a self-adaptive threshold method;
step 2, calculating the normal direction of the initial point; calculating a covariance matrix from gradient vectors of pixels in the initial point field, and obtaining the normal direction of the light striations by solving eigenvectors of the covariance matrix through a principal component analysis method;
step 3, binarization processing; selecting 255 pixels for threshold segmentation to obtain a processed binary image;
step 4, contour extraction; deleting redundant points by a method of hollowing out internal points to obtain a contour line of the structured light with lines;
step 5, extracting the centers of the linear striations of the line structure; and calculating a linear function of the initial point in the normal direction, an upper crossing profile and a lower crossing profile 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 structural light stripe center.
2. The method for rapidly and accurately extracting the centers of linear striations of a regular surface according to claim 1, wherein the method comprises the following steps: in step 1, the image f (x, y) is convolved with a gaussian function g (x, y), i.e.
To reduce the influence of image noise points; calculating the gray gradient (G) of the imagex，Gy) And the magnitude | G (x, y) |, the calculation process is:
within ROI of the extracted line structured light, Mi(i-1, 2,3 …) represents the ith row in the light bar, and the point P with zero amplitude is searched line by line0(i0，j0) This point is taken as the initial point.
3. The method for rapidly and accurately extracting the centers of linear striations of a regular surface according to claim 1, wherein the method comprises the following steps: in step 2, the selected field size is W, and a covariance matrix C is established:
solving an eigenvector v corresponding to the eigenvalue lambda of the matrix:
and the characteristic vector v corresponding to the lambda is the normal direction of the initial point.
4. The method for rapidly and accurately extracting the centers of linear striations of a regular surface according to claim 1, wherein the method comprises the following steps: in step 4, the method of hollowing out the internal points is adopted, if one point in the original image is black and all 8 adjacent points are black, the point is deleted, and finally the contour line of the line structured light is obtained.
5. The method for rapidly and accurately extracting the centers of linear striations of a regular surface according to claim 1, wherein the method comprises the following steps: in step 5, the normal direction v calculated as above is set to (a, b), and the initial point P is set to0(i0，j0) Calculating a linear function in the normal direction through the initial point as
The upper contour is P1(i1，j1) The cross-over profile is P2(i2，j2) The remaining centers of the structured light stripes are:
then for P0(i0，j0) Repeating the operation at the next point in the tangential direction to finally obtain the complete center of the structured light stripe.
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