JPH10198712A - Method for recognizing oblique wall from construction drawing, and device for recognizing construction plan - Google Patents

Abstract

PROBLEM TO BE SOLVED: To precisely recognize the contour or skeleton and especially an oblique wall inclined horizontally or vertically being information important for knowing layout from a construction plan. SOLUTION: The image area composed of black or white dots in the image picture data of construction plan read by an image reading part 2 is limited by an oblique wall recognizing part 13, a skew angle entirely shown by black or white dots in the image picture data of that limited image area is found, dot number measurement arrangement data are prepared by measuring the number of black or white dots in the image picture data of this limited image area toward that found skew angle and based on these prepared dot number measurement arrangement data, the oblique wall forming one part of contour or skeleton on the construction drawing is recognized.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to the recognition of walls as outlines and skeletons in construction drawings widely used in the construction industry such as the construction and equipment industries, and more particularly, to oblique walls inclined with respect to the horizontal or vertical direction. And a construction drawing recognition apparatus having the function.

[0002]

2. Description of the Related Art In recent years, CAD systems and the like have been used to easily create construction drawings including houses and buildings or piping drawings of water pipes, gas pipes, power / communication cables, etc. Is stored and used in the case of design change or extension / remodeling. However, the data of the construction drawing is not compatible due to the created system, and cannot be used due to elapse of the period or change of the contractor. In addition, when renovating or renovating a house, there are many cases where only old construction drawings are drawn on paper, and all the drawings, including those that do not change the floor plan of the renovation, must be redrawn.

[0003] Therefore, it has been attempted to read a construction drawing drawn on paper and recognize and store the data as data that can be processed by a computer. However, there is no special method or apparatus for that, and the construction drawing is image-scanned. And input the image data to a personal computer or the like, and perform line segment recognition and pattern recognition using a general graphic recognition function. Alternatively, the graphic recognition function can be upgraded by using a high-performance graphic editor to assist, and even if the automatic recognition function is somewhat imperfect, basic vector diagrams such as straight lines and arcs can be converted by raster-to-vector conversion, for example. Some of them can be automatically recognized.

[0004] However, such a conventional drawing recognition apparatus requires a high degree of operation knowledge and the like, and its use is limited to persons who are accustomed to using a personal computer or the like or a specialized operator, and there is an industry relation that frequently uses construction drawings. Was not always easy to use.

Although a basic diagram such as a straight line or an arc can be automatically recognized, a graphic symbol (for example, a wall or a column) unique to a construction drawing composed of a combination of the basic diagram and the like is individually recognized. I couldn't do that. Therefore, when the recognized drawing is corrected, it must be performed for each line segment, which requires much labor.

Further, in construction drawings of houses, buildings, and the like used in the construction industry, even if each line segment on the drawing appears to the human eye as a continuous line of the same size, if viewed strictly, the thickness is also one. The trajectory is not fluctuating. In addition, even if it should be a continuous line, it may be broken in some places. These imperfections occur not only at the time of drawing, but also due to aging of paper, errors in reading, and the like. Therefore, for example, if the limit of the thickness that can be recognized as a line segment is too small, it is recognized that the line segment is broken even with a slight blur.
Also, thick line segments may be recognized as rectangles.

[0007] This is of course not only the quality of the original drawing, but also many drawings with low contrast (the boundary between black and white is not clear) like a so-called blueprint using photosensitive paper.
Furthermore, since the fine points characteristic of the blue printing appear on the surface, it is difficult to perform highly accurate automatic recognition with a conventional drawing recognition device, and the correction requires a lot of trouble, so that it is hardly practical. Was.

By the way, in the construction drawing of a house (architectural drawing), a wall partitioning a floor plan plays a central role in the drawing, and by recognizing which part in the drawing is a wall, the approximate drawing of the construction drawing is made. I can understand. Therefore, recognizing the position and length of the wall in the drawing is the most important matter in recognizing the construction drawing.

Furthermore, most construction drawings are floor plans separated by horizontal and vertical profiles and skeleton walls. Therefore, focusing on the wall, many walls and partitions have a horizontal and vertical linear shape. However, some walls and partitions may have a linear shape (oblique wall) inclined obliquely to the horizontal or vertical direction, or may have a shape that draws an arc. Therefore, in order to recognize the construction drawing, it is necessary to surely recognize all the walls including the “diagonal wall”.

[0010]

However, the conventional drawing recognition method has the following problems. (1) In the contour tracking method, if the contour of a wall or the like has much blur or unevenness, it is recognized as a broken straight line, or is recognized as a straight line divided into two or three, and cannot be accurately recognized. (2) In the projected feature extraction method obtained from the frequency distribution of black dots, when a wall or partition line is inclined with respect to the horizontal and vertical directions, feature extraction cannot be performed, and a wall-like straight line cannot be recognized.

SUMMARY OF THE INVENTION The present invention has been made in view of the above point, and is based on image image data obtained by reading a construction drawing drawn on paper or encoded image data obtained by encoding the run length of the construction drawing. To provide a method for accurately recognizing a wall forming a contour and a skeleton, which is important information for knowing a floor plan, particularly a diagonal wall, and a construction drawing recognition device having a function of automatically recognizing the diagonal wall. With the goal.

[0012]

SUMMARY OF THE INVENTION In order to achieve the above object, the present invention provides the following method and apparatus for recognizing an oblique wall from a construction drawing. A method for recognizing an oblique wall from a construction drawing according to the present invention limits an image region consisting of black or white dots of image image data obtained by reading an image of a construction drawing, and sets black or white in the image image data of the limited image region. A skew angle indicated by the white dots as a whole is obtained, and in the obtained skew angle direction, the number of black or white dots of the image image data of the limited image area is measured, and dot number measurement array data is created. The oblique wall forming a part of the outline and skeleton of the construction drawing is recognized based on the obtained dot number measurement array data.

In the method for recognizing an oblique wall from a construction drawing, the number of black or white dots in the horizontal and vertical directions of the image data obtained by reading the image of the construction drawing is measured, and the horizontal and vertical dot number measurement array data is obtained. Based on the created horizontal and vertical dot number measurement array data, the outline and skeleton of the construction drawing are recognized, and based on the recognized outline and skeleton, the image is composed of black or white dots of the image data. It is preferable to limit the image area.

According to another aspect of the present invention, there is provided a construction drawing recognizing apparatus, comprising: image data input means for inputting image image data obtained by reading an image of a drawing; and black or white in the horizontal or vertical direction of the image image data input by the means. Dot number measurement array data creating means for measuring the number of dots and creating horizontal and vertical dot number measurement array data,
There is provided a contour / skeleton recognizing means for recognizing the outline and skeleton of the construction drawing based on the horizontal and vertical dot number measurement array data created by the means.

Further, based on the outline and skeleton of the construction drawing recognized by the outline / skeleton recognition means, the image area of the input image image data is limited, and the image image data of the limited image area is defined. Obtain the skew angle indicated by the black or white dots as a whole, create the dot number measurement array data by measuring the number of black or white dots of the image image data of the limited image area in the direction of the skew angle, and create the dot number measurement array data. And an oblique wall recognizing means for recognizing an oblique wall forming a part of the outline and skeleton of the construction drawing based on the obtained dot number measurement array data. The image data input means may be any image reading means for reading an image of a construction drawing and inputting the image data.

Alternatively, image data input means for inputting encoded image data obtained by encoding the run length of image image data obtained by reading an image of a construction drawing as image data input means is provided, and the dot number measurement array data creating means is provided. Alternatively, the number of horizontal or vertical dots of black or white of the original image image data may be measured from the input encoded image data to create the horizontal or vertical dot number measurement array data.

It is preferable that these construction drawing recognition apparatuses are provided with display means for displaying the result of recognition by the above-mentioned oblique wall recognition means. The display means preferably has means for displaying the image data input by the image data input means simultaneously with the recognition result. Further, it is preferable that the means for simultaneously displaying the image data is a means for displaying the image data and the recognition result in a superimposed manner.

Alternatively, there may be provided display selection means for changing display contents so as to selectively display the image image data input by the image data input means and the recognition result. Also, the operator confirms the above recognition result,
It is desirable to provide a recognition result confirming means for determining the recognition result by an external instruction.

[0019]

Embodiments of the present invention will be specifically described below with reference to the drawings. FIG. 1 is a block diagram showing a schematic configuration of an example of a construction drawing recognition apparatus for implementing a method for recognizing an oblique wall from a construction drawing according to the present invention, showing a hardware configuration and a function of software processing by a microcomputer in a mixed manner. ing.

This apparatus comprises an overall control unit 1, an image reading unit 2, a communication control unit 3, a memory 4, an automatic skew correction unit 5,
Dot number measurement array data creation unit 6, contour / skeleton recognition unit 7, remapping control unit 8, display unit 9, operation input unit 1
0, external storage device 11, printing device 12, oblique wall recognition unit 1
3 and a bus 14 for connecting them. It should be noted that interface units required between these units (or devices) and the bus 14 are not shown.

The overall control unit 1 includes a microcomputer (C) for controlling the operation and functions of the entire construction drawing recognition apparatus.
PU, ROM, RAM, etc.
PU ”), the automatic skew correction unit 5 and the dot number measurement array data creation unit 6 according to the present invention.
Each function of the outline / skeleton recognition unit 7, the remapping control unit 8, and the oblique wall recognition unit 13 can also be realized by software processing of the CPU.

The image reading section 2 is an image data input means for scanning a set construction drawing such as an architectural drawing, reading the image, and inputting image image data, and includes a scanning optical system and an image sensor such as a CCD. This is a known image scanner including the driving circuit and the like. It also includes a circuit for binarizing the read image image data at a predetermined resolution to obtain white dot and black dot image data.

The communication control unit 3 receives image data or an encoded image data obtained by encoding the run-length of the image data from outside through an external communication, instead of taking in the image data from the image reading unit 2. In addition to the means, the outline and skeleton data of the construction drawing recognized by this device can be transmitted to an external device. Specifically, it includes a FAX modem and personal computer communication control means.

The memory 4 stores image image data read by the image reading unit 2, image image data or encoded image data received by the communication control unit 3,
Image data skew-corrected by the automatic skew correction unit 5, dot-number measurement array data created by the dot-number measurement array data creation unit 6, contour and skeleton recognition results recognized by the outline / skeleton recognition unit 7, oblique walls This is a large-capacity RAM or a hard disk memory that stores the oblique wall recognition result recognized by the recognition unit 13 and the image data and the like re-mapped by the re-mapping control unit 8.

The automatic skew correction unit 5 corrects the angle of the image data stored in the memory 4 so that the horizontal and vertical line segment directions match the horizontal and vertical reference directions of the apparatus. Yes, a known automatic skew correction technique can be used. The image data corrected by the automatic skew correction unit 5 is stored in the memory 4 again.

The number-of-dots-measurement-array-data creating unit 6 converts the image data or the encoded image data stored in the memory 4 after the automatic skew correction and the image data re-mapped by the re-mapping control unit 8 described later. On the other hand, the image data is limited to two directions of the horizontal and vertical directions, and the number of black or white dots is measured (counted) for each dot width unit. (Histogram) and stores it in the memory 4.

If the read construction drawing is a positive drawing (a drawing whose brightness is lower than the brightness of the ground), the number of black dots is measured, and a negative drawing (a drawing whose brightness is higher than the brightness of the ground) is measured. In this case, the number of white dots is measured.

The outline / skeleton recognition unit 7 recognizes the outline and skeleton of the construction drawing based on the horizontal and vertical dot number measurement array data created by the dot number measurement array data creation unit 6, and particularly recognizes the wall. Outline / skeleton recognition means for extracting wall data such as position, length, thickness, type, etc.
The details will be described later.

If it is difficult or uncertain to recognize (extract) a wall from the referenced dot number measurement array data,
A request for resetting the creation range of the number-of-dots measurement array data in the horizontal and vertical directions of the drawing is sent to the overall control unit 1, the creation range is changed and set, and the number-of-dots measurement array data creation unit 6 creates the number-of-dots measurement array data again. Let it.

The remapping control unit 8 limits the range of the construction drawing by the part recognized as a wall by the contour / skeleton recognition unit 7 and remaps the part in consideration of other recognition information. Then, image data for causing the dot number measurement array data creation unit 6 to create dot number measurement array data again for each of the limited ranges is created.
At this time, image data is created in which noise of the original or noise read by the image reading unit 2 is also removed. This data is also stored in the memory 4.

The oblique wall recognizing section 13 is provided with a contour / skeleton recognizing section 7.
Starting from the intersection or end point of the horizontal and vertical walls extracted as described above, the skew that represents the entirety of black or white dots (such as straight lines and isolated islands) of the image data stored in the memory 4 The angle is obtained, the number of black or white dots is measured in the obtained direction, dot number measurement array data in the oblique direction is created, and the outline and skeleton of the construction drawing are created based on the created dot number measurement array data in each direction. A process for recognizing (oblique wall) is performed. The image data whose outline has been extended by this recognition is stored in the memory 4 again.
That is, the diagonal wall recognition unit 13 is a means for recognizing a contour and a skeleton forming a linear shape in the diagonal direction of a portion composed of black or white dots of the image data as "diagonal walls", and details thereof will be described later. I do.

The display unit 9 receives the image data of the construction drawing input from the image reading unit 2 or the communication control unit 3 and skew-corrected by the automatic skew correction unit 5 and the horizontal data generated by the dot number measurement array data generation unit 6. And the number of black or white dot count measurement array data in the vertical direction, the wall data recognized by the contour / skeleton recognition unit 7, the data of the diagonal wall recognized by the diagonal wall recognition unit 13, and re-mapped by the re-mapping control unit 8. For example, a CRT or a liquid crystal display is used for displaying the image data.

FIGS. 2 and 3 show examples of the display state of the screen 9a of the display unit 9. FIG.
And a table of image data (recognition result) obtained by remapping the data of the outline and the skeleton (the wall W including the oblique wall W ′ in this example) of the construction drawing recognized by the oblique wall recognition unit 13 by the remapping control unit 8. This is an example. In this display example, double solid lines indicate both sides of the wall, and thin lines indicate the core line of the wall and its extension.

FIG. 3 shows the image data of the construction drawing (input image data) skew-corrected by the automatic skew correction section 5 and the image data of the wall, which is the re-mapped recognition result, simultaneously superimposed and displayed. An example is shown below. In this case, the image data of the skew-corrected construction drawing is displayed in halftone (in FIG. 3, it is indicated by stippling for convenience of illustration) so that the two can be easily identified. The image data is displayed as a solid line.

When the display unit 9 is a color display device, the discrimination can be improved by displaying both images in different colors. For example, by displaying the skew-corrected input image image data in light blue and displaying the recognition result image data in orange or green, the operator can easily identify the recognition result portion.

Alternatively, the screen 9a of the display unit 9 can be divided, and the skew-corrected input image data and the image data of the recognition result can be displayed in comparison with each of the divided screens. Alternatively, the skew-corrected input image data and the remapped image data of the recognition result may be selectively displayed on the same screen. In that case, display selection means (keys and the like) may be provided in the operation input unit 10 described later.

Further, the display section 9 also displays a screen for the operator to confirm the recognition result. That is, the image data of the re-mapped recognition result is displayed, and a display such as "Is this recognition result OK? (YES / NO)" is performed. This allows the operator to confirm whether the wall has been correctly recognized.

The operation input unit 10 is for inputting various operation instructions, function selection commands, edit data, and the like, and is a keyboard, a mouse, a touch panel, or the like. The operation input unit 10 also has a function as a display selection unit,
The display state of the display unit 9 can be changed to a display state desired by the operator. For example, by key operation, the input image data of the skew-corrected construction drawing and the image data of the re-mapped recognition result can be superimposed and displayed, or only one of them can be selected and displayed.

Further, the operation input unit 10 inputs a key or the like for inputting information of "YES" or "NO" with respect to the display of "Is this recognition result OK? (YES / NO)". It also has a means. Then, if "YES" is selected, the recognition process ends, and if "NO" is selected, the process proceeds to the re-recognition process or the correction process. Thereby, the operator can confirm the content of the recognition result and determine it.

The external storage device 11 is recognized by the input image data, the black dot number measurement array data created by the dot number measurement array data creation section 6, the outline / skeleton recognition section 7 and the oblique wall recognition section 13. The storage device stores wall data, image data of a recognition result re-mapped by the re-mapping control unit 8, and the like in a storage medium that can be taken out, such as a floppy disk (FD) or a magneto-optical disk (OMD). The printing device 12 is a printer or a plotter that prints or renders the above various data on paper and outputs the data.

Here, the definitions of "outline" and "skeleton" and "outer wall" and "inner wall" in construction drawings (mainly architectural drawings) will be described with reference to Tables 1, 2 and 4.

[0042]

[Table 1]

[0043]

[Table 2]

The "outline" means the "outside outline", and only the portion facing the outside of the outer peripheral wall (shown by a double line in FIG. Case 1 which means the outer line part) and the entire wall (FIG. 4)
(B) is a case indicated by a bold line).

"Skeleton" means all of the walls (FIG. 4 (b)
(C) both cases indicated by bold lines)
There are two cases, and a case 3 meaning a wall excluding the outer contour (only a portion indicated by a thick line in FIG. 4C). In these definitions, unless otherwise specified, they are treated as the normal meaning of Case 1.

The "outer wall" means only the portion facing the outside of the outer peripheral wall as shown in FIG.
Case 4 meaning the portion indicated by the line outside the double line shown in FIG. 4A), and Case 5 meaning the entire wall in contact with the outside (the portion indicated by the thick line in FIG. 4B). There are cases.

The “inner wall” is a wall excluding the outer wall (wall = outer wall + inner wall) in both cases 4 and 5 as shown by a circle in Table 2, while in case 4 the case is shown in FIG. 4 (c) and the thick line in FIG. 4 (c), and the case 5 is the thick line in FIG. 4 (c).
Also in these definitions, unless otherwise specified, it is treated as the normal meaning of Case 5.

Next, the procedure for recognizing construction drawings (mainly architectural drawings of houses, buildings, etc.) by the construction drawing recognition apparatus shown in FIG. 1 will be described with reference to the flowcharts of FIGS. 5 to 8 and FIGS. 9 to 29.
It will be explained by. 6 to 8, each step is indicated by “S”. In this embodiment, the construction drawing to be recognized is a positive drawing.

FIG. 5 is a flowchart showing the main routine of the construction drawing recognition process. When the processing in FIG. 5 is started, the construction drawing set in the image reading unit 2 in FIG. 1 is read, the image image data is input, and the skew is automatically corrected by the automatic skew correction unit 5.

Then, in the subroutine of step A, horizontal and vertical wall position recognition processing is executed on the input image data by the dot number measurement array data creation unit 6 and the outline / skeleton recognition unit 7 and the like. . This processing will be described later in detail. Note that data obtained by encoding image image data may be input from the communication control unit 3.

Thereafter, the diagonal wall recognition section 13 executes the diagonal wall recognition processing in steps B to L. First, in step B, the end points and intersections of the wall recognized in step A are extracted, and the number n of them and the position coordinates of each point are obtained. The end points and intersections of the recognized walls are used in the following steps as starting points for oblique wall recognition. The extraction of the end points and the intersection points of the wall can be realized by a conventional technique, and thus the description is omitted.

In step C, in order to sequentially loop through all the starting points for oblique wall recognition obtained in step B, the count value i of the number counter is initialized (i
Perform ← 1). Then, in step D, it is determined whether or not the count value i of the number counter exceeds the number n of starting points for oblique wall recognition (i> n). If it exceeds, the analysis of the drawing is terminated, and the process proceeds to step M. If not, the diagonal wall recognition process of steps E to L is performed for the starting point for the i-th diagonal wall recognition.

In step E, for the starting point for the i-th diagonal wall recognition, the count value j of the number counter is initialized (j ← 1) in order to loop through the areas in the four directions of the image in order. ). And step F
The count value j of the number counter exceeds 4 (4
The search for all areas in the direction is completed: j> 4) is determined. If it exceeds, the analysis (search for the diagonal wall) from the starting point for the i-th diagonal wall recognition ends, and the process proceeds to step G. If it does not exceed, for the area in the j-th direction from the starting point for the i-th oblique wall recognition,
The oblique wall recognition processing of steps H to L is performed.

In step G, the count counter i of the starting point for oblique wall recognition is incremented by 1 to continue the recognition processing from the next starting point for oblique wall recognition, and then the process returns to step D. The above processing is repeated.

In step H, a skew angle is calculated from the starting point for recognition of the i-th oblique wall, assuming that there is a wall in the direction of the j region, focusing on the black dot. j
The regions are divided into the directions (four directions) by, for example, appropriately setting the region 1 (j = 1) from the first quadrant to the fourth quadrant on the XY plane with the origin at the starting point, the X axis in the horizontal direction, and the Y axis in the vertical direction. ) To region 4 (j = 4).

FIG. 29 shows an example. In this example, VW in the figure is a vertical wall, and HW is a horizontal wall. The origin O is the starting point, and the SW in the first area is the target diagonal wall. Is the skew angle. In this processing, for example, in FIG. 29, it is checked whether or not there is a black dot in the vicinity of the origin O in the area 1 (j = 1) from the origin O as the starting point. This is achieved by determining whether or not the operation is continued until the angle θ can be determined.

In Step I, a diagonal straight wall is searched in the obtained skew angle direction. This is done by creating black dot number measurement array data in the direction of the skew angle obtained in step H, and based on the data, the wall position recognition process in step A (to be described in detail later with reference to FIGS. 6 to 8). ).

Thereafter, in step J, it is determined whether or not a straight wall in the oblique direction has been recognized. If a straight wall in the oblique direction can be recognized, the process proceeds to step K, and
Then, after storing recognition result information such as wall position coordinates, the process proceeds to step L. On the other hand, if the rectilinear wall in the oblique direction cannot be recognized, step K is skipped and step L is performed directly.
Move to

In step L, in order to search for the next area for oblique wall recognition, the area direction number counter j from the start point for the i-th oblique wall recognition is incremented by +1. Return to the above process three more times (j = 4
Repeat).

When j> 4 in step F, i is incremented by 1 in step G, and the process returns in step D. The above processing from step D to step L is repeated a number of times equal to the number n of the extracted starting points, the recognition result is output in step M, and the wall recognition processing ends. In the processing in step M, the wall position of the image data (the coordinate values of the start point, end point, etc.)
Is stored in the memory 4 or the external storage device 11 as a result of recognition of the wall from the construction drawing, or displayed on the display unit 9 or the like.

FIG. 25 is a display example of image data obtained by remapping input image data (image image) of a construction drawing used in a process of recognizing a core line of a wall and extracting a rectangular closed loop from the result.

FIG. 26 is a diagram showing an example in which a rectangular closed loop of the recognition result is displayed. FIG. 27 shows an example in which the input image data of the skew-corrected construction drawing is superimposed and displayed on the rectangular closed loop image data of the recognition result.
FIG. 28 is an architectural drawing (floor plan) which is a construction drawing.
2 shows a specific example of the image data of (1) and the black dot number measurement array data in the oblique direction created from a partial area focusing on the oblique wall therein.

In this way, even for drawings which are partially interrupted or connected due to blur or unevenness, first, horizontal and vertical wall recognition is performed, and then the end points and intersection points are set as starting points. Since a rectilinear wall in an oblique direction can also be recognized, the wall recognition process can be performed more accurately.

Next, the wall recognition process in step A of FIG. 5 will be described. FIG. 6 is a flowchart showing the contents of the subroutine of the processing for recognizing the position of the wall. First,
In step 3, the entirety of the automatically skew-corrected image data is set as a survey target. Then, in step 4, the nest variable is 0 (initial value: meaning the entire image is targeted). The “nest variable” indicates a narrowing-down step when narrowing down the analysis range of the construction drawing from the top down. The larger the value of the nested variable, the deeper the analysis (the more advanced the part).

In step 5, a process for limiting the area to be examined for the position of the wall is performed. Specifically, the instruction of the investigation area is shown immediately before the process, and here, only the preparation for the subsequent processes of steps 6 to 9 (common use of the interface) is performed. The shape of each survey target area is a rectangular diagram. At first, since the nest variable is 0, the whole drawing is to be investigated.

In step 6, horizontal black dot number measurement array data is created. In this method, the number of black dots in the horizontal direction is counted (measured) for each one dot width in the vertical direction of the image data, and the counted data is held.
Next, in step 7, a wall extraction (recognition) process is performed based on the horizontal black dot number measurement array data created in step 6. The processing procedure will be described later.

In step 8, as in step 6, black dot number measurement array data in the vertical direction is created. That is, the number of black dots in the vertical direction is counted (measured) for each horizontal dot width of the image data, and the counted data is held. In step 9, a wall extraction (recognition) process is performed based on the black dot number measurement array data in the vertical direction.

Then, in step 10, it is determined whether there is a wall candidate for dividing the area. If there is at least one wall candidate that divides the region in the horizontal or vertical direction, the process proceeds to step 11. If there is no such wall candidate, the process proceeds to step 13. For example, a wall candidate Wd that divides a region into a survey target region Sa as shown in FIG.
To determine if is present.

In step 11, the nest variable is incremented by 1 and reset. This indicates that a wall is recognized from the current analysis area, and the analysis area is limited to a small area in the newly partitioned current area using the wall.

Then, in step 12, the area to be investigated is subdivided. Specifically, at the core line (center line) of the wall candidate recognized in steps 7 and 9,
For example, as shown in FIG. 9A, the survey target area Sa is defined by core lines indicated by thin lines of the wall candidates W and Wd.
a2 is divided into two. Further, the position of the survey target is set in the first subdivided area (for example, the upper left area).

The order of analysis in the newly subdivided area group does not require a special order. For example, the analysis is performed in the order of smaller x and y coordinate values of the area start position. And so on. FIG. 9B shows an example of the nest No. of each area subdivided by the wall candidates and the analysis processing order. The solid line indicates the core line (center line) of the wall candidate.
,, Indicate the nest number, and small numbers 1 to 8 indicate the processing order.

Thereafter, the flow returns to step S5 to repeat the above-mentioned processing. If there is no wall candidate to be divided in step 10, the process proceeds to step 13 to determine whether there is any remaining homogeneous nest area (the area of the same nest No. in FIG. 9B). to decide. If there is any remaining, the object to be investigated is advanced to the next area of the homogeneous nest in step 15, and the process returns to step 5.

If there is no remaining homogeneous nest area, the routine proceeds to step 14, where it is determined whether the nest variable is 0 or not. If the nest variable is not 0, the nest variable is decremented by 1 at step 16 and the process returns to the processing of the nest area one level higher. At step 13, it is determined whether there is a nest area remaining at that level. If there is, in step 15 the search target is advanced to the next nest area, and the process returns to step 5.

If the nest variable is 0 in step 14,
Proceeding to step 17, the position and size of each wall are determined based on the recognition data of the wall candidates for each area obtained in steps 7 and 9, and the wall recognition data is stored in the memory 4. The storage method will be described later. After the above processing, the subroutine ends.

Next, the procedure of the wall extraction (recognition) processing in steps 7 and 9 in FIG. 6 will be described with reference to FIG. 7. Prior to that, the construction drawing (floor plan) of the construction drawing will be described. FIGS. 10 and 11 show specific examples of the array data of the number of black dots in the horizontal and vertical directions created from the image data and the entire area thereof. In these figures, (a) is image data of an architectural drawing, (b) is horizontal dot count measurement array data, and (c) is vertical dot count measurement array data. Further, FIG. 12 is an enlarged view of the array data for measuring the number of black dots in the vertical direction shown in FIG.

In the architectural drawing of FIG. 10, the symbol of the wall is represented by both sides of the wall and the core, and in the architectural drawing of FIG. 11, the symbol of the wall is filled (black) within the thickness of the wall.
Is represented by In this black dot number measurement array data, the width of the thinnest black line is one dot width, and the length of each black line is horizontal or vertical for each one dot width of the image data of the architectural drawing shown in FIG. It corresponds to the counted value of the number of black dots in the direction.

As is apparent from these figures, most (90% or more) of the line segments constituting the architectural drawing are drawn in the horizontal direction or the vertical direction. Has become. Therefore, a peak appears at the position where the wall exists in the black dot number measurement array data in the horizontal direction and the vertical direction.

When the processing of the flow of FIG. 7 is started, first, in step 21, a direction crossing the designated direction (vertical direction if the black dot number measurement array data creation in the horizontal direction is designated, and vertical direction if designated in the vertical direction). If it is horizontal)
Next, it is confirmed how many times the loop of the wall recognition process can be performed at predetermined intervals corresponding to the reference unit length of the construction drawing. here,
In the case of a general house, this unit length is “half-half” or “1 meter (m)”, which is the minimum wall interval, and is set to half-half (91 cm) here.

For the black dot number measurement array data in the horizontal direction, a dimension slightly longer than the image width in the vertical direction is defined as the width size L. For the black dot number measurement array data in the vertical direction, the width image L is used. A dimension slightly longer than the width is automatically set as the width size L. In addition, the half space size is set to h, and this value is determined in advance by inputting the scale data and half space length of the drawing or by automatic calculation by calculation. L / h is calculated from the width size L and the half space size from h, and the value rounded up to the decimal point is defined as the number of executions n of the large loop of the wall recognition process. The range indicated by h in FIG. 12 is the processing range in one large loop.

Next, in step 22, the count value i of the large loop number counter is initialized (i ← 1).
Then, in step 23, the count value i of the number counter exceeds the number of possible large loop executions n (i> n).
Is determined. If so, the analysis of the area is terminated. If not, in step 24, it is positioned at the highest peak (indicated by P in FIG. 12) as the first analysis process within the i-th half, and that point is defined as xp. By performing the analysis processing every half-hour in this way, it is highly likely that the peak value in the analysis value is a part of the wall.

Next, in step 25, it is determined whether or not the height of a peak (maximum value), which is the first condition as an object of the wall, is equal to or longer than half an hour (h). As a result, if the height of the peak is less than half a minute, the recognition of the wall is unknown, and the process proceeds to step 34 in order to proceed to the analysis of the next half a point. When the height of the peak is half or more, the process proceeds to the next analysis step 26. In this step 26, the left and right sides from the position of the highest peak (e.g., 2W width) examine the range of wall thickness (double-sided position: W _1, W ₂ and thickness _{We = | W 1 -W 2 |} )
To narrow down. Here, W means the thickness of the wall, and is used, for example, with the same value as Wmax.

As a narrowing-down method, the peak positions at both ends or one side of the highest peak position having a value of (maximum peak value−min) * rate + min or more are determined by using the positions W ₁ ,
W ₂ or highest peak position xp is a method to narrow the position W ₂ of the other surface when the position W ₁ of one side of the wall.

FIG. 13 is an explanatory diagram of this narrowing-down process. FIG. 13A shows an example in which positions W ₁ and W _{2 on} both sides of the wall exist on both sides of the highest peak position xp. In this case, the highest peak position xp is estimated as a candidate for the center line position of the wall.

FIG. 13B shows an example in which the highest peak position xp is the position W ₁ of _one surface of the wall, and the position W ₂ of the other surface exists on one side. In this case, it can be estimated that the longer peak position is a candidate for the outer wall position of the outer contour indicated by the double line in FIG. 2 and the shorter peak adjacent thereto is a candidate for the inner wall position.

Here, the rate is set small in the first half (when the value of the nest variable is small) and large in the second half (when the value of the nest variable is large) (for example, rater = 0.
70). min is the minimum value of the black dot number measurement data within a width of about 4 W widened to the left and right sides 2 W of the position xp of the current maximum peak P as shown in FIG. 13C.

In this way, the validity as a wall is confirmed in steps 27 and 28 based on the result narrowed down in step 26 in FIG. First, in step 27, it is determined whether or not the wall thickness We exceeds its maximum value Wmax (for example, 30 cm). Proceed to.
If not, the process proceeds to step 28, where it is determined whether or not the wall thickness We is less than a minimum value Wmin (for example, 2.5 cm).

As a result, if the wall thickness We is less than the minimum value Wmin, the process proceeds to step 30; otherwise, the process proceeds to step 29. In step 30, the information is obtained on the assumption that a peak other than a wall (for example, a tatami mat, a window, a sliding door, etc.) has been recognized. In step 29, it is determined from the candidate area of the wall whether or not the condition as a wall is satisfied by a bisecting search method described later. Information such as the positions W ₁ and W ₂ and the thickness We is saved (stored), and the process proceeds to step 32.

If the condition as a wall is not satisfied in step 29, the process proceeds to step 32. In step 32, it is determined whether both side positions (coordinates) W ₁ and W ₂ of the wall are inside the area currently being processed. If not, the process proceeds to step 34.

In step 33, the current processing area is subdivided, and W ₁ ,
W ₂ and We are saved (stored). Also, (W ₁ + W ₂ ) /
2 is the position of the center line of the wall. In step 34, the count value i of the large loop number counter is incremented by 1 in order to perform the next half-ahead process, and the process returns to step 23 to repeat the above-described processing.

In this way, the analysis processing is performed every half a period from one end (head element) of the target black dot number measurement array data, and when the analysis is completed up to the opposite end, the analysis of the current range is completed. The two directions of the horizontal direction and the vertical direction are separately analyzed with respect to the black dot number measurement array data, and the next analysis range is limited to a range that can be recognized as a wall in the current analysis. Therefore, the result of analyzing the array data of the number of black dots in the horizontal and vertical directions is combined to perform the round robin classification.

In this embodiment, it is considered that the center line position of the wall is arranged so that the interval between the adjacent wall is an integral multiple of a half unit. Then, a range up to a characteristic peak can be effectively used as analysis data. Conversely,
If no feature corresponding to the wall is found within a certain range to be analyzed, even in both the horizontal and vertical directions, the analysis ends. A single analysis range initially covers the entire drawing, and is then limited to a range demarcated by walls that have been discovered since then, making it easier to find wall peaks and to analyze details in detail. So that

Next, the halving search method for judging “whether the condition as a wall is satisfied” in step 29 of FIG. 7 will be described with reference to the flowchart of FIG. When the processing of the flow shown in FIG. 8 is started, first, in step 41, initialization of the halving search method is performed. In other words, the number n of the area division elements for the wall analysis is set to 1, and the smallest No. of the array elements of the division elements for which the wall or the non-wall is undetermined.
Is set to 0, and S is set as a divided array element of the survey area.

[0], E

[0] Substitute the input start and end image addresses for each and set the initial settings.

Thereafter, the flow advances to step 42. If the start address S [k] of the investigation area is equal to the end address E [k], the flow shifts to step 53;
Proceed to 3. In step 43, the target area of the image data is divided, and the original area is divided into two equal parts except for errors of decimal numbers or less.

That is, the start and end image addresses S1 and E1 of the first half area to be divided are set as S1 = S
[K], E1 = (1/2) (S [k] + E [k]),
Start and end image addresses S2, E of the latter half area
2 as S2 = (1/2) (S [k] + E [k]) + 1, E
Let 2 = E [k].

Accordingly, as shown in FIG. 14, for example, in the image data of the construction drawing, the original area width of the line (indicated by the dashed line) where the wall candidate exists is initially divided into two equal parts at the position of one. I do. Thereafter, until the presence or absence of the wall can be determined, the second time at the position 2 shown in FIG.
The wall inspection of steps 44 and 45 is performed in the subdivided area by repeating the halving such as the fourth time at the fourth and fourth positions. In steps 44 and 45,
It is checked whether or not each of the equally-divided areas P1 and P2 is filled with a wall.

Here, as a result of analyzing the array data of the number of black dots in the designated area (from the start address to the end address), the peak height of the black dot (with the wall thickness kept wide) is specified. The following (1) to (3) are compared with the width (length) of the region.

: When less than 5%, v = 0: Judgment as non-wall part: When more than 95%, v = 1: Judgment as wall: In other cases, v = 2: Neither can be judged FIG.

In step 46, if it is not possible to determine whether a wall exists in the first half area P1 divided in step 43 (v = 2), the flow proceeds to step 47; otherwise, the flow proceeds to step 49. Step 47
In, the storage array elements S [k], E [k], and v [k] of the area range before being divided in step 43 are converted into the latter half of the area data (start and end image addresses S2, E2 is replaced with the determination result v2).

In step 48, new storage array elements S [n], E [n], v [n] are set as step 4
The first half area data (start and end image addresses S1, E1 and determination result v1) divided by 3 are saved, and the process proceeds to step 51. In step 49, the storage array elements S [k], E [k], and v [k] of the area range before the division in step 43 are replaced with the first half area data (start and end image addresses) divided in step 43. S1 and E1 are replaced with the determination result v1).

In step 50, new storage array elements S [n], E [n], v [n] are set as step 4
The second half area data (start and end image addresses S2, E2 and determination result v2) divided by 3 are saved, and the process proceeds to step 51. In step 51, the variable n indicating the new storage array element No. is incremented by 1 so as to indicate the new storage array element of the area address, and then the process proceeds to step 52.

In step 52, the classification code v in the k element indicating the smallest No. of the divided element whose wall portion or non-wall portion is undetermined is a content for which it is not known whether a wall exists (v = 2). In the case of ()), the process returns to step 42 and the process of further subdividing is repeated. Otherwise, go to step 53. In step 53, it is determined that one content that cannot be determined whether or not a wall exists has been resolved, and the index k is incremented by one, and then the process proceeds to step 54.

In step 54, the number n of the area dividing elements for the wall analysis is equal to the k indicating the smallest No. of the array elements of the dividing elements whose walls or non-walls are undetermined. It is determined whether or not they are equal, and if they are equal, it is determined that the division processing for wall recognition has been completed, and the flow proceeds to step 55.
If not, the process returns to step 52.

In step 55, sorting is performed in the order of the start addresses (ascending order) so that the divided area address data (array) is arranged in the ascending order.
Move to That is, S

[0] to S [n-1], E

[0] to E [n-1], v

The data [0] to v [n-1] are sorted in ascending order using S [] as a key.

In step 56, if the wall portion and the non-wall portion are continuous, the process is reduced (represented in one range), and the process ends. That is, continuous v
If the value of [] is 0 or 1, S [], E
[] Compress the data. At this time, if an array element having contents (v = 2) for which it is not known whether or not a wall exists is included, if the element before and after that element indicates a wall, the data is changed to wall data. If it indicates a non-wall, it is changed to non-wall data and processed.

FIG. 16 shows an example of analyzing the wall position in the image data by the above-mentioned halving search processing. In FIG. 16A, a wall image image (shaded portion) W and its investigation target area are indicated by broken lines, and this investigation area is located along the position of the previously recognized wall candidate. Is set.
And S

[0] = 0 is the first start address of the investigation area, E

[0] = 15 is the first end address. The numbers in the middle such as 4, 5, 7 are the start or end address (all are image addresses) of the target area after division.

FIG. 16B shows the start address S and end address E of each target area and the determination result v regarding the presence / absence of a wall in each investigation stage of variables n = 1 to 10 and the determination status, sort status, In addition, the degeneration processing result is shown together with the value of k. And finally, the image address 5
-12 recognize that a wall exists.

Next, a simple example of wall recognition in a construction drawing will be described with reference to FIG. FIG. 17 shows a nested variable (ne
(st) and the actual state of the wall recognized as the region division state. First, a wall is extracted with the nest variable = 0 as the whole construction drawing to be inspected for the wall position. As a result, it is assumed that the outline of the house in the construction drawing and the positions of the wall candidates in the horizontal and vertical directions can be recognized as indicated by the solid line in FIG. However, among the recognized wall candidates, the actual wall is only the portion shown in (a), but it is not yet known.

Then, next, the nest variable = 1 is set, and
A wall is extracted by limiting the investigation target area for each rectangular area demarcated by the core line of the recognized wall candidate shown in FIG.
As a result, in (B), wall candidates indicated by thick lines are newly recognized in the four investigation target regions indicated by,, and among the previously recognized wall candidates, actual walls are indicated in (a). That is, it is confirmed that the part is only the part which has been changed, and the state of the wall shown in (b) is recognized.

Further, by setting the nest variable = 2, the four regions-in which the new wall candidate is recognized in (B) are each divided by the newly recognized wall to further limit the region to be investigated. Extract the wall. As a result, a wall candidate indicated by a thick line is newly recognized in the two investigation target areas indicated by (C), and a state of the wall illustrated by (c) is recognized.

Thereafter, the nest variable is set to 3, and the two regions in which the new wall candidate is recognized in (C) are divided by the newly recognized wall, respectively, to further limit the investigation target region. Extract the wall. As a result, if it is not possible to extract a new wall candidate in any of the divided regions, the investigation of the wall position is thereby terminated,
It is determined that the wall position shown in (c) is the final wall recognition result, and the data is stored in the memory.

As described above, until no new wall candidate is extracted in any of the divided investigation target areas, the investigation target area is subdivided to extract the wall. Thereby,
Even a small wall can be reliably recognized, and a portion where a wall actually exists and a portion where no wall exists among wall candidates can be accurately determined.

Here, an example of a concrete construction drawing recognition processing procedure according to the flowchart of FIG.
This will be described with reference to FIGS. 18 to 20
Is a series of figures, which are divided into three figures for convenience of illustration. S3 to S17 in these figures are shown in FIG.
Correspond to each step of S3 to S17. In addition, an area division diagram and an actual wall state at each stage are also shown.

In the following description, steps are abbreviated as “S”. In S3 of FIG. 18, the entire drawing is to be investigated, and the nest variable is set to 0 in S4. In S5, the search target is limited. However, since the nest variable is 0, the whole drawing is also set as the search target. In steps S6 and S7, the black dot number measurement array data in the horizontal direction is created and the wall is extracted. However, a wall other than the contour cannot be found, and the black dot number measurement array data in the vertical direction is created in S8 and S9. Is extracted, and a wall other than the contour is found in two places. Therefore, YES is determined in S10, the nest variable is set to 1 in S11, the area is subdivided (at each wall position) in S12, and the process returns to S5 to limit the investigation target area to the leftmost area.

Then, in S6 and S7, a wall was found in two places. In S8 and S9, no wall was found, but in S10, the result was YES. In S11, the nesting variable was set to 2 and "nesting processing" was performed. The processing is repeatedly executed until the result of the determination in S14 of No. becomes No and the nesting processing is completed, and wall extraction processing is sequentially performed on three divided areas obtained by dividing the left vertically long area by two newly discovered walls.

In this example, no new wall is found, and the nesting variable is decremented by 1 in step S16 in FIG. 19, and the nest variable is returned to 1, and the wall is similarly extracted with the vertically long region in the middle as the investigation target region. In this example, no new wall candidate is found. Therefore, the investigation target area is changed to the right vertically elongated area in S15 and S5, and one wall is found in S6 and S7.

Then, YES is obtained in S10 of FIG.
In step S11, the nesting variable is set to 2 and the "nesting process" is started, and the vertical region on the right is divided by a newly discovered wall, and the wall extraction process for each of the divided regions is sequentially performed. As a result, no new wall is found in any of the divided areas, and S1
At 3, the nesting process is terminated, and the nesting variable is decremented to 1 at S16. Since the area of the nesting variable 1 does not remain, the nesting variable is further returned to 0, but the area also remains. Absent. Therefore, it is determined that the wall extraction process has been completed, the position and size of each wall are determined based on the recognition data of the wall candidates extracted in S17, and the data is stored in the memory.

Next, information (analysis result data) such as the position and size of the wall corresponding to the outline and skeleton of the construction drawing recognized as described above is stored in the memory 4 and the external storage device 11 shown in FIG. For an example of the content stored on the medium,
This will be described with reference to FIG. In FIG. 21, (A) is the number of nests, (B) is the specific nest information, (C) is the horizontal wall information, (D) is the vertical wall information, (E) is the model of the horizontal wall, (F) ) Indicates a vertical wall model.

The number of nests indicates the depth of nesting (parent-child relationship) of child nest pointers. If no wall is recognized, the number of nests is zero. The unique nest information includes a nest No., a NEXT sibling pointer, a child nest pointer, the number of walls (horizontal and vertical directions), and a wall information pointer (horizontal and vertical directions).

The NEXT sibling pointer has the next address of the simultaneous hierarchical nest information. Therefore, the nest number in the unique nest information at the location indicated by the pointer has the same value as that of the processing. The child nest pointer has the address of the leading data of the hierarchy nest information one level lower. Therefore, the nest number in the specific nest information at the location indicated by this pointer is a value obtained by adding +1 to that of the processing.

The horizontal wall information shown in (C) is stored with the address indicated by the horizontal wall information pointer in the unique nest information shown in (B) as the head address. The wall information includes a NEXT pointer indicating the position of the start address of the next horizontal wall information, and the starting point coordinates (x coordinate: a,
y coordinate: b), horizontal (x-direction) size of wall: c, vertical (y-direction) size of wall: wall thickness d. These a ~
By d, the model of the horizontal wall shown in (E) can be stored and reproduced.

Similarly, the address indicated by the vertical wall information pointer in the unique nest information is set as the head address.
The vertical wall information shown in (D) is stored. The wall information includes a NEXT pointer indicating the position of the start address of the next vertical wall information, the starting point coordinates of the wall (x coordinate: e, y coordinate: f), the vertical (y direction) size of the wall: g, the wall Horizontal (x-direction) size: Consists of wall thickness h. With these e to h, the model of the vertical wall shown in (F) can be stored and reproduced.

By the way, FIGS. 10 and 11 show examples in which the black dot number measurement array data in the horizontal direction and the vertical direction are prepared with respect to the image data of an actual architectural drawing by using the entire drawing as an investigation area. In the next step of recognizing a wall candidate based on the black dot count measurement array data, the area of the drawing is divided by the recognized wall candidate, and black in the horizontal and vertical directions based on image data in which the investigation target area is limited. FIGS. 22 to 24 show examples of creating dot number measurement array data.
Shown in

FIGS. 23 and 24 are examples in which the investigation target area is further subdivided than in FIG. In this way, until no new wall candidates are found, the investigation target area is subdivided, and the horizontal and vertical black dot number measurement array data based on the image data is created, and the wall is extracted.

In this embodiment, since the construction drawing of a positive image is to be recognized, the number of black dots in the horizontal and vertical directions of the binarized image data is measured (counted) to obtain a black dot number measurement array data. However, when a construction drawing of a negative image is to be recognized, the number of white dots in the horizontal and vertical directions of the binarized image data is measured (counted) to generate white dot number measurement array data. Then, the recognition of the wall can be performed similarly.

Further, the recognition data relating to the outline and skeleton of the construction drawing recognized as described above can be converted into CAD vector data, and compatibility of CAD data between different models can be obtained.

Further, in this embodiment, the number of black dots is counted (counted) in a diagonal direction on the contour of the portion composed of black dots, and black dot number measurement array data is created. Since the possible recognition processing is performed, there are the following merits.

(1) Even if a figure is formed by a straight line inclined obliquely from horizontal and vertical lines in the drawing, the recognition range in which the figure can be recognized is expanded as long as the inclination angle is known. (2) It is possible to recognize and extract a target (a polygon other than a rectangle) in which both straight lines (such as walls) facing in the horizontal and vertical directions of the drawing and straight lines inclined obliquely coexist.

If it is difficult to correct the skew in the direction opposite to the house drawing in the handwritten drawing, the handwritten drawing may be described on a graph paper, and the skew correction may be performed using the grid of the graph paper. . If it is unavoidable to leave the work to the user's manual work, a manual may be attached to draw attention to create a facing drawing that requires as little skew correction as possible when reading the scanner.

[0129]

As described above, according to the method for recognizing an oblique wall from a construction drawing according to the present invention, a straight oblique wall that is inclined in the horizontal or vertical direction existing in the construction drawing is formed. Can be reliably recognized as a part of the outline and skeleton of. In addition, it is possible to accurately recognize the oblique wall even in a construction drawing with a poor description state or image quality, such as a drawing having a relatively low contrast such as a handwritten drawing or a blueprint, a drawing with a lot of noise, or an old construction drawing.

According to the construction drawing recognition apparatus of the present invention, image data obtained by reading an image of a construction drawing is input, and walls forming the outline and skeleton of the construction drawing are included, including oblique and sloping walls. It can be automatically recognized with high accuracy.

If the recognized data is taken into a personal computer or the like and used, it is possible to easily and quickly create a construction drawing such as a sketch at the time of extension or remodeling. Furthermore, image data of a construction drawing can be input as image data obtained by run-length encoding by facsimile communication or the like, and the outline and skeleton of the construction drawing can be recognized.

Further, by displaying the recognition result of the outline / skeleton including the oblique wall, the recognition result can be confirmed and confirmed. In this case, by displaying the input image image data of the construction drawing simultaneously or selectively with the recognition result, the displayed contents can be compared and examined to find a misrecognized part or a part that could not be recognized. If the input image data and the recognition result are displayed in a superimposed manner, it is much easier to find an erroneously recognized part or a part that could not be recognized and to correct it.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a schematic configuration of an example of a construction drawing recognition device for implementing a method for recognizing an oblique wall from a construction drawing according to the present invention.

FIG. 2 is a diagram showing a display example of image data of a re-mapped recognition result on a display unit 9 of FIG. 1;

FIG. 3 is a diagram showing an example in which input data of a construction drawing, which is also skew-corrected, is superimposed on image data of a recognition result and displayed.

FIG. 4 is a diagram for explaining definitions of an outer contour, an outer wall, an inner wall, and a skeleton in a construction drawing.

FIG. 5 is a flowchart showing a main routine of construction drawing recognition processing by the construction drawing recognition device shown in FIG. 1;

FIG. 6 is a flowchart showing details of a subroutine of a wall position recognition process in step A in FIG. 5;

FIG. 7 is a flowchart of a subroutine of extraction (recognition) processing of walls of steps 7 and 9 in FIG. 6;

FIG. 8 is a flowchart for executing a bisection search method for recognizing the position of a wall.

FIG. 9 is a diagram illustrating an example of a wall candidate in a survey target region, a region division by the wall candidate, and a nest N of a region group subdivided by the wall candidate.
o. FIG. 4 is an explanatory diagram showing an example of the order of analysis processing.

FIG. 10 is a diagram showing a specific example of image data of an architectural drawing (floor floor plan) and black dot number measurement array data in the horizontal and vertical directions created from the entire area thereof.

FIG. 11 is a diagram showing another specific example.

FIG. 12 is an enlarged view of the vertical black dot number measurement array data shown in FIG. 10 (c).

FIG. 13 is an explanatory diagram of a process of narrowing both sides of a wall to both sides or one side of a peak in step 26 of FIG. 7;

FIG. 14 is an explanatory diagram of an area width dividing process in step 43 of FIG. 8;

FIG. 15 is an explanatory diagram of a judgment based on a wall survey of a target area in steps 44 and 45 in FIG. 8;

16 is an explanatory diagram illustrating an example of analysis of a wall sample (wall candidate) by the halving search process illustrated in FIG. 8;

FIG. 17 is an explanatory diagram of a simple construction drawing wall recognition example according to the present invention.

FIG. 18 is an explanatory diagram showing an example of a specific construction drawing recognition processing procedure according to the flowchart of FIG. 6;

FIG. 19 is an explanatory view continued from FIG. 18;

FIG. 20 is an explanatory view continued from FIG. 19;

FIG. 21 is an explanatory diagram showing an example of the contents of analysis result data stored in a memory;

22 is a diagram showing an example of image data of the architectural drawing shown in FIG. 10 in which the investigation target area is limited, and the array data of the number of black dots measured in the horizontal and vertical directions based on the image data.

FIG. 23 is a diagram showing an example of image data in which the investigation target area is further limited from FIG. 22, and an example of the arrangement data of the number of black dots in the horizontal and vertical directions based on the image data.

24 is a diagram showing an example of image data of another portion in which the investigation target area is further limited from FIG. 22 and black dot count measurement array data in the horizontal and vertical directions based on the image data.

25 is a diagram showing a display example of input image data of a skew-corrected construction drawing on the display unit 9 in FIG. 1;

FIG. 26 is a diagram showing an example in which the recognition result of the input image data is displayed.

27 is a diagram showing an example in which input image data of the construction drawing shown in FIG. 25 and image data of its recognition result are superimposed and displayed.

FIG. 28 is a diagram showing input image data of a construction drawing having an oblique wall and black dot number measurement array data created in a skew direction of each oblique wall in a region including the oblique wall.

FIG. 29 is an explanatory diagram of a j-region and skew angle calculation process in step H of FIG. 5;

[Explanation of symbols]

 1: Overall control unit (CPU) 2: Image reading unit 3: Communication control unit 4: Memory 5: Automatic skew correction unit 6: Dot number measurement array data creation unit 7: Outline / skeleton recognition unit 8: Remapping control unit 9 : Display unit 10: operation input unit 11: external storage device 12: printing device 13: oblique wall recognition unit 14: bus

Claims (11)

[Claims] An image area consisting of black or white dots of image image data obtained by reading an image of a construction drawing is limited, and a skew indicated entirely by black or white dots in the image image data of the limited image area. An angle is obtained, and in the obtained skew angle direction, the number of black or white dots of the image image data of the limited image area is measured to create dot number measurement array data, based on the created dot number measurement array data. And recognizing an oblique wall forming a part of a contour and a skeleton of the construction drawing. 2. The construction drawing recognition method according to claim 1, wherein the number of black or white dots in the horizontal and vertical directions of the image data obtained by reading the image of the construction drawing is measured to measure the number of dots in the horizontal and vertical directions. Data is created, and the outline and skeleton of the construction drawing are recognized based on the created horizontal and vertical dot count measurement array data. Based on the recognized outline and skeleton, black or white dots of the image image data are recognized. A method for recognizing an oblique wall from a construction drawing, comprising: 3. An image data input means for inputting image image data obtained by reading an image of a construction drawing, and measuring the number of black or white dots in the horizontal or vertical direction of the input image image data by means of the image data. A dot number measurement array data creating means for creating the vertical dot number measurement array data; and a contour / skeleton for recognizing the outline and skeleton of the construction drawing based on the horizontal and vertical dot number measurement array data created by the means. Based on the outline and skeleton of the construction drawing recognized by the skeleton recognition means, the image area of the input image image data is limited, and the black or white dots of the image image data of the limited image area are entirely formed. The skew angle is calculated in the direction of the skew angle, and the black or white image of the image data of the limited image area is determined in the direction of the skew angle. And an oblique wall recognizing means for recognizing an oblique wall forming part of the outline and skeleton of the construction drawing based on the generated dot number measurement array data by measuring the number of dots. A construction drawing recognition device characterized by the above-mentioned. 4. The construction drawing recognition apparatus according to claim 3, wherein said image data input means is image reading means for reading an image of the construction drawing and inputting the image data. apparatus. 5. An image data input means for inputting encoded image data obtained by encoding a run length of image image data obtained by reading an image of a construction drawing, and original image image data from the encoded image data inputted by said means. Dot number measurement array data creating means for creating the horizontal or vertical dot number measurement array data by measuring the number of black or white dots in the horizontal or vertical direction, and the horizontal and vertical dots created by the means Contour / skeleton recognition means for recognizing the outline and skeleton of the construction drawing based on the number measurement array data; and limiting the image area of the input image image data based on the outline and skeleton of the construction drawing recognized by the means. Then, a skew angle indicated by the black or white dots of the image data of the limited image area as a whole is determined, and the skew angle is determined. -The number of black or white dots of the image data of the limited image area is measured in the direction of the angle to create dot number measurement array data, and the contour and skeleton of the construction drawing are created based on the created dot number measurement array data. And an oblique wall recognizing means for recognizing an oblique wall forming a part of the construction drawing. 6. The construction drawing recognition apparatus according to claim 5, wherein said image data input means is image data receiving means for receiving and inputting said encoded image data by communication. apparatus. 7. The construction drawing recognition device according to claim 3, further comprising a display unit for displaying a recognition result by said oblique wall recognition unit. 8. The construction drawing recognition apparatus according to claim 7, wherein said display means has means for displaying image image data input by said image data input means simultaneously with said recognition result. Drawing recognition device. 9. The construction drawing recognition apparatus according to claim 8, wherein said means for displaying said image image data simultaneously with said recognition result is means for displaying said image image data superimposed on said recognition result. Characteristic construction drawing recognition device. 10. The construction drawing recognizing device according to claim 7, further comprising: display selection means for changing display contents so as to selectively display the image data input by the image data input means and the recognition result. A construction drawing recognition device provided. 11. The construction drawing recognition device according to claim 7, further comprising: a recognition result confirming means for confirming the recognition result displayed on said display means by an external instruction. Characteristic construction drawing recognition device.