KR20200006764A - A method calculating view score of each household - Google Patents

Abstract

The present invention relates to a method for calculating a perspective score for each household which is a method for calculating a perspective score for each household. The method comprises the steps of: (a) generating a perspective image of a perspective viewed from a household; (b) mapping the perspective image by one or more colors; (c) counting the number of pixels for each color mapped on the perspective image; and (d) operating the number of pixels for each color counted in (c) and a score for each preset color by inputting the number of pixels and the score in a predetermined equation as a variable.

Description

A method calculating view score of each household}

The present invention relates to a method for calculating the perspective score for each household generation, generating a view image of the view viewed from the housing generation, mapping the feature displayed in the view image to a predetermined feature color, and mapping the number of pixels of the feature color to the feature. The method relates to a method of calculating a view score for a housing generation by counting a predetermined score for each color and a counting number of pixels for each color as a variable in a predetermined formula.

Increasing the price of real estate due to the densely populated city has led to an increase in the legal capping rate of multi-family housing, which has resulted in a much worsening residential environment as the gap between apartments has narrowed. This is causing legal disputes due to violation of human rights.

Due to this view, the real estate value of a housing unit with a good view, even if it is a multi-family house, a side-by-side apartment house or the same multi-family house in the same area, will be considerably greater than that of a less-suited housing unit.

In such a situation, to estimate the value of real estate by quantifying the view of each household, the value of the view of the housing generation is reflected, or legal disputes are prevented or used to resolve disputes in case of legal disputes. There is a lot of research that attempts to score actual perspectives by generation.

Housing household view scores are displayed by scoring the percentage of each feature of green areas, buildings, land, sky, and rivers. .

Types of housing generation views include mountain view, river view, park view, ocean view, lake view, island view, Golf course with green views.

In this regard, look at the prior art Republic of Korea Patent Registration No. 10-1670793.

The view rate calculation and analysis method according to the related art includes a first process of setting a view test point for looking at a surrounding building from an elevation of a test building, and a virtual extension line from the view test point toward each vertex of the surrounding building. A second process of generating, a third process of generating an effective extension line by removing unnecessary extension lines that are obscured by the surrounding building among the extension lines generated in the second process, and an outer polygon segment using the effective intersection points among the vertices ( A fourth process of generating a polyline) and an outer surface, a fifth process of generating an intersection line using the outer surface and a three-dimensional virtual sphere, and a perspective test using a shielded surface shielded by the surrounding building And a sixth process of calculating the viewing rate according to the point.

However, according to the related art, as a method of calculating the view ratio except the view shielded from the neighboring building as the view ratio, the view other than the building is regarded as the same view and the view rate is calculated. There is a disadvantage that does not substantially reflect the value of.

That is, as described above, there are various views, and each of these various views provides a view of different values, and the value according to the actual view is limited by calculating the view ratio by limiting the various views to the building view and the non-building view. There is a disadvantage that does not reflect.

In order to solve the problems according to the prior art described above, a method for calculating a score for the overall view after dividing and quantifying the various views into respective views so that the actual value of the various views viewed by the housing generation can be reflected. I would like to.

Furthermore, the present invention aims to provide a view score calculation method that can simplify in assigning view scores to various views by quantifying only views that can be easily understood from the eyes of people rather than scores for all views.

In order to solve the problems according to the prior art described above, the method for calculating the perspective score according to the present invention includes the steps of: (a) generating a perspective image for the view viewed from the housing generation; (b) mapping the view image into one or more colors; (c) counting the number of pixels for each color mapped on the view image; And (d) calculating the number of pixels for each color counted in the step (c) and a predetermined score for each color as a variable in a predetermined equation.

Preferably, in the step (b), the color for each feature is mapped to the feature displayed in the view image.

Preferably, the step (b) comprises the steps of: (b-1) lattice the view image into a predetermined unit cell; (b-2) partitioning the view image for each feature displayed in the view image; (b-3) calculating the number of unit cells included in each feature partitioned in step (b-1); (b-4) A feature in which the number of unit cells calculated in step (b-3) is greater than or equal to a predetermined number is recognized as a predetermined feature, and a feature less than a predetermined number is a predetermined number of unit cells. Recognized as another nearby feature that is greater than or equal to the number of; And (b-4) each of the unit cells constituting the feature recognized as the preset feature in step (b-3) is mapped to a preset feature-specific color.

As a means for solving the problems described above, the actual value of the various views viewed by the housing generation can be reflected, and the overall view score can be calculated after dividing the various views into the respective views. You can quantify the value of the view as closely as possible.

In addition, various views may be reflected in the view score, but by absorbing a view within a certain range to a view above a certain range, only the prominent view that catches most eyes may be reflected in the view score to simplify the view score as much as possible.

1 is a flowchart of an embodiment of a method for calculating a perspective score according to the present invention.
2 is a flowchart of another embodiment of a method for calculating a perspective score according to the present invention.
3 is a diagram illustrating an example of a view image that is a target of a view score calculation method according to the present invention.

Hereinafter, preferred embodiments of the method according to the present invention will be described with reference to the accompanying drawings. In this process, the thickness of the lines or the size of the components shown in the drawings may be exaggerated for clarity and convenience of description. In addition, terms to be described below are terms defined in consideration of functions in the present invention, which may vary according to the intention or custom of a user or an operator. Therefore, the definitions of these terms should be made based on the contents throughout the specification.

An embodiment according to the present invention will be described with reference to FIGS. 1 and 3.

S100: step of generating a view image of the view from the housing generation

A view image of the view from the housing generation is created. Housing households include both individual housing generations of multi-family housing, individual housing generations of multi-family buildings, and housing generations of single-family homes.

The view seen from the housing generation may be the view seen from the window located in the housing generation. If multiple windows are located in a residential household, it may be the view seen through the window that has the highest viewing score as a result.

The view image for the view may be any image as long as it is an image that can be imaged, that is, an image that visually expresses the view.

Of course, the angle of view from the window can be preset. It may be a view viewed vertically, or may be viewed from an inclined state at an angle. In determining the view score, it is of course possible to generate a view image of the view viewed from a predetermined angle.

In the view image of the view viewed through the window, the size of the view image may be determined based on the window. For example, it may be a view viewed through a partitioned area marked 1m wide and 1m long displayed on the window at a distance of 30cm from the window. In this case, the part partitioned in the window may be selected to be the most visible part of the window.

Referring to FIG. 3, rivers, surrounding buildings, sky, and landmarks appear in the view image viewed through the window. In FIG. 3, the main features are displayed, and the features that do not affect other views are removed. This is to simplify the calculation of the view score of the view image.

S200: Step in which the view image is mapped to one or more colors

The generated view image is mapped to one or more colors. Colors are mapped for each feature on the view image.

Features can be preset by limiting the main features that affect the landscape (for example, sky, rivers, landmarks, surrounding buildings, etc.). For example, nearby intersections, etc. can be assigned to the main features that affect the view. If not applicable, it may not correspond to a predetermined feature.

The color may be preset for each of the predetermined features. Referring to FIG. 3, which is an example, the sky is a predetermined feature, and a part corresponding to the sky on the view image is a predetermined feature as the sky, and a part corresponding to the river is a river, and the buildings located around the surrounding building are neighboring buildings. As a predetermined feature, the feature of the sky may be preset in a color called green. Thus, referring to FIG. 3, green corresponding to the sky, which is a predetermined feature, is mapped to a portion corresponding to the sky, landmarks are yellow, rivers are blue, and surrounding buildings are mapped red.

The part recognized as the main feature of the sky, the river, the landmark, the surrounding building, etc. allows the preset color to be mapped to each of the recognized features. If the main feature is not a preset feature or if the feature is included in the view image, the part included in the view is insignificant and cannot be affected by the view. Colors can be mapped in the water. The purpose is to simplify the calculation in calculating the view score by providing view scores for the required features, but not providing the view scores for unnecessary views that have little effect on the view.

As an aspect of the means for mapping the color for each feature, the color may be mapped for each feature on the view image by hand.

In another aspect, by using a computer, a character representing a feature is described in a section partitioned by feature, and then, the written character is recognized by a computer, and the character corresponding to a predetermined feature corresponds to a recognized character. The predetermined color may be mapped. The character recognition program may be any software, but may be recognized through template matching and character recognition algorithms, which are image processing algorithms, and corresponding colors may be mapped to portions separated by spaces that mean characters. Can be. In the above-described example, the part divided by the space in which the sky is displayed is given green color, which is a predetermined color corresponding to the sky, and the corresponding part is mapped to green color. This color mapping can be any software, but can preferably be mapped using a flood fill algorithm.

In another aspect, it is of course possible to use any means capable of mapping the color of the feature to the landscape image in addition to the manual or computer means.

Color overlap occurs. For example, while the color corresponding to the sky is mapped, the color corresponding to the landmark may also be mapped. In this case, the color overlapping portion may be mapped to a color corresponding to a character representing a feature occupying a wider space. In addition to the overlapping parts, the number of pixels for each color mapped to the parts is counted to give priority to colors that occupy a larger space, and to map the overlapping parts to the preferred colors. Larger spaces appear wider than actual spaces, and narrower spaces are intended to simplify visual scores by simplifying these visualizations and visual phenomena that do not fit into visual perspectives.

S300: counting the number of pixels for each color mapped on the view image

One or more colors are mapped onto the view image, and the predetermined colors for each feature may be mapped. Thus, referring to FIG. 3, the number of green pixels, which are colors mapped to a part corresponding to the sky, the number of yellow pixels mapped to a part corresponding to a landmark, and the like may be counted. The size of the pixel is a predetermined size, so the number of pixels for each color and the area of the color mapped portion are proportional to each other. In FIG. 3, the number of green pixels mapped to a portion corresponding to the sky is greater than the number of pixels of a landmark having an area smaller than that of the sky.

The preset color is mapped to each feature only for the main feature, and the number of pixels constituting the mapped color is counted. As described above, the larger the number of pixels, the larger the area occupied by the feature in the view image.

S400: step of inputting a score for each color and the number of pixels for each color as a variable in a predetermined formula

The main features affecting the landscape are preset, so that the feature appearing in the view image is determined first as the preset feature, and if the feature appearing in the view image is the preset feature, Since the set color is mapped, the score is preset for each preset color. For example, referring to FIG. 3, a score for each color may be preset such that the green score, which is a color corresponding to the sky, is 10 points, and the yellow score, which is a landmark, is 7 points.

The predetermined score for each color and the number of pixels for each color counted are input as variables in a predetermined formula. The number of pixels for each color and the number of pixels for each color on the view image are input and ground in a predetermined equation as a variable, and the calculated result value may be a view score.

For example, referring to FIG. 3, the green score corresponding to the sky is 10 points, the yellow score corresponding to the landmark is 7 points, the blue corresponding to the river is 5 points, and the surrounding buildings are -5 points. If the number of pixels constituting the green is 500, the number of pixels constituting the yellow is 30, the number of pixels constituting the blue is 120, and the number of pixels constituting the red is 400, FIG. The viewing score corresponding to may be (10 * 500) + (7 * 30) + (5 * 120) + (-5 * 400) = 3810. The above examples are merely examples, and do not limit the present invention.

Of course, the predetermined formula may be determined in consideration of the living environment or which view has a higher value, and of course, if the number of pixels in the color score is rather high, the view value may be preset to decrease the view value. to be.

S500: Step of calculating the viewing score for the viewing image

The score for each color and the number of pixels for each color are input to a predetermined formula and calculated. The result value calculated as described above may be a viewing score. In the predetermined formula, the above-described score for each color and the number of pixels for each color may be input as variables in a state adjusted according to the living environment or the value of the view.

2 and 3, but another embodiment according to the present invention that can be carried out in the above-described step S200 will be described.

For example, as shown in FIG. 3, the sky, the river, the landmark, and the surrounding buildings correspond to the main features that affect the view area. In addition, it may include a feature that is not a predetermined main feature, which corresponds to a predetermined main feature but has a small portion, or has little influence on the view.

S200-1: step of lattice the view image into a predetermined unit cell

The generated view image is gridized into unit cells having a predetermined size. The size of the unit cell is determined in consideration of the resolution, and may be a unit cell having a size that may appear to be smoothly divided by colors when the color between the unit cell and the unit cell is different.

S200-2: step of partitioning the view image by the features displayed on the view image

Referring to FIG. 3, the view image includes sky, rivers, landmarks, and main features of surrounding buildings. The landscape image is divided and divided into these predetermined main features.

S200-3: step of counting the number of unit cells included for each partitioned feature

In the state partitioned by feature on the view image, the number of unit cells of the portion occupied by each feature is counted.

S300-4: Recognizing a preset feature only when the number of counted unit cells is greater than or equal to a predetermined number, and recognizing the other feature as a nearby feature recognized as the preset feature when less than the predetermined number

If the number of counted unit cells is less than the predetermined number, the effect on the view is considered to be insignificant. As described above, a very narrow space is compared with a very large space to reflect visual phenomena that do not enter the visual view, and to simplify the calculation of the view score as much as possible by removing them. Therefore, when the number of counted unit cells is less than the predetermined number, it is recognized as another nearby feature recognized as a predetermined feature. Referring to Figure 3, even if there is a very narrow range of forest around the surrounding buildings buried in the surrounding buildings do not enter the visual view, located in the neighborhood is located in a state consisting of a certain number of unit cells or more It can be recognized as a surrounding building in a state of being included in the surrounding building.

When the number of counted unit cells is equal to or greater than a predetermined number, it is recognized as one of the predetermined features.

When the number of counted unit cells is greater than or equal to a predetermined number, it is necessary to preset the feature to correspond to the preset feature. Accordingly, when a feature is preset in consideration of the living environment or the value of each view, a feature that occupies a large portion but a feature that is not preset will be an exception, and in such a case, mapping to a predetermined color Of course, it may not be reflected in the calculation of the view score.

S200-5: step in which the unit cell constituting the feature recognized as the preset feature is mapped to the preset feature-specific color

After the view image is recognized and classified as a predetermined feature, the color of each feature is mapped to the unit cell, so that the color of the entire view image is mapped to each feature.

In the above specification, the present invention has been described with reference to the embodiments shown in the drawings so that those skilled in the art can easily understand and reproduce the present invention, which is merely exemplary, and those skilled in the art can make various modifications and equivalents from the embodiments of the present invention. It will be appreciated that embodiments are possible. Therefore, the protection scope of the present invention will be defined by the claims.

Claims (3)

In the method of calculating the viewpoint of each household generation,
(a) generating a view image of the view viewed from the housing generation;
(b) mapping the view image into one or more colors;
(c) counting the number of pixels for each color mapped on the view image; And
and (d) calculating the perspective score by inputting the number of pixels for each color counted in the step (c) and a predetermined color score as a variable in a predetermined equation.
The method of claim 1,
In step (b),
Viewing point calculation method for mapping the color of the predetermined feature to the feature displayed in the view image.
The method of claim 2,
Step (b),
(b-1) lattice the view image into a predetermined unit cell;
(b-2) partitioning the view image for each feature displayed in the view image;
(b-3) calculating the number of unit cells included in each feature partitioned in the step (b-1);
(b-4) The feature having the number of unit cells calculated in the step (b-3) is greater than or equal to the predetermined number is recognized as a predetermined feature, and the feature less than the predetermined number is the predetermined number of unit cells. Recognized as another nearby feature that is greater than or equal to the number of; And
(b-4) A method for calculating a perspective score, the method comprising: mapping each of the unit cells constituting a feature recognized as a feature to be preset in step (b-3) to a color for each feature.

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