# Morphological Structural Element Optimization for Automatic Line Characterization of Ink Drawings

Authors: Caixia Ma
Journal: Security and Communication Networks
 (2022)

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## Abstract

As a traditional Chinese art, the tools of Chinese ink painting are mainly brushes, ink, and rice paper and ink stone. As an important form of ink painting, ink landscape painting consists of ink lines that first outline the overall trend of the mountain and then complete the basic process of painting with rich chapping techniques. The article proposes a process to draw water animation based on Chinese painting style; this method first perceives the position of water form in Chinese painting and enhances it by morphological structural elements used to automatically draw the line feature analysis of ink painting; next, the image rendering technique is used to render the water form, firstly using BF for noise removal processing and then gradienting by Canny edge detection operator to get the gradient amplitude. Next, morphological optimization is applied, and finally the image is visualized. In the process of morphological optimization, the optimal parameters of the structural elements are found through the threshold segmentation number, and the rendering algorithm for ink-based landscape painting is explored in depth, starting from the ink lines. The results reveal that the method is more accurate than typical marker-watershed algorithm segmentation, and that changing different structural elements and their parameters has a bigger impact on the ink painting drawing effect.


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## Body

## 1. Introduction

Non-photorealistic rendering (NPR) is a technique that focuses on adding features. The NPR technique is to add rich artistic features to the resulting images, embedding the charm of things and the mood of the characters, more similar to the hand-drawn style of painting art [1]. Currently, non-realistic rendering techniques have been elaborate in computer art (CA), computer animation, visual medicine, and 3D images of plants and animals. The variance with realistic rendering techniques is that they focus on the parts of the scene that people need and ignore some unnecessary details [2, 3] [4].In recent years, along with the rapid development of CA simulation, the research results of CA simulation have gradually started to be used in many fields such as film and television, game scenes, and computer painting, and a large number of simulation implementation methods have emerged [5–7]. In western painting, for example, computers can simulate oil paintings, drawings, watercolors, and other works of art very realistically and provide an eclectic range of simulation methods [8]. The development of ink painting simulation is still in a relative stage of development compared with the development of computer simulation in western painting, and the rendering of ink painting in film and animation is still in a relative stage of development [9, 10]. There is still a huge gap in the rendering simulation of ink painting in the fields of film, television, and animation [11, 12]. Therefore, it is necessary to conduct research on ink painting rendering [13, 14].As a traditional Chinese art, the tools of Chinese ink painting are mainly brushes, ink, Xuan paper, and ink stone [15]. As an important form of ink painting, ink landscape painting consists of ink lines that first outline the overall trend of the mountain, and then with the rich chapping techniques, the basic process of painting is completed [16, 17]. There are two main movements in the simulation of ink and landscape painting [18]. The first is physical ink painting simulation, in which researchers simulate brushes, ink, and rice paper with hardware devices, and this research started early [19]. The research results are rich, but the cost of the research is high because of the need to make hardware models of brushes and paper [20]. Another is non-physical-based ink painting simulation, which realizes ink simulation through computer image and graphics technology and computer mathematical modelling [21]. As a new branch of non-physical ink painting simulation, image-based ink effect simulation is gradually being emphasized by researchers because of its low research cost, simple geometric models, real-time processing of real photos, and simulation results being closer to real scenes. However, compared with the simulation of foreign oil paintings and watercolor paintings, there is still a big gap [22].In ink painting, the line drawn by brush is the main and most basic component, while “hook,” “chap,” “dye,” and “dot” are the most important components of ink painting. As the basic technique of ink landscape, the “hook,” “chap,” “dye,” and “point” are also made up of a thousand different combinations of lines [23]. From this, we can see that ink lines are the soul of ink painting and the basic steps of ink painting [24]. Therefore, this paper will discover the rendering algorithm of ink and landscape painting based on images, starting from ink lines [25].This paper demonstrates a Chinese painting-inspired method for sketching water animation. According to the painter’s painting viewpoint, the essence and qualities of the painter’s painting are extracted and merged with the program intended to extract the entire contour lines of mountain peaks and apply an ink and wash effect to them. Non-physical simulation is further divided into two types in this paper: mathematical model-based ink simulation and image-based ink simulation.This paper is organized as follows.Section2 discusses various art computer simulations. The synthesis of ink and landscape paintings based on real images is offered as an algorithm. The ink simulation technology is examined based on texture synthesis. Section 3 defines the painter’s ink strokes used as textures and the texture mapping algorithm. Section 4 deliberates the bilateral filtering on the basis of normal weighted filtering, using the Canny operator based on edge detection. Section 5 expresses morphological processing such as expansion and erosion and opening and closing operations. Section 6 considers an ink rendering model. Section 7 concludes the article.


## 2. Related Works

In recent years, the art of computer simulation has developed rapidly, and researchers have conducted a lot of research and exploration on the implementation of simulation of non-realistic image painting, and new image graphics simulation techniques and algorithms have been explored in a constant stream. This research classifies non-physical-based ink simulation into two categories based on an analysis of existing non-physical-based ink simulation techniques: mathematical model-based ink simulation and image-based ink simulation.


### 2.1. Status of Research on Image-Based Ink Simulation

The algorithm of ink and landscape painting synthesis based on real pictures is proposed, which firstly obtains the painter’s ink strokes and drawing control images and then synthesizes the painter’s strokes on the control images through textures to finally obtain the real landscape painting effect; an image-based Chinese ink painting drawing system is established, which firstly divides the real pictures into several drawing parts and connects the edges of each part according to the edge connection mechanism as brush paths. The internal grayscale information from each part’s color space is then processed using image processing methods such as grayscale change, filtering, and color equalization to reduce the image from many grayscale levels to five grayscale levels, and finally, paper and ink diffusion effects are added to complete the rendering. Its simulation strokes, on the other hand, are too single and unable to draw more complex lines. The result only shows the diffusion effect of colored ink; the ink line drawing algorithm based on the real picture first detects the characteristic edges of the picture to divide the constrained area, diffuses the edges through the edge protection minimization energy model, generates the absorbent paper background according to the characteristics of absorbent paper combined with the texture synthesis technology, and finally fuses the diffusion map and absorbent paper to form the ink diffusion effect of the picture. The algorithm can simulate the black or colored ink effect of real pictures, and there is still a certain gap to complete the ink painting.


### 2.2. Ink Simulation Technology Based on Mathematical Model

Based on the theory of texture synthesis, by carefully analyzing the line composition of rocks and trees in ink painting, a non-physical 3D model is established for simulation, starting from the ink lines of the chops and rendering the rocks in ink style by controlling the information of the position, simulation, and thickness characteristics of the brush path. The simulation system is established on non-realistic sense particle system. We simulate the “Brownian motion” as the driving force of the ink particles; firstly, we initialize the particles by detecting the edges of the input strokes, and then we achieve the effect of multiple strokes by mixing the layers of objects. A fractal simulation of ink painting diffusion boundary implementation method is proposed, which applies fractal simulation of ink diffusion contour to deal with the boundary problem of ink painting well, and the boundary is more realistic and natural, but the ink edge processing is too single, which cannot better show the skill of using brush and ink in ink painting. It is impossible to demonstrate the artistic effect of “flying white.”


## 2.1. Status of Research on Image-Based Ink Simulation

The algorithm of ink and landscape painting synthesis based on real pictures is proposed, which firstly obtains the painter’s ink strokes and drawing control images and then synthesizes the painter’s strokes on the control images through textures to finally obtain the real landscape painting effect; an image-based Chinese ink painting drawing system is established, which firstly divides the real pictures into several drawing parts and connects the edges of each part according to the edge connection mechanism as brush paths. The internal grayscale information from each part’s color space is then processed using image processing methods such as grayscale change, filtering, and color equalization to reduce the image from many grayscale levels to five grayscale levels, and finally, paper and ink diffusion effects are added to complete the rendering. Its simulation strokes, on the other hand, are too single and unable to draw more complex lines. The result only shows the diffusion effect of colored ink; the ink line drawing algorithm based on the real picture first detects the characteristic edges of the picture to divide the constrained area, diffuses the edges through the edge protection minimization energy model, generates the absorbent paper background according to the characteristics of absorbent paper combined with the texture synthesis technology, and finally fuses the diffusion map and absorbent paper to form the ink diffusion effect of the picture. The algorithm can simulate the black or colored ink effect of real pictures, and there is still a certain gap to complete the ink painting.


## 2.2. Ink Simulation Technology Based on Mathematical Model

Based on the theory of texture synthesis, by carefully analyzing the line composition of rocks and trees in ink painting, a non-physical 3D model is established for simulation, starting from the ink lines of the chops and rendering the rocks in ink style by controlling the information of the position, simulation, and thickness characteristics of the brush path. The simulation system is established on non-realistic sense particle system. We simulate the “Brownian motion” as the driving force of the ink particles; firstly, we initialize the particles by detecting the edges of the input strokes, and then we achieve the effect of multiple strokes by mixing the layers of objects. A fractal simulation of ink painting diffusion boundary implementation method is proposed, which applies fractal simulation of ink diffusion contour to deal with the boundary problem of ink painting well, and the boundary is more realistic and natural, but the ink edge processing is too single, which cannot better show the skill of using brush and ink in ink painting. It is impossible to demonstrate the artistic effect of “flying white.”


## 3. Ink Line Rendering

After the painter’s ink strokes are acquired, the painter’s ink strokes are used as textures, and the texture mapping algorithm is used to map the painter’s ink strokes textures to the strip model to generate the ink line effect of the real mountain peak contours. The painter’s ink strokes will be mapped to the sample strip model by altering a texture mapping technique based on bilinear interpolation in this thesis. Bilinear interpolation is used to alter the painter’s stroke size so that the real ink strokes match the width of the sample strip model.


### 3.1. Bilinear Interpolation Algorithm

The bilinear interpolation algorithm is used in image processing to complete the enlargement or reduction of the original image by generating new pixels. The painter’s stroke size is adjusted by bilinear interpolation so that the real ink strokes can match the width of the sample strip model. Each ink stroke segment is mapped to the grid model’s corresponding point. Bilinear interpolation is used to estimate the pixel value of a point in the target image by finding the 2∗2 neighboring pixels of the point in the foundation image and using the weighted average of the pixels in the 2∗ 2 region to obtain the pixel value of a point in the target image.The bilinear interpolation inx-direction is calculated by the following formula:(1)fR1≈x2−xx2−x1fQ11+x−x1x2−x1fQ21in whichR1=x,y1,fR2≈x2−xx2−x1fQ12+x−x1x2−x1fQ22in whichR2=x,y2.In the implementation,Q11, Q12, Q21, and Q22 are 4 adjacent pixels and the p point falls on one of the above 4 points. Let the upper left point in the region be the region origin and the horizontal distance from the target pixel to the origin of the region it belongs to be Δcol; then, the color values of R1 and R2 in the x-direction are calculated as follows:(2)δR1=ColorQ21−ColorQ11Δcol+ColorQ11256,δR2=ColorQ22−ColorQ12Δcol+ColorQ12256,where Color (X) represents the color value of point X. The specific calculation is done in 24 bit true color format.The above describes the interpolation in thex-direction, and in general, for the y-direction, the interpolation is calculated as(3)fP≈y2−yy2−y1fR1+y−y1y2−y1fR2.


### 3.2. Texture Mapping of Real Ink Strokes

Texture mapping is a technique for mapping a texture to a target place in order to give the target item the desired texture effect.By understanding the bilinear interpolation algorithm and texture mapping technique, we combine these two key techniques to map the painter’s real brushstrokes mapped onto the strip model. Observe the painter’s ink painting of the mountain peaks, as shown in Figure1; the peaks are continuous when painting the folded strip. The lines of the brushstrokes are straighter and less curved, and the lines of the brushstrokes are minimally parallel to each other, and the lengths of the lines appear together.Figure 1
Ink and stone outline.Observe the characteristics of the painter’s ink and stone outline, the ink line mapping of the mountain peak outline should be carried out in segments, that is, the sample strip model is divided into a parametric grid model of segments, and the painter’s brushstrokes are divided according to the proportion of the segments of the grid. Finally, each segment of ink strokes is mapped to the corresponding position of the grid model. It is not necessary to find the topological relationship between the 3D grid and the 2D grid in this paper because the parametric grid model is built directly on the 2D image. Instead, the correspondence between the grid model and the texture image is established, and the effect map is generated using current texture mapping technology, which has the advantage of increasing texture mapping efficiency and reducing modelling complexity.


## 3.1. Bilinear Interpolation Algorithm

The bilinear interpolation algorithm is used in image processing to complete the enlargement or reduction of the original image by generating new pixels. The painter’s stroke size is adjusted by bilinear interpolation so that the real ink strokes can match the width of the sample strip model. Each ink stroke segment is mapped to the grid model’s corresponding point. Bilinear interpolation is used to estimate the pixel value of a point in the target image by finding the 2∗2 neighboring pixels of the point in the foundation image and using the weighted average of the pixels in the 2∗ 2 region to obtain the pixel value of a point in the target image.The bilinear interpolation inx-direction is calculated by the following formula:(1)fR1≈x2−xx2−x1fQ11+x−x1x2−x1fQ21in whichR1=x,y1,fR2≈x2−xx2−x1fQ12+x−x1x2−x1fQ22in whichR2=x,y2.In the implementation,Q11, Q12, Q21, and Q22 are 4 adjacent pixels and the p point falls on one of the above 4 points. Let the upper left point in the region be the region origin and the horizontal distance from the target pixel to the origin of the region it belongs to be Δcol; then, the color values of R1 and R2 in the x-direction are calculated as follows:(2)δR1=ColorQ21−ColorQ11Δcol+ColorQ11256,δR2=ColorQ22−ColorQ12Δcol+ColorQ12256,where Color (X) represents the color value of point X. The specific calculation is done in 24 bit true color format.The above describes the interpolation in thex-direction, and in general, for the y-direction, the interpolation is calculated as(3)fP≈y2−yy2−y1fR1+y−y1y2−y1fR2.


## 3.2. Texture Mapping of Real Ink Strokes

Texture mapping is a technique for mapping a texture to a target place in order to give the target item the desired texture effect.By understanding the bilinear interpolation algorithm and texture mapping technique, we combine these two key techniques to map the painter’s real brushstrokes mapped onto the strip model. Observe the painter’s ink painting of the mountain peaks, as shown in Figure1; the peaks are continuous when painting the folded strip. The lines of the brushstrokes are straighter and less curved, and the lines of the brushstrokes are minimally parallel to each other, and the lengths of the lines appear together.Figure 1
Ink and stone outline.Observe the characteristics of the painter’s ink and stone outline, the ink line mapping of the mountain peak outline should be carried out in segments, that is, the sample strip model is divided into a parametric grid model of segments, and the painter’s brushstrokes are divided according to the proportion of the segments of the grid. Finally, each segment of ink strokes is mapped to the corresponding position of the grid model. It is not necessary to find the topological relationship between the 3D grid and the 2D grid in this paper because the parametric grid model is built directly on the 2D image. Instead, the correspondence between the grid model and the texture image is established, and the effect map is generated using current texture mapping technology, which has the advantage of increasing texture mapping efficiency and reducing modelling complexity.


## 4. Morphological Structures

Here, we discuss the BF for the basis of normal weighted filtering using the Canny operator-based edge detection. The edge detection using a clever algorithm can smooth the image and reduce noise.


### 4.1. Bilateral Filtering (BF)

BF as a non-linear filter, on the basis of mean or normal weighted filtering, smoothens the image by distance weight and color weight and also deliberates the geometric distance and color distance between pixels, which can eliminate noise and achieve image edge protection at the same time. BF is defined as follows:(4)BFIp=1W∑q∈SGσdp−qGσrIp−IqIq,Wp=∑q∈SGσdp−qGσrIp−Iq,where BF denotes bilateral filtering; Wp denotes a standard quantity; Gσd is a spatial function to reduce the effect of distant pixels; and Gσr is a parametric function to reduce the effect of pixels q with gray values different from Iq. They are defined as(5)Gσd=e−1/2dp,a/σd2,Gσr=e−1/2δIp·I∂/σr2,where d (p, q) and δIp,Ip are the Euclidean distance between two pixel points of the image and the grayscale difference of the pixels, respectively, and parameters σd and σr are the spatial proximity factor and the luminance proximity factor, respectively, which are measures of the amount of filtering in the image, based on the standard deviation of the Gaussian filtering function, which has a significant effect on the filtering.


### 4.2. Canny Operator-Based Edge Detection

Canny algorithm edge detection can smooth the image and eliminate noise. It consists of four steps: (i) smoothing the original image with Gaussian filter; (ii) calculating the amplitude and direction of the gradient with first-order partial derivative difference; (iii) non-maximal suppression of the gradient amplitude; and (iv) detecting and connecting the edges with double thresholding algorithm.Only the last three steps are executed here because of the BF and denoising process. The gradient mode size and direction must be computed in order to correctly estimate the edge position. The gradient operator is commonly used to detect edges in the discrete case; given the gradientsφ1m,n and φ2m,n of the image f (m, n) in the two orthogonal directions H1 and H2, then obtain the edge direction θφ to accomplish image edge detection.First, a first-order differential convolution template is constructed, and then a threshold is calculated, which is defined as follows:(6)H1=−1−111,H2=1−11−1,φ1m,n=fm,n∗H1m,n,φ2m,n=fm,n∗H2m,n,θφ=tan−1φ2m,n/φ1m,n.When the gradient magnitude calculation can only attain the global gradient and cannot determine the edges, to attain the determination of edges, non-maximum suppression (NMS) is performed by keeping the local gradient maximum points. At each point of the image, the central pixelM of the neighborhood is compared with the two pixels along the gradient line [26–28]. If the gradient of M is smaller than the gradient of the two neighboring pixels along the gradient line, then let M = 0, i.e., the threshold edge image is obtained:(7)Ni,j=NMSMi,j,ζi,j,where NMS (,) denotes the size judgment function between the pixel at the center of the domain and the pixel value on the gradient line and ζi,j denotes any pixel on the gradient line.The double thresholding algorithm is then used to detect and connect the edges. The dual thresholding algorithm acts on the two thresholdsθ1 and θ 2(2θ1 = θ2) so as to obtain two edge images N1i,j and N2i,j. Then, the edges are connected into contours in N2i,j and when the endpoints of the contours are reached, the edges that can be connected to the contours are searched for at the 8 neighboring locations of N1i,j making it possible to continuously collect edges in N1i,j until N2i,j is connected, so that the edges are precisely determined.


## 4.1. Bilateral Filtering (BF)

BF as a non-linear filter, on the basis of mean or normal weighted filtering, smoothens the image by distance weight and color weight and also deliberates the geometric distance and color distance between pixels, which can eliminate noise and achieve image edge protection at the same time. BF is defined as follows:(4)BFIp=1W∑q∈SGσdp−qGσrIp−IqIq,Wp=∑q∈SGσdp−qGσrIp−Iq,where BF denotes bilateral filtering; Wp denotes a standard quantity; Gσd is a spatial function to reduce the effect of distant pixels; and Gσr is a parametric function to reduce the effect of pixels q with gray values different from Iq. They are defined as(5)Gσd=e−1/2dp,a/σd2,Gσr=e−1/2δIp·I∂/σr2,where d (p, q) and δIp,Ip are the Euclidean distance between two pixel points of the image and the grayscale difference of the pixels, respectively, and parameters σd and σr are the spatial proximity factor and the luminance proximity factor, respectively, which are measures of the amount of filtering in the image, based on the standard deviation of the Gaussian filtering function, which has a significant effect on the filtering.


## 4.2. Canny Operator-Based Edge Detection

Canny algorithm edge detection can smooth the image and eliminate noise. It consists of four steps: (i) smoothing the original image with Gaussian filter; (ii) calculating the amplitude and direction of the gradient with first-order partial derivative difference; (iii) non-maximal suppression of the gradient amplitude; and (iv) detecting and connecting the edges with double thresholding algorithm.Only the last three steps are executed here because of the BF and denoising process. The gradient mode size and direction must be computed in order to correctly estimate the edge position. The gradient operator is commonly used to detect edges in the discrete case; given the gradientsφ1m,n and φ2m,n of the image f (m, n) in the two orthogonal directions H1 and H2, then obtain the edge direction θφ to accomplish image edge detection.First, a first-order differential convolution template is constructed, and then a threshold is calculated, which is defined as follows:(6)H1=−1−111,H2=1−11−1,φ1m,n=fm,n∗H1m,n,φ2m,n=fm,n∗H2m,n,θφ=tan−1φ2m,n/φ1m,n.When the gradient magnitude calculation can only attain the global gradient and cannot determine the edges, to attain the determination of edges, non-maximum suppression (NMS) is performed by keeping the local gradient maximum points. At each point of the image, the central pixelM of the neighborhood is compared with the two pixels along the gradient line [26–28]. If the gradient of M is smaller than the gradient of the two neighboring pixels along the gradient line, then let M = 0, i.e., the threshold edge image is obtained:(7)Ni,j=NMSMi,j,ζi,j,where NMS (,) denotes the size judgment function between the pixel at the center of the domain and the pixel value on the gradient line and ζi,j denotes any pixel on the gradient line.The double thresholding algorithm is then used to detect and connect the edges. The dual thresholding algorithm acts on the two thresholdsθ1 and θ 2(2θ1 = θ2) so as to obtain two edge images N1i,j and N2i,j. Then, the edges are connected into contours in N2i,j and when the endpoints of the contours are reached, the edges that can be connected to the contours are searched for at the 8 neighboring locations of N1i,j making it possible to continuously collect edges in N1i,j until N2i,j is connected, so that the edges are precisely determined.


## 5. Morphological Optimization Processing

Morphological processing includes processes such as expansion and erosion, opening and closing operations, and morphological reconstruction, all of which are closely related to structural elements. Non-flat structured elements in the specified domain include spheres, diamonds, circles, lines, octagons, pairs, periodic lines, rectangles, and squares, while flat structured elements in the specified domain include spheres, diamonds, circles, lines, octagons, pairs, periodic lines, rectangles, and squares.Let A be the target image andB be the structuring element; then, the process of expansion and erosion of the target image A by the structuring element B is defined as(8)A⊕B=x|B^x∩​A≠ϕ,AΘB=x|Bx⊆A,where x is the translation and ⊕ and Θ are the operators for expansion and erosion, respectively. The expansion expands the boundary of image A, while the erosion is the opposite. The methods of expansion and erosion are controlled by the structural elements [29].The open operation corrupts and then expands the image. The opposite is true for the closed operation, which is defined as(9)A°B=AΘB⊕B,A·B=A⊕BΘB,where ∘and ⋅ are the open and closed operators, respectively. The open operator filters out the vestigial parts of the relative structural elements, and the closed operator can complement the geometric features of the image by selecting structural elements to make them clearer and more coherent.IfRgf is a mask and f is a marker, the reconstruction of g, g from f can be written as Rgf defined by the following iterative procedure.(1)
Initializeh1 to the marker image f.(2)
Create structural elements.(3)
Repeat:(10)hk+1=hk⊕B∩​g,until:(11)hk+1=hk.


## 6. Ink Painting Effect

In ink painting, the line drawn by the brush is the main and most basic component”. As the basic techniques of ink painting, they are also made up of a thousand different combinations of lines. It can be said that ink lines are the soul of ink painting. Therefore, the study of ink line rendering techniques is of great significance. Based on this, this paper explores the line rendering algorithm for ink and landscape painting using real mountain pictures as input and image graphics technology [29, 30].Starting with a real mountain photograph, the essence and features of the painter’s work are extracted and merged with the photographer’s perspective. The program is intended to extract the entire contour lines of the mountain peaks and apply an ink and wash effect to them. The algorithm is designed to extract the whole contour lines of mountain peaks, draw the ink effect on the contour lines, and complete the “hook” method of ink painting; second, the “chap” effect of the mountain body is reproduced using appropriate literature technology; finally, the design adds the Xuanhua. Finally, the algorithm is designed to add the effect of rice paper to the image [31, 32]. The experimental results are shown in Figures 2 and 3.Figure 2
One of the mountain peaks’ ink line rendering effect.Figure 3
Mountain peaks’ ink line rendering effect of the second show.From Figure3 results, it can be seen that through the program implementation of the ink line rendering algorithm, the ink line rendering algorithm based on the real picture can basically meet the simulation of “hooking” and “chapping” techniques of ink landscape painting in real pictures.Different gray scale coefficientsg are set to interfere with the overall color scale of the ink painting image, as shown in Figure 4.Figure 4
Ink painting images. (a) Input. (b) g = 0.9. (c) g = 0.7. (d) g = 0.5. (e) g = 0.3. (f) g = 0.1.
(a)(b)(c)(d)(e)(f)


## 7. Conclusion

This paper presents a technique for drawing water animation based on Chinese painting style. The correspondence between the grid model and the texture image is established, and the effect map is generated using current texture mapping technology, which has the advantage of increasing texture mapping efficiency and reducing modelling complexity. This method first detects the location of water shapes in Chinese paintings and optimizes the line feature analysis by morphological structure elements for automatic drawing of ink paintings. An in-depth exploration is conducted for the image-based rendering algorithm of ink and landscape paintings. The results show that the method is more exact than the traditional marker-watershed algorithm segmentation, and the variation of diverse structural elements and their parameters has a greater impact on the ink painting drawing effect.



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*Source: 8595203-2022-05-11.xml*