KR20240003077A - Apparatus for automatic creation of korean landscape painting and control method thereof - Google Patents

Abstract

The present invention relates to a device for automatically generating Korean landscape paintings and a control method thereof. A method of controlling a Korean landscape painting modeling device according to the present invention includes receiving Korean landscape painting image data; extracting ridge lines from each of the received Korean landscape painting image data; selecting a region of interest based on each coordinate on the extracted ridge line; extracting a crop image from the Korean landscape painting image data corresponding to the selected region of interest; It may include the step of building an artificial intelligence model by performing machine learning using the extracted cropped image.

Description

Device for automatically generating Korean landscape paintings and its control method {APPARATUS FOR AUTOMATIC CREATION OF KOREAN LANDSCAPE PAINTING AND CONTROL METHOD THEREOF}

The present invention relates to a device for automatically generating Korean landscape paintings and a method of controlling the same. More specifically, the present invention relates to an apparatus for automatically generating Korean landscape paintings and a method for controlling the same. More specifically, after building an artificial intelligence model through machine learning, the artificial intelligence model is used to automatically generate Korean landscape paintings according to user input. It relates to a device that performs and a method of controlling it.

Recently, various technologies for automatically generating specific images through machine learning have been proposed.

For example, through image learning of specific animals, the animal's shape can be automatically generated, a human face that does not exist in the world can be generated, or an abstract image can be generated.

In order to create new images through machine learning, a proper artificial intelligence model must be built through machine learning.

For example, when performing machine learning through deep learning, after placing the input layer, hidden layer, and output layer, related learning images are input and learned while modifying the parameters included in the hidden layer to create a proper artificial intelligence model. In this case, learning data is very important.

In other words, if learning data is not appropriately selected or input, the constructed artificial intelligence model will not operate properly and the results will be unsatisfactory.

Meanwhile, Korean landscape paintings have a unique painting style that is different from the landscape paintings of other countries.

In particular, Korean landscape painting has a unique painting style that anyone with knowledge in the related field can easily distinguish it from Chinese landscape painting.

However, while a relatively large amount of Chinese-style landscape paintings remain, the remaining amount of Korean landscape paintings is small, and the amount of additional production is not large, so it is not desirable to perform machine learning on them in the conventional manner.

For example, 1,000 images of Korean landscape paintings are needed to improve the performance of machine learning, but if there are only 100 images of Korean landscape paintings that currently exist, a high-performance artificial intelligence model cannot be built even if machine learning is performed on these alone. will be.

Therefore, there is a request for a method to build an artificial intelligence model for generating Korean landscape paintings with excellent performance even with an insufficient number of Korean landscape paintings.

Public Patent No. 10-2021-0097314

The present invention was developed to meet the above-described conventional requests, and is a device for constructing an artificial intelligence model for generating Korean landscape paintings with excellent performance even from an insufficient number of Korean landscape painting images and using this to generate new Korean landscape painting images, and the same. It provides a control method.

In order to achieve the above object, a control method of a Korean landscape painting modeling device according to the present invention includes the steps of receiving Korean landscape painting image data; extracting ridge lines from each of the received Korean landscape painting image data; selecting a region of interest based on each coordinate on the extracted ridge line; extracting a crop image from the Korean landscape painting image data corresponding to the selected region of interest; It may include the step of building an artificial intelligence model by performing machine learning using the extracted cropped image.

Here, converting each of the received Korean landscape painting image data into grayscale; performing Gaussian blur filter processing on the grayscale converted image data for blurring to reduce the noise ratio; Converting the Gaussian blur filter-processed image to black and white based on a preset reference setting value; The method may include extracting a ridge line by applying a preset edge detection algorithm to the generated black-and-white converted image.

Here, a region of interest can be selected that includes each coordinate on the extracted ridge line and ensures that the ratio of the area above the ridge line and the area below the ridge line falls within a preset range.

Here, selecting coordinate groups having preset equal intervals on the extracted ridge line; It may include the step of selecting a square region of interest of the minimum size using each coordinate in the selected coordinate group as the center point and ensuring that the ratio of the area above the ridge line to the area below the ridge line is 3:7.

Here, for the specific coordinates in the selected coordinate group, if it is not possible to select a square region of interest with the specific coordinates as the center point and the ratio of the area above the ridge line and the area below the ridge line to be 3:7, the specific coordinates A square region of interest of the minimum size that is moved 20% in the vertical direction from the center point can be selected.

Here, for the coordinates included in the mountain peak area among the coordinates in the selected coordinate group, a square region of interest with the minimum size in which the corresponding coordinates are shifted 20% in the vertical direction upward from the center point can be selected.

In addition, in order to achieve the above object, a method of controlling an apparatus for automatically generating Korean landscape paintings according to the present invention includes the steps of receiving Korean landscape painting image data; extracting ridge lines from each of the received Korean landscape painting image data; selecting a region of interest based on each coordinate on the extracted ridge line; extracting a crop image from the Korean landscape painting image data corresponding to the selected region of interest; Constructing an artificial intelligence model by performing machine learning using the extracted cropped image; After the above, when line image data is received from the user, the step of automatically generating a Korean landscape painting image by inputting the received line image data into the generated artificial intelligence model may be included.

In addition, in order to achieve the above object, a Korean landscape painting modeling device according to the present invention includes a learning data receiver that receives Korean landscape painting image data; a ridge extraction unit that extracts ridge lines from each Korean landscape painting image data received by the learning data receiver; a region of interest selection unit that selects a region of interest based on each coordinate on the ridge line extracted from the ridge extraction unit; a partial image extraction unit for extracting a cropped image of the Korean landscape painting image data corresponding to the area of interest selected by the area of interest selection unit; It may include an artificial intelligence model building unit that builds an artificial intelligence model by performing machine learning using the cropped image extracted from the partial image extraction unit.

Here, the ridge extractor converts each Korean landscape painting image data received from the learning data receiver into grayscale, and performs Gaussian blur filter processing on the grayscale converted image data for blur processing to reduce the noise ratio, After converting the Gaussian blur filter-processed image to black and white based on a preset standard setting value, a ridge line can be extracted by applying a preset edge detection algorithm.

Here, the region of interest selection unit includes each coordinate on the ridge line extracted from the ridge extraction unit, and selects each region of interest such that the ratio of the area above the ridge line and the area below the ridge line falls within a preset range. .

Here, the region of interest selection unit selects coordinate groups having the same preset interval on the ridge line extracted from the ridge extraction unit, and sets each coordinate in the selected coordinate group as the center point, and sets the area above the ridge line and the area below the ridge line. Each square region of interest can be selected with the minimum size such that the ratio is 3:7.

Here, the region of interest selection unit may select a square region of interest for a specific coordinate in the selected coordinate group, with the specific coordinate as the center point and having the ratio of the area above the ridge line and the area below the ridge line be 3:7. If there is none, a square region of interest of the minimum size where the specific coordinates are moved by a preset distance in the vertical direction from the center point can be selected.

Here, the region of interest selection unit may select a square region of interest of the minimum size for coordinates included in the mountain peak area among the coordinates in the selected coordinate group, the coordinates of which are moved by a preset distance in the vertical direction upward from the center point. .

In addition, in order to achieve the above object, an apparatus for automatically generating Korean landscape paintings according to the present invention includes a learning data receiver that receives Korean landscape painting image data; a ridge extraction unit that extracts ridge lines from each Korean landscape painting image data received by the learning data receiver; a region of interest selection unit that selects a region of interest based on each coordinate on the ridge line extracted from the ridge extraction unit; a partial image extraction unit for extracting a cropped image of the Korean landscape painting image data corresponding to the area of interest selected by the area of interest selection unit; an artificial intelligence model construction unit that constructs an artificial intelligence model by performing machine learning using the cropped image extracted from the partial image extraction unit; After the artificial intelligence model is constructed by the artificial authentication model building unit, when line image data is received from the user, the received line image data is input into the constructed artificial intelligence model to automatically generate a Korean landscape painting image. It may include a landscape image generating unit.

As described above, according to the present invention, it is possible to secure a significant amount of Korean landscape painting images for machine learning with just a small amount of Korean landscape painting images, and using this, the corresponding Korean landscape painting image is automatically generated when the user draws a simple line. can be created.

1 is a schematic configuration diagram of the entire system including a device for automatically generating Korean landscape paintings according to an embodiment of the present invention;
Figure 2 is a functional block diagram of the automatic Korean landscape painting generation device of Figure 1;
Figures 3 to 7 are diagrams to explain the process of using or generating the Korean landscape painting automatic generation device of Figure 1 in the process of performing its function;
Figure 8 is an overall control flow diagram of an automatic Korean landscape painting generation device according to an embodiment of the present invention;
Figure 9 is a diagram for explaining the processing performed by an automatic Korean landscape painting generation device according to another embodiment of the present invention.

Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

Each embodiment according to the present invention below is only an example to aid understanding of the present invention, and the present invention is not limited to these embodiments. In particular, the present invention may be comprised of a combination of at least one of the individual components, individual functions, or individual steps included in each embodiment.

In particular, for convenience, alphabet letters such as '(a)' are included in some claims, but these alphabet letters do not specify the order of each step.

A schematic configuration of the entire system including the automatic Korean landscape generation device 100 according to an embodiment of the present invention is shown in FIG. 1.

In the drawing, the user terminal 200 inputs basic data for machine learning according to the user's operation or inputs line image data to automatically generate a Korean landscape painting image as described later, for example, provided in the home. It may be a personal computer, or furthermore, it may be a portable wireless communication terminal such as a smartphone.

The Korean landscape painting automatic generation device 100 builds an artificial intelligence model through machine learning at the request of the user terminal 200, and also has a function of automatically generating Korean landscape painting images at the request of the user terminal 200. Perform.

To this end, the Korean landscape painting automatic generation device 100 includes a Korean landscape painting modeling device that builds a model for Korean landscape painting by conducting learning using learning data, and a Korean landscape painting model created in this way to create a Korean landscape painting model on a line entered by the user. It can be functionally divided into a model application device that generates a corresponding Korean landscape painting image. Hereinafter, the integrated state will be explained as an example.

The specific functional blocks of the automatic Korean landscape painting generation device 100 according to an embodiment of the present invention are as shown in FIG. 2.

As shown in the figure, the Korean landscape painting automatic generation device 100 includes a receiving unit 110, a ridge extraction unit 120, a region of interest selection unit 130, a partial image extraction unit 140, and an artificial intelligence model building unit ( 150), and may be configured to include a Korean landscape painting image generator 160.

Here, the receiving unit 110 performs a function of receiving various data received from the user terminal 200.

For example, the receiver 110 can receive image data of Korean landscape paintings needed for machine learning, and can also receive line data corresponding to lines arbitrarily drawn by the user. In other words, the receiver 110 not only functions as a learning data receiver that receives data necessary for machine learning, but also functions as a user input line data receiver that receives a user's arbitrary line during application.

The ridge extractor 120 performs a function of extracting ridge lines from each Korean landscape painting image data received by the learning data receiver 110.

In other words, since Korean landscape painting image data is data about a picture depicting a type of landscape painting, at least one mountain is naturally drawn in the picture, and the ridge extraction unit 120 performs a function of automatically extracting the ridge line of this mountain. .

In order to extract the ridge line in this way, the ridge extractor 120 may use a preset algorithm. For example, the ridge extractor 120 converts each Korean landscape painting image data received from the learning data receiver 110 into grayscale. Convert, perform Gaussian blur filter processing on the image data converted to grayscale for blurring to reduce the noise ratio, and convert the Gaussian blur filter-processed image data to black and white based on preset reference settings. Later, the ridge line can be extracted by applying a predetermined edge detection algorithm.

Figure 3 is a diagram illustrating this process.

For example, when a Korean landscape painting image as shown in FIG. 3(a) is input, the ridge extractor 120 first converts it to grayscale as shown in FIG. 3(b) and then processes it again with Gaussian blur. Convert as shown in Figure 3(c), and based on the standard setting value (for example, brightness standard 170), the darker part becomes completely black (brightness standard 0), and the lighter part becomes completely black. After converting white (brightness standard 255) into a black and white image as shown in Figure 3(d), the ridge line can be extracted using a predetermined edge detection algorithm (for example, Canny Edge Detection), and the ridge line is extracted. The current state is shown in Figure 3(e).

At this time, the ridge extraction unit 120 can only extract ridges that are close to the sky, and this state is shown in FIG. 3(f). In the following description of this embodiment, the ridge line extracted by the ridge extraction unit 120 is shown in FIG. An example is the ridge line at the highest position, as shown in 3(f).

The region of interest selection unit 130 performs the function of selecting the region of interest based on each coordinate on the ridge line extracted by the ridge extraction unit 120.

That is, the region of interest selection unit 130 selects a predetermined region of interest based on the coordinates of each point forming the ridge line. At this time, the region of interest selection unit 130 extracts each point coordinate at regular intervals, and extracts the extracted A region of interest containing point coordinates can be selected.

In particular, the region of interest selection unit 130 includes each coordinate (i.e., point coordinates) on the ridge line extracted from the ridge extraction unit 120, and the ratio of the area above the ridge line and the area below the ridge line is within a preset range. Each area of interest can be selected.

For example, the region of interest selection unit 130 selects coordinate groups having the same preset interval on the ridge line extracted from the ridge extraction unit 120, sets each coordinate within the selected coordinate group as the center point, and sets the area above the ridge line. A square of the minimum size such that the ratio of the area below the ridge line is 3:7 can be selected as each area of interest.

That is, in this case, the location of the midpoint of the square region of interest corresponds to the point coordinates previously extracted on the ridge line.

An example of this is shown in Figure 4, where it can be seen that a square of the minimum size with the coordinates on the ridge line as the center point was selected.

However, if a region of interest cannot be created due to the shape of the ridge line, such that the point coordinates are located at the center and form a square shape, and the ratio of the area above the ridge line to the area below the ridge line within the square is 3:7, the region of interest line The government 130 may select a square of the smallest size whose point coordinates are moved by a preset distance in the vertical direction from the center point of the square as the region of interest.

That is, in this case, the reference point corresponding to the point coordinates on the ridge line is not located at the center point of the square, but the reference point is moved upward or downward from the center point of the square.

For example, the point coordinates on the ridge line can be located at a location corresponding to 80% of the vertical length of the square.

In particular, the area of interest selection unit 130 selects a square of interest of the minimum size for coordinates included in the mountain peak area among the coordinates in the selected coordinate group, such that the coordinates are moved by a preset distance in the upper vertical direction from the center point of the square. You can also select an area.

In other words, referring to FIG. 5, a square located at 80% of the area of interest window based on the mountain peak coordinates can be extracted as the area of interest. At this time, whether it is a mountain peak is determined by calculating the slope at each coordinate and changing the sign of the slope. This can also be done by checking the branch.

Meanwhile, the partial image extraction unit 140 performs a function of extracting a crop image from the Korean landscape painting image data corresponding to the region of interest selected by the region of interest selection unit 130.

An example in which a crop image of Korean landscape painting image data is extracted by the partial image extraction unit 140 is shown in FIG. 6.

In Figure 6, a square with a solid red line corresponds to the area of interest, and a crop image of the Korean landscape painting image data belonging to this area of interest is also displayed in Figure 6.

Although FIG. 6 shows only the crop image extraction process corresponding to three point coordinates for convenience, in reality, it is preferable to extract the crop image based on hundreds to thousands of point coordinates.

Hundreds to thousands of Korean landscape painting images can be extracted from one Korean landscape painting image by this partial image extraction unit 140.

The artificial intelligence model building unit 150 performs a function of building an artificial intelligence model by performing machine learning using the cropped image extracted from the partial image extraction unit 140.

As mentioned earlier, hundreds to thousands of Korean landscape painting image data have been extracted from a single Korean landscape painting image data, so it is possible to secure sufficient data for machine learning from only the few existing Korean landscape painting image data. .

When using an artificial neural network such as deep learning, the artificial intelligence model building unit 150 determines the parameter value of each layer, including the hidden layer, using machine learning, and the parameter value of each layer is determined in this way by artificial intelligence. It can be said to be building an intelligence model.

For example, the artificial intelligence model building unit 150 outputs a virtual Korean landscape painting image by using the ridge line within each region of interest as input in Figure 5, and outputs this virtual Korean landscape painting image and the Korean landscape painting corresponding to each region of interest. Learning can be done by comparing cropped images on image data. For example, machine learning can be done in the method disclosed in the so-called Pix2Pix paper.

Since this machine learning process itself corresponds to known technology, a more detailed description is omitted.

As described above, the Korean landscape image creation unit 160 receives random line image data drawn by the user in the reception unit 110 after the artificial intelligence model is built by the artificial authentication model construction unit. It performs a function of automatically generating Korean landscape painting images by inputting line image data into an already established artificial intelligence model.

For example, when an image (FIG. 7(a)) containing simple lines drawn by the user as shown in FIG. 7 is received, the Korean landscape painting image generator 160 responds to such lines using a pre-built artificial intelligence model. It is possible to output a Korean landscape painting image (Figure 7(b)).

Hereinafter, the overall control flow of the automatic Korean landscape painting generation device 100 according to an embodiment of the present invention will be described with reference to FIG. 8.

First, the Korean landscape painting automatic generating device 100 receives Korean landscape painting image data (step S1) and extracts ridge lines from the received Korean landscape painting image data (step S3).

To extract ridge lines, the Korean landscape painting automatic generation device 100 can use a combination of various methods such as grayscale conversion, Gaussian blur processing, black and white conversion, and edge detection.

Next, the Korean landscape painting automatic generating device 100 selects the area of interest based on the coordinates included in the ridge line (step S5).

At this time, the apparatus 100 for automatically generating Korean landscape paintings may select a region of interest with the minimum size of a square so that the ratio of the area above and below the ridge line within the region of interest is a preset ratio (for example, 3:7).

Next, the Korean landscape painting automatic generating device 100 extracts an image (i.e., crop image) of the Korean landscape painting automatic generating device 100 corresponding to the region of interest (step S7).

For example, if 1,000 crop images are extracted per Korean landscape painting image, a total of 10,000 crop images can be extracted for 10 Korean landscape painting images.

The Korean landscape painting automatic generation device 100 performs machine learning using the plurality of cropped images extracted in this way to build an artificial intelligence model (step S9).

Since the process of building an artificial intelligence model itself corresponds to a known technology, a more detailed explanation is omitted.

After the artificial intelligence model is built in this way, when line image data is received from the user (step S11), the Korean landscape painting automatic generation device 100 inputs the received line image data into the artificial intelligence model (step S13) to create a new Korean landscape painting. An image is automatically created and output (step S15).

Therefore, users can have a diverse and new Korean landscape painting image that maintains the Korean landscape painting style by using only images with random lines drawn.

Meanwhile, in the above-described embodiment, the ridge line is extracted only once to select the region of interest as an example, but the ridge line can be extracted twice to select a new region of interest each and perform crop image extraction and machine learning. .

For example, as mentioned above, when a Korean landscape painting image for machine learning is input, the Korean landscape painting automatic generating device 100 uses the above-mentioned process, for example (i.e., the Korean landscape painting automatic generating device converts it into grayscale). After conversion, Gaussian blur processing is performed to convert, and based on the standard setting value, the darker part is completely black (brightness standard 0), and the lighter part is completely white (brightness standard 0). After converting it into a black and white image using the standard 255), the ridge line can be extracted using a predetermined edge detection algorithm (for example, Canny Edge Detection). At this time, the Korean landscape painting automatic generation device 100 uses the sky and the edge. Only the closest ridge line (i.e., the ridge line at the highest position) (hereinafter referred to as the first ridge line) is extracted and a first machine learning process is performed using this, and then the image between the first ridge line and the second ridge line is extracted. After deleting the second ridge line so that it appears as if it touches the sky, a secondary machine learning process using this second ridge line can be performed.

At this time, if the second ridge lines are not connected to each other by a single line, a pre-connection process may be further included.

This process is shown in Figure 9.

That is, FIG. 9(a) is a diagram showing a state in which the initially extracted ridge line is extracted, and FIG. 9(b) is a diagram showing a state in which only the first ridge line described above is left from the initially extracted ridge line, and FIG. 9 (c) is a diagram showing a state in which broken second ridge lines are connected to each other, and Figure 9(d) shows a state in which only the second ridge line is left by removing the area between the first ridge line and the second ridge line. It is a drawing.

That is, the Korean landscape painting automatic generating device 100 performs machine learning using the first ridge line in FIG. 9(b) and then performs machine learning using the second ridge line in FIG. 9(d). Here, the process of performing machine learning is to select an area of interest based on each coordinate on the currently selected ridge line (i.e., the first ridge line or the second ridge line), and select the Korean area corresponding to the selected area of interest. It may include the process of extracting a cropped image from landscape image data and then performing machine learning using the extracted cropped image to build an artificial intelligence model.

The interconnection of the broken second ridge lines in FIG. 9(c) can be accomplished by performing processing that takes into account the curvature of a straight line or an existing ridge line.

Meanwhile, of course, the process of performing each of the above-described embodiments can be performed by a program or application stored in a predetermined recording medium (eg, computer-readable). Here, recording media include electronic recording media such as RAM (Random Access Memory), magnetic recording media such as hard disks, and optical recording media such as CDs (Compact Disk).

At this time, the program stored in the recording medium can be executed on hardware such as a computer or smartphone to perform each of the above-described embodiments. In particular, at least one of the functional blocks of the apparatus for automatically generating Korean landscape paintings according to the present invention described above may be implemented by such a program or application.

In addition, the present invention is not limited to the specific embodiments described above, but can be implemented with various changes and modifications without departing from the gist of the present invention. It will be apparent that such changes and modifications are included in the present invention if they fall within the scope of the appended claims.

100: Korean landscape painting automatic generation device 200: User terminal
110: Receiving unit 120: Ridge extraction unit
130: Region of interest selection unit 140: Partial image extraction unit
150: Artificial intelligence model construction unit 160: Korean landscape painting image creation unit

Claims (16)

(a) receiving Korean landscape painting image data;
(b) extracting ridge lines from each Korean landscape image data received in step (a);
(c) selecting a region of interest based on each coordinate on the ridge line extracted in step (b);
(d) extracting a crop image from the Korean landscape painting image data corresponding to the region of interest selected in step (c);
(e) A control method of a Korean landscape painting modeling device, comprising the step of constructing an artificial intelligence model by performing machine learning using the cropped image extracted in step (d). According to paragraph 1,
In step (b),
(b1) converting each Korean landscape image data received in step (a) into grayscale;
(b2) performing Gaussian blur filter processing on the grayscale converted image data of step (b1) for blurring to reduce the noise ratio;
(b3) converting the Gaussian blur filter-processed image of step (b2) to black and white based on a preset reference setting value;
(b4) A control method for a Korean landscape painting modeling device, comprising the step of extracting ridge lines by applying a preset edge detection algorithm to the black-and-white conversion image generated in step (b3). According to paragraph 1,
In step (c), a region of interest is selected that includes each coordinate on the ridge line extracted in step (b), and the ratio of the area above the ridge line to the area below the ridge line is within a preset range. Control method of Korean landscape painting modeling device. According to paragraph 3,
In step (c),
(c1) selecting coordinate groups having preset equal intervals on the ridge line extracted in step (b);
(c2) selecting a square region of interest of the minimum size such that each coordinate in the selected coordinate group is used as the center point and the ratio of the area above the ridge line to the area below the ridge line is 3:7. Control method of Korean landscape painting modeling device. According to paragraph 4,
In step (c2), for a specific coordinate in the selected coordinate group, a square region of interest cannot be selected such that the specific coordinate is the center point and the ratio of the area above the ridge line and the area below the ridge line is 3:7. In this case, a control method of a Korean landscape painting modeling device, characterized in that a square area of interest of the minimum size in which the specific coordinates are shifted 20% in the vertical direction from the center point is selected. According to paragraph 4,
In step (c2), for coordinates included in the mountain peak area among the coordinates in the selected coordinate group, a square region of interest of the minimum size is selected where the coordinates are shifted 20% in the vertical direction upward from the center point. Control method of landscape modeling device. (a) receiving Korean landscape painting image data;
(b) extracting ridge lines from each Korean landscape image data received in step (a);
(c) selecting a region of interest based on each coordinate on the ridge line extracted in step (b);
(d) extracting a crop image from the Korean landscape painting image data corresponding to the region of interest selected in step (c);
(e) constructing an artificial intelligence model by performing machine learning using the cropped image extracted in step (d);
(f) After step (e), when line image data is received from the user, automatically generating a Korean landscape painting image by inputting the received line image data into the artificial intelligence model created in step (e). A control method for an automatic Korean landscape painting generation device comprising: A computer-readable recording medium recording a program for executing the method of any one of claims 1 to 7. An application program stored in a computer-readable recording medium in combination with hardware to execute the method of any one of claims 1 to 7. a learning data receiving unit that receives Korean landscape painting image data;
a ridge extraction unit that extracts ridge lines from each Korean landscape painting image data received by the learning data receiver;
a region of interest selection unit that selects a region of interest based on each coordinate on the ridge line extracted from the ridge extraction unit;
a partial image extraction unit for extracting a cropped image of the Korean landscape painting image data corresponding to the area of interest selected by the area of interest selection unit;
A Korean landscape painting modeling device comprising an artificial intelligence model construction unit that constructs an artificial intelligence model by performing machine learning using the cropped image extracted from the partial image extraction unit. According to clause 10,
The ridge extractor converts each Korean landscape painting image data received from the learning data receiver into grayscale, performs Gaussian blur filter processing on the grayscale converted image data for blur processing to reduce the noise ratio, and the Gaussian blur filter process A Korean landscape painting modeling device characterized by converting a thin blur filter-processed image to black and white based on preset reference settings and then extracting ridge lines by applying a preset edge detection algorithm. According to clause 10,
The region of interest selection unit includes each coordinate on the ridge line extracted from the ridge extraction unit, and selects each region of interest such that the ratio of the area above the ridge line and the area below the ridge line falls within a preset range. Korean landscape painting modeling device. According to clause 12,
The region of interest selection unit selects coordinate groups having the same preset interval on the ridge line extracted from the ridge extraction unit, sets each coordinate in the selected coordinate group as the center point, and sets the ratio of the area above the ridge line to the area below the ridge line. A Korean landscape painting modeling device characterized by selecting each square area of interest with a minimum size of 3:7. According to clause 13,
When the region of interest selection unit cannot select a square region of interest for a specific coordinate in the selected coordinate group, with the specific coordinate as the center point and the ratio of the area above the ridge line to the area below the ridge line being 3:7. A Korean landscape painting modeling device, characterized in that a square area of interest of the minimum size in which the specific coordinates are moved by a preset distance in the vertical direction from the center point is selected. According to clause 13,
The region of interest selection unit is characterized in that, for coordinates included in the mountain peak area among the coordinates in the selected coordinate group, a square region of interest of the minimum size in which the corresponding coordinates are moved by a preset distance in the vertical direction upward from the center point is selected. Korean landscape painting modeling device. a learning data receiving unit that receives Korean landscape painting image data;
a ridge extraction unit that extracts ridge lines from each Korean landscape painting image data received by the learning data receiver;
a region of interest selection unit that selects a region of interest based on each coordinate on the ridge line extracted from the ridge extraction unit;
a partial image extraction unit for extracting a cropped image of the Korean landscape painting image data corresponding to the area of interest selected by the area of interest selection unit;
an artificial intelligence model construction unit that constructs an artificial intelligence model by performing machine learning using the cropped image extracted from the partial image extraction unit;
After the artificial intelligence model is constructed by the artificial authentication model building unit, when line image data is received from the user, the received line image data is input into the constructed artificial intelligence model to automatically generate a Korean landscape painting image. An automatic Korean landscape painting generation device comprising a landscape painting image generator.