CN114461829A - Method for vectorization of traditional culture memory symbol subgraph - Google Patents

Abstract

The invention discloses a method for vectorizing traditional cultural memory symbol sub-images. The method includes the following steps: S1. Calculating pixel points in an image, determining element outlines and extracting complete sub-elements; S2. Calculating the sub-elements Perform image processing and divide it into different areas; S3, approximate the edges of each area, and convert them into smooth vector area contours; S4, integrate the data information in each area, obtain and save the area vectorization results, and output the sub-elements. Vector illustration. Accurate and complete sub-elements are obtained through less interaction, and local elements are vectorized through vectorization technology to obtain their corresponding vector materials, thus completing the entire process of sub-element vectorization in traditional cultural images, and obtaining an image The vector graphics of the local elements in the image can be quickly obtained, which improves the quality of the material and greatly reduces the difficulty of secondary creation of cultural materials.

Description

A method for vectorization of traditional cultural memory symbol subgraphs

technical field

The present invention relates to the technical field of computer image processing, in particular, to a method for vectorizing traditional cultural memory symbol subgraphs.

Background technique

In the context of today's era, the digitization and reuse of cultural heritage is an effective way to protect and inherit culture. ", has epoch-making significance for inheriting and disseminating Chinese culture, changing cultural production methods, and developing cultural productivity. Image vectorization technology is the core tool for building a "Chinese cultural material library".

Compared with raster images, vector graphics have many advantages, such as image quality independent of resolution, can be arbitrarily scaled without distortion; compact storage, small files, efficient storage and transmission; support for editing geometric primitives Revise. Image vectorization is the conversion of raster-type images into vector-formatted images.

At present, the materials in the Chinese cultural material library are still mainly vector graphics. An image with cultural meaning usually contains one or more sub-elements with rich connotations. The vector graphics of such elements are used in cultural creation. It is particularly important. Usually, users need certain post-processing operations to obtain partial vector graphics from the complete vector graphics, which greatly reduces the efficiency of using materials in the material library. In addition, due to the influence of the image acquisition environment, the image quality is uneven. If the image with unsatisfactory effect is directly vectorized for the entire image, the effect of the obtained vector illustration will also be unsatisfactory, and its cultural value cannot be fully exerted, reducing the cost of the whole image. The use value of the material in the material library.

For the problems in the related technologies, no effective solutions have been proposed so far.

SUMMARY OF THE INVENTION

In view of the problems in the related art, the present invention proposes a method for vectorizing traditional cultural memory symbol subgraphs to overcome the above-mentioned technical problems existing in the related art.

For this reason, the concrete technical scheme that the present invention adopts is as follows:

A method for vectorizing traditional cultural memory symbol subgraphs, the method includes the following steps:

S1, calculate the pixel points in the image, determine the outline of the element and extract the complete sub-element;

S2, performing image processing on the sub-elements, and dividing them into different regions;

S3. Approximate the edge of each area and convert it into a smooth vector area outline;

S4. Integrate the data information in each area, obtain and save the area vectorization result, and output the vector diagram of the sub-elements.

Further, the pixel point in the described calculation image, determine the outline of the element and extract the complete sub-element, comprising the following steps:

S11, select the feature component representing the edge of the target element, and the feature component cost is weighted and summed to calculate the local cost from each pixel to adjacent pixels;

S12. Calculate the channel difference between adjacent pixel points by fusing the color gradient feature to improve the fit of the element extraction contour;

S13. Use the Dijkstra algorithm to search for the minimum cost path to obtain a complete and closed element contour.

Further, the feature components include Laplacian zeros, gradient magnitudes and gradient directions.

Further, the described calculation formula of the weighted summation of the feature component cost to calculate the local cost from each pixel point to the adjacent pixel point is:

C(p,q)= _wG · _fG (q)+ _wZ · _fZ (q)+wD· _{fD (} p,q);

where C(p,q) represents the local cost of the directional link from pixel p to adjacent pixel q, _fZ represents the Laplacian zero, _fG represents the gradient magnitude, _fD represents the gradient direction, _wG = 0.43, w _Z =0.43, w _D =0.14.

Further, described by the fusion color gradient feature, calculate the channel difference between adjacent pixels, improve the fit degree of the element extraction contour, comprising the following steps:

S121, fuse the color gradient features, convert the image to a colorimetric system (CIE-Lab) space, and perform Lab channel separation;

S122, calculate the difference between the L channel and the ab fusion channel between adjacent pixels, and the calculation formula is as follows:

f _C (q)=(C _r ⊙C _l , C _t ⊙C _b );

△L_ij＝△L_i-△L_j，△a_ij＝△a_i-△a_j，△b_ij＝△b_i-△b_j，C_r、C_l、C_t及C_b分别为右、左、上、下邻域中像素点在表色体系空间下的加权平均值。where f _C (q) represents the color gradient,

ΔL _ij =ΔL _i -ΔL _j , Δa _ij =Δa _i -Δa _j , Δb _ij =Δb _i -Δb _j , C _r , C _l , C _t and C _b are right , the weighted average of the pixels in the left, upper and lower neighborhoods in the color system space.

S123. Substitute the difference value into the calculation process of the local cost, improve the fit of the element extraction contour, and reduce the number of interactions.

Further, the formula for the calculation process of substituting the difference value into the local cost is:

C(p,q)= _wG ·[ _βfG (q)+(1-β) _fZ (q)]+ _wZ · _fZ (q)+wD· _{fD (} p,q);

where C(p,q) represents the local cost of the directional link from pixel p to adjacent pixel q, _fZ represents the Laplacian zero, _fG represents the gradient magnitude, _fD represents the gradient direction, _wG = 0.43, w _Z =0.43, w _D =0.14.

Further, using the Dijkstra algorithm to search for the minimum cost path to obtain a complete and closed element contour includes the following steps:

S131, select any point on the element boundary to expand, and then add the local cost of the point to its adjacent nodes;

S132, select the adjacent node with the smallest accumulated cost to continue to expand;

S133, repeat the node expansion to form a sequence until it coincides with the initial point, and obtain a complete and closed element outline.

Further, the described sub-elements are subjected to image processing and divided into different regions, including the following steps:

S21. Use the clustering algorithm to cluster the regions in the image, and set a certain spatial distance and color distance;

S22, performing multiple iterations, replacing the original pixel value with the converged pixel value, and merging locally similar pixel points to obtain different color regions;

S23. Extract the edge structure of each region, use the multi-level edge detection (canny) operator to detect the edge of the region, calculate the size and direction of the image gradient, perform non-maximum suppression and double-threshold screening, and obtain the edge of the region.

Further, the described approximation of the edge of each region, converted into a smooth vector region outline, including the following steps:

S31. Simplify the obtained edge curve by the Douglas-Peucker algorithm;

S32, iteratively select the point on the curve with the farthest distance from the corresponding straight line segment greater than the set threshold as the polygon vertex, and connect it to obtain a fitted polygon;

S33. Use Bezier curve to fit and smooth the polygon, and convert it into an accurate and smooth vector outline.

Further, the data information includes vector outline information and color information.

The beneficial effects of the invention are as follows: accurate and complete sub-elements are obtained through less interaction, and the vectorization of local elements is realized through the vectorization technology, and the corresponding vector materials are obtained, thereby completing the vectorization of sub-elements in traditional cultural images. The whole process of obtaining a vector diagram of the local elements in the image. Compared with the general image extraction method, the present invention not only ensures the fit of the extracted contour during element extraction, but also greatly reduces the number of interactions. At the same time, the two-stage process proposed by the present invention enables users to pass fewer Interaction, quickly get the local sub-element vector graphics in the image, improve the quality of the materials in the material library, greatly reduce the difficulty of secondary creation of cultural materials, and enable cultural materials to exert their maximum creative value.

Description of drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings required in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some of the present invention. In the embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.

Fig. 1 is a flowchart of a method for vectorizing traditional cultural memory symbol subgraphs according to an embodiment of the present invention.

Detailed ways

According to an embodiment of the present invention, a method for vectorizing traditional cultural memory symbol subgraphs is provided.

The present invention will now be further described in conjunction with the accompanying drawings and the specific embodiments, as shown in Figure 1, according to the method for the vectorization of traditional cultural memory symbol subgraphs according to an embodiment of the present invention, the method comprises the following steps:

S1, calculate the pixel points in the image, determine the outline of the element and extract the complete sub-element;

In this step, the problem of finding the edge contour of an element is regarded as the optimal path search problem in the process of graph search. The optimal path must be a path that fits the boundary of the element. Therefore, there are links between pixels with strong edge features and other pixels. should have low local cost.

Among them, S1 includes the following steps:

S11. Select a feature component representing the edge of the target element (the feature component includes Laplacian zero point, gradient size and gradient direction), and perform weighted summation on the feature component cost to calculate the local cost from each pixel to adjacent pixels , its calculation formula is:

C(p,q)= _wG · _fG (q)+ _wZ · _fZ (q)+wD· _{fD (} p,q);

where C(p,q) represents the local cost of the directional link from pixel p to adjacent pixel q, _fZ represents the Laplacian zero, _fG represents the gradient magnitude, _fD represents the gradient direction, _wG = 0.43, w _Z =0.43, w _D =0.14.

The gradient size cost term is mainly a description of the edge strength, and the position with strong edge strength should pay a smaller price. Convolve the image with the sobel operator to get the gradient magnitude.

The Laplacian zero-point cost term is mainly a description of the boundary positioning. The image is convolved with the Laplacian operator to obtain Out _L . If Out _L (q) = 0 or there is a neighbor with a different sign, let f _Z (q)=0, otherwise f _Z (q)=1.

The gradient direction cost term is mainly to add smooth constraints to the position where the direction of the boundary changes, and to give a larger cost to the position where the direction changes sharply. Let D(p) be a unit vector perpendicular to the gradient direction at point p, then the gradient direction cost calculation formula is as follows:

_dp (p,q)=D(p)·L(p,q);

d _q (p,q)=L(p,q)·D(q);

Where L(p,q) represents the link direction between two points, so that the difference between p and the final link direction is less than 90 degrees. When the gradient directions of two pixels are similar to each other, and the link directions between them are also similar, the cost of gradient direction features is low.

Considering the inconsistency between gray and color changes in the image, the gray gradient penalty term is further optimized. Given an input image, first convert it to a perceptually unified CIE-Lab color space, and then perform the color gradient fusion of the following color images.

S12. Calculate the channel difference between adjacent pixel points by fusing the color gradient feature to improve the fit of the element extraction contour;

Wherein, S12 includes the following steps:

S121, fuse the color gradient features, convert the image to a colorimetric system (CIE-Lab) space, and perform Lab channel separation;

S122, calculate the difference between the L channel and the ab fusion channel between adjacent pixels, and the calculation formula is as follows:

f _C (q)=(C _r ⊙C _l , C _t ⊙C _b );

△L_ij＝△L_i-△L_j，△a_ij＝△a_i-△a_j，△b_ij＝△b_i-△b_j，C_r、C_l、C_t及C_b分别为右、左、上、下邻域中像素点在表色体系(CIE-Lab)空间下 的加权平均值，其表达式分别为：where f _C (q) represents the color gradient,

ΔL _ij =ΔL _i -ΔL _j , Δa _ij =Δa _i -Δa _j , Δb _ij =Δb _i -Δb _j , C _r , C _l , C _t and C _b are right , the weighted average of the pixels in the left, upper and lower neighborhoods in the color system (CIE-Lab) space, and their expressions are:

C _r =(2c(x+1,y)+c(x+1,y-1)+c(x+1,y+1))/4;

C _l =(2c(x-1,y)+c(x-1,y-1)+c(x-1,y+1))/4;

C _t =(2c(x,y+1)+c(x-1,y+1)+c(x+1,y+1))/4;

C _d =(2c(x,y-1)+c(x-1,y-1)+c(x+1,y-1))/4;

c(x,y) is the CIE-Lab value at the (x,y) position in the image.

When searching for edges, the position with the larger color gradient change is more likely to be the target boundary, which is consistent with the gray gradient change.

S123. Substitute the difference value into the calculation process of the local cost, improve the fit of the element extraction contour, and reduce the number of interactions.

Wherein, the formula for the calculation process of substituting the difference value into the local cost is:

C(p,q)= _wG ·[ _βfG (q)+(1-β) _fZ (q)]+ _wZ · _fZ (q)+wD· _{fD (} p,q);

where C(p,q) represents the local cost of the directional link from pixel p to adjacent pixel q, _fZ represents the Laplacian zero, _fG represents the gradient size, _fD represents the gradient direction, _wG = 0.43, w _Z =0.43, w _D =0.14.

S13. Use the Dijkstra algorithm to search for the minimum cost path to obtain a complete and closed element contour.

Wherein, S13 includes the following steps:

S131, select any point on the element boundary to expand, and then add the local cost of the point to its adjacent nodes;

S132, select the adjacent node with the smallest accumulated cost to continue to expand;

S133, repeat the node expansion to form a sequence until it coincides with the initial point, and obtain a complete and closed element outline.

S2, performing image processing on the sub-elements, and dividing them into different regions;

Among them, S2 includes the following steps:

S21. Use the clustering algorithm to cluster the regions in the image, and set a certain spatial distance and color distance;

S22, performing multiple iterations, replacing the original pixel value with the converged pixel value, and merging locally similar pixel points to obtain different color regions;

Among them, the specific steps of performing color clustering and edge detection on the elements to obtain different areas in the elements are: first, select a feature point arbitrarily in the feature space, and delimit a circular area with a radius of R around the feature point, Calculate the offset vector from the center point to other feature points in the area, and specify that the feature point closer to the center point has a greater estimated weight on the offset vector. The calculation formula is as follows:

The center point is moved according to the obtained vector, and then the solution is iteratively solved. When the mean shift is less than the set error, the movement is stopped, and a clustered image is obtained. The image is divided into different color areas, and the color of each area is determined by the feature. The color of the point is replaced, and the color information of each area is stored, which is convenient for subsequent color filling.

S23. Extract the edge structure of each region, use the multi-level edge detection (canny) operator to detect the edge of the region, calculate the size and direction of the image gradient, perform non-maximum suppression and double-threshold screening, and obtain the edge of the region.

For each continuous edge, connect a straight line AB between the two points A and B at the beginning and end of the curve. The straight line is the chord of the curve. Calculate the point C with the largest distance from the straight line segment on the curve, and calculate its distance d from AB. . Compare the distance with the preset threshold threshold, if it is less than the threshold, the straight line segment is used as an approximation of the curve, and the curve of this segment is processed. If the distance is greater than the threshold, use C to divide the curve into two sections AC and BC, and perform the above calculation on the two sections of the curve respectively. When all the curves are processed, the polylines formed by connecting the dividing points in turn can be used as the approximation of the curve.

S3. Approximate the edge of each area and convert it into a smooth vector area outline;

Among them, S3 includes the following steps:

S31. Simplify the obtained edge curve by the Douglas-Peucker algorithm;

S32, iteratively select the point on the curve with the farthest distance from the corresponding straight line segment greater than the set threshold as the polygon vertex, and connect it to obtain a fitted polygon;

S33. Use Bezier curve to fit and smooth the polygon, and convert it into an accurate and smooth vector outline.

Among them, the cubic Bezier curve is defined as follows:

B _n (t)=P ₀ (1-t) ³ +3P ₁ t(1-t) ² +3P ₂ t ² (1-t)+P ₃ t ³ ,t∈[0,1];

The first two points are the starting point and midpoint of the curve segment, and the two middle points need to be calculated. Set the tangential direction of the point P _i to be the same as the direction of P _i-1 P _i+1 , and ensure that the control point in front of the point and the control point behind the point are all on this tangent line, so the coordinates of the two control points as follows:

A _i (x _i +a(x _i+1 -x _i-1 ),y _i +a(y _i+1 -y _i-1 ));

B _i (x _i+1 +b(x _i+2 -x _i ),y _i+1 +b(y _i+2 -y _i ));

Converting the polygon to a vector outline is done based on the start and end points of each edge of the polygon and the coordinates of the control points.

S4. Integrate the data information in each area, obtain and save the area vectorization result, and output the vector diagram of the sub-elements.

Wherein, the data information includes vector outline information and color information, and the result is written in svg format for saving.

To sum up, with the help of the above technical solutions of the present invention, accurate and complete sub-elements are obtained through less interaction, and local elements are vectorized through vectorization technology to obtain their corresponding vector materials, thus completing the traditional The whole process of vectorization of sub-elements in cultural images, and the vector diagrams of local elements in the image are obtained. Compared with the general image extraction method, the present invention not only ensures the fit of the extracted contour during element extraction, but also greatly reduces the number of interactions. At the same time, the two-stage process proposed by the present invention enables users to pass fewer Interaction, quickly get the local sub-element vector graphics in the image, improve the quality of the materials in the material library, greatly reduce the difficulty of secondary creation of cultural materials, and enable cultural materials to exert their maximum creative value.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the scope of the present invention. within the scope of protection.

Claims (10)

1. A vectorization method for a traditional culture memory symbol subgraph is characterized by comprising the following steps:

s1, calculating pixel points in the image, determining element outlines and extracting complete sub-elements;

s2, performing image processing on the sub-elements, and dividing the sub-elements into different areas;

s3, approximating the edges of each area and converting the edges into smooth vector area outlines;

and S4, integrating the data information in each region, acquiring and storing the regional vectorization result, and outputting the vector diagram of the sub-elements.

2. The method for performing vectorization on a traditionally cultural memory symbol subgraph according to claim 1, wherein the steps of calculating pixel points in an image, determining element outlines and extracting complete sub-elements comprise:

s11, selecting characteristic components representing the edges of the target elements, and carrying out weighted summation on the characteristic component costs to calculate the local costs from each pixel point to the adjacent pixel points;

s12, calculating the channel difference between adjacent pixel points by fusing the color gradient characteristics, and improving the fitting degree of the element extraction contour;

and S13, searching the minimum cost path by using the Dixtera algorithm to obtain the complete closed element outline.

3. The method for vectorization of a traditional culture memory symbol subgraph according to claim 2, wherein the feature components include laplacian zeros, gradient magnitude and gradient direction.

4. The method for performing vectorization on a traditional culture memory symbol subgraph according to claim 3, wherein the calculation formula for performing weighted summation on the feature component cost to calculate the local cost from each pixel point to the adjacent pixel point is as follows:

C(p,q)＝w_G·f_G(q)+w_Z·f_Z(q)+w_D·f_D(p,q)；

where C (p, q) denotes the local cost of the directed link from pixel p to neighboring pixel q, f_ZDenotes the Laplace zero, f_GDenotes the magnitude of the gradient, f_DDenotes the direction of the gradient, w_G＝0.43，w_Z＝0.43，w_D＝0.14。

5. The method for performing vectorization on a traditional culture memory symbol subgraph according to claim 4, wherein the channel difference between adjacent pixel points is calculated by fusing color gradient features, so as to improve the fit degree of element extraction outlines, and the method comprises the following steps:

s121, fusing color gradient characteristics, converting the image into a color system space, and separating Lab channels;

s122, calculating the difference between the L channel and the ab fusion channel between the adjacent pixel points, wherein the calculation formula is as follows:

f_C(q)＝(C_r⊙C_l,C_t⊙C_b)；

wherein f is_C(q) represents a color gradient and (q),

△L_ij＝△L_i-△L_j，△a_ij＝△a_i-△a_j，△b_ij＝△b_i-△b_j，C_r、C_l、C_tand C_bAnd respectively the weighted average values of the pixel points in the right, left, upper and lower neighborhoods in the space of the color system.

And S123, substituting the difference value into the calculation process of the local cost, improving the fitting degree of the element extraction outline, and reducing the interaction times.

6. The method for vectorization of a traditional culture memory symbol oriented subgraph according to claim 5, wherein the formula for substituting the difference value into the calculation process of the local cost is as follows:

C(p,q)＝w_G·[βf_G(q)+(1-β)f_Z(q)]+w_Z·f_Z(q)+w_D·f_D(p,q)；

where C (p, q) denotes the local cost of the directed link from pixel p to neighboring pixel q, f_ZDenotes the Laplace zero point, f_GDenotes the magnitude of the gradient, f_DIndicating the direction of the gradient，w_G＝0.43，w_Z＝0.43，w_D＝0.14。

7. The method for vectorization of a traditional culture memory symbol subgraph according to claim 6, wherein the step of searching the minimum cost path by using the dix-tesla algorithm to obtain the complete closed element contour comprises the following steps:

s131, selecting any point on the element boundary for expansion, and adding the local cost of the point to the adjacent node;

s132, selecting the adjacent node with the minimum accumulated cost to continue expansion;

and S133, repeating the node expansion to form a sequence until the sequence is superposed with the initial point, and acquiring a complete closed element outline.

8. The method for performing traditional culture memory symbol oriented subgraph vectorization according to claim 1, wherein the sub-elements are subjected to image processing and divided into different regions, and the method comprises the following steps:

s21, clustering the areas in the image by using a clustering algorithm, and setting a certain spatial distance and a certain color distance;

s22, carrying out multiple iterations, replacing the original pixel values with the converged pixel values, merging locally similar pixel points, and obtaining different color regions;

s23, extracting the edge structure of each region, performing edge detection on the regions by using a multi-stage edge detection operator, calculating the size and the direction of the image gradient, performing non-maximum suppression and double-threshold screening, and acquiring the region edges.

9. The method for performing vectorization on a traditional culture memory symbol subgraph according to claim 1, wherein the approximating each region edge to a smooth vector region contour comprises the following steps:

s31, simplifying the obtained edge curve through a Douglas-Pock algorithm;

s32, iteratively selecting points on the curve, which are farthest from the corresponding straight-line segment and larger than a set threshold value, as polygon vertexes, and connecting the points to obtain a fitting polygon;

and S33, fitting and smoothing the polygon by using a Bezier curve, and converting the polygon into an accurate and smooth vector contour.

10. The method for performing subpicture vectorization on a traditional culture memory symbol according to claim 1, wherein the data information comprises vector contour information and color information.

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