CN111611935B - Automatic identification method for similar vector diagrams in CAD drawing - Google Patents
Automatic identification method for similar vector diagrams in CAD drawing Download PDFInfo
- Publication number
- CN111611935B CN111611935B CN202010442575.4A CN202010442575A CN111611935B CN 111611935 B CN111611935 B CN 111611935B CN 202010442575 A CN202010442575 A CN 202010442575A CN 111611935 B CN111611935 B CN 111611935B
- Authority
- CN
- China
- Prior art keywords
- graph
- source
- primitive
- primitives
- characteristic value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/40—Document-oriented image-based pattern recognition
- G06V30/42—Document-oriented image-based pattern recognition based on the type of document
- G06V30/422—Technical drawings; Geographical maps
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/55—Clustering; Classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/56—Information retrieval; Database structures therefor; File system structures therefor of still image data having vectorial format
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
Abstract
The invention relates to an automatic identification method of similar vector diagrams in CAD drawings, which comprises the steps of collecting a source graph, and obtaining a plurality of graph characteristic points in the source graph; determining a characteristic value of the source graph according to the characteristic point distance of each primitive in the source graph, and taking the characteristic value as a characteristic identifier of the source graph; searching other primitives with the same characteristic value as the source graph in the graph layer range of the source graph and grouping according to the primitive types; then extracting the primitives with the coordinate positions within the source graph size range by taking the first primitive as the center from each group to form a new graph; obtaining a characteristic value of the new graph by calculating the characteristic point distance between the extracted primitives, and comparing the characteristic value of the new graph serving as a characteristic identifier of the new graph with a characteristic identifier of the source graph; if the two graphs are consistent, the formed new graph is the graph to be searched; otherwise, continuing extraction calculation until all the primitives in each group are extracted. The scheme can automatically identify the primitive information in the CAD file, and is convenient to operate and high in identification efficiency.
Description
Technical Field
The invention relates to a pattern recognition method, in particular to an automatic recognition method of similar vector diagrams in CAD drawings.
Background
In the field of engineering cost calculation, cost engineers calculate the identified component graphic information in original drawings in a human-eye identification and manual statistics mode in many times. Thus, manual operation of the drawing is very inefficient, time and labor consuming, and recognition accuracy is not high. Especially in large drawings, human eyes are not easy to recognize and search, most of components are composed of discrete primitives, and selection omission is likely to happen when component graphs are selected manually, so that the problem of inaccurate subsequent calculation amount is caused.
The general original drawing is dwg drawing based on two-dimensional CAD software drawing. Most of the graphs drawn by the two-dimensional drawings are composed of primitives, such as straight lines, multiple lines, arcs, circles and the like, to form a member graph, and the graphs at different positions have the problems of rotation, scaling and the like. In addition, the graphics are vector graphics, and the graphics can be formed between every two primitives. In general, if all primitives are combined in sequence, then converted into bitmaps, and then recognized by the existing image recognition method, the efficiency is very low. Moreover, the whole image is large, the relative positions of the primitives are discrete, and the relative positions are distorted after being converted into the bitmap, so that the image is difficult to recognize.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides an automatic identification method of similar vector diagrams in CAD drawings. The method combines program automatic identification and statistical data, automatically identifies all graphs similar to 'long-phase' through the program in an original drawing, classifies the identified graphs, and then counts attribute data to be used as a calculation amount basis.
The purpose of the invention is realized by adopting the following technical scheme:
a method for automatically identifying similar vector graphics in a CAD drawing, the method comprising:
collecting a source graph, and obtaining a plurality of primitive feature points in the source graph;
determining a characteristic value of a source graph according to the characteristic point distance of each primitive in the source graph, and defining the characteristic value of the source graph as a characteristic identifier of the source graph;
searching other primitives with the same characteristic value as the source graph in the graph layer range of the source graph, and grouping according to the primitive types;
extracting other primitives with the coordinate position taking the first primitive as the center and within the size range of the source graphics from each group to form a new graphic;
obtaining a characteristic value of a new graph by calculating characteristic point distances among the extracted primitives;
taking the characteristic value of the new graph as a characteristic identifier of the new graph, and comparing the characteristic value with the characteristic identifier of the source graph; if the two images are consistent, obtaining similar images; otherwise, continuing extraction calculation until all the primitives in each group are extracted.
Preferably, the acquisition source pattern includes: sending the graph source information selected by the user to the CAD graphic service of the host computer;
the CAD graph service matches a graph source of a corresponding type according to the received graph source information, calls a uniform service interface corresponding to the graph source to read the graph source information, and obtains a source graph; wherein the graph source types comprise a relational type, a label type and a universal query string type.
Further, before the acquiring the source pattern, the method further includes: a predefined configuration file is read.
Further, the reading the predefined configuration file includes:
searching a configuration file path related to the graph source;
generating an available graph source information list by reading a predefined configuration file according to the configuration file path;
and feeding back the available graph source information list to a front-end interface for selection by a user.
Further, the generating the available graph source information list by reading the predefined configuration file includes:
the background graph access service loads access dynamic libraries corresponding to a plurality of graph sources according to a predefined configuration file, decouples the loaded graph sources and the access dynamic libraries corresponding to the graph sources, and generates an available graph source information list.
Further, the predefined interactive format template file is determined according to the primitive features of the primitive data; wherein the content of the first and second substances,
the primitive features include: dynamic data, pie charts, curves, lists, and dynamic trees.
Preferably, the searching for other primitives with the same characteristic value as the source graph in the graph layer range where the source graph is located, and the grouping according to the primitive types includes: setting the number of primitive types, respectively carrying out cluster analysis on other primitives with the same characteristic value as the source graph by using a standard software system (SRS) software and selecting a coefficient clustering method, or carrying out cluster analysis on two or more primitives, and checking whether the cluster analysis result conforms to normal distribution:
if yes, outputting grouping results of similar primitives; otherwise, performing clustering analysis again by adopting other methods of the coefficient clustering method until the clustering analysis result obeys normal distribution.
Further, the primitive types include straight lines, circles, arcs, ellipses, polylines, tables, and text.
The invention has the beneficial effects that:
the automatic identification method for the similar vector diagrams in the CAD drawing can automatically identify the primitive information in the CAD file, is convenient and fast to operate and has high identification efficiency. Firstly, acquiring a plurality of primitive feature point distances in a source graph through an acquired source graph, and calculating to obtain a feature value of the source graph; and searching other primitives with the same characteristic point distance to generate a new graph, and calculating to obtain a characteristic value of the new graph. Finally, the obtained figure characteristic values are used as the basis, and similar new figures are obtained through comparison.
The technical scheme of the invention combines automatic program identification and statistical data, automatically identifies all graphs similar to 'long phase' through the program in the original drawing, and takes the attribute data of the identified graphs after classification and statistics as the basis of calculation amount. The method for determining the primitive relation by the space relative position has the advantages of high speed and high accuracy.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a flow chart of an automatic identification method for similar vector diagrams in a CAD drawing provided by the invention;
FIG. 2(a) is a schematic diagram of selecting a source pattern provided in an embodiment of the present invention;
FIG. 2(b) is a diagram illustrating automatic searching for similar graphics provided in an embodiment of the present invention;
FIG. 3 is a diagram illustrating a similar graphics structure of source graphics primitives provided in an embodiment of the present invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
In order to specifically understand the technical solutions provided by the present invention, the technical solutions of the present invention will be described and illustrated in detail in the following examples. It is apparent that the embodiments provided by the present invention are not limited to the specific details familiar to those skilled in the art. The following detailed description of the preferred embodiments of the invention is intended to provide further embodiments of the invention in addition to those described herein.
The present invention will be described in detail with reference to the accompanying drawings and examples. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention discloses an automatic identification method of similar vector diagrams in CAD drawings, and the whole identification process can be simply summarized into the following steps:
1, collecting a source graph (comprising a plurality of primitives)
2, acquiring the characteristic point of each graphic element in the source graph;
calculating the characteristic point distance of the graphic primitive through the characteristic point of the graphic primitive in the source graph, and then calculating the characteristic point distance of the whole graph as a characteristic mark;
4, searching other primitives which are the same as the source primitive feature points in the layer range of the source primitive;
5, grouping the searched primitives according to types;
extracting other primitives of which the coordinates are in a source graphic size area by taking the first primitive as a center from each group, and forming a new graphic;
7, calculating the characteristic value of the primitive in the new graph, and then calculating the characteristic point distance of the new graph to obtain the characteristic value as the characteristic identification of the new graph;
and 8, comparing whether the feature identifier of the new graph is similar to the feature identifier of the source graph, if so, taking the new graph as the found graph, otherwise, continuously extracting and comparing until all the primitives in each group are extracted.
Specifically, as shown in fig. 1, the automatic identification method for similar vector diagrams in a CAD drawing disclosed in the embodiment of the present application includes:
s1, collecting a source graph, and acquiring a plurality of primitive feature points in the source graph;
step S2, determining a characteristic value of a source graph according to the characteristic point distance of each primitive in the source graph, and defining the characteristic value of the source graph as a characteristic identifier of the source graph;
step S3, searching other primitives with the same characteristic value as the source graph in the graph layer range of the source graph, and grouping according to primitive types;
step S4, extracting other primitives with the coordinate position of the first primitive as the center and within the source graphic size range from each group to form a new graphic;
step S5, obtaining a characteristic value of a new graph by calculating characteristic point distances among the extracted primitives;
step S6, taking the feature value of the new graph as the feature identifier of the new graph, and comparing the feature value with the feature identifier of the source graph; if the two images are consistent, obtaining similar images; otherwise, continuing extraction calculation until all the primitives in each group are extracted.
In step S1, acquiring the source pattern includes: sending the graph source information selected by the user to the CAD graphic service of the host computer;
the CAD graph service matches a graph source of a corresponding type according to the received graph source information, calls a uniform service interface corresponding to the graph source to read the graph source information, and obtains a source graph; the graph source types comprise a relational type, a label type, a universal query string type and the like.
In addition, before the step S1 of collecting the source pattern, the method further includes: a predefined configuration file is read.
Reading the predefined configuration file includes:
a. searching a configuration file path related to the graph source;
b. generating an available graph source information list by reading a predefined configuration file according to the path of the configuration file; and feeding back the available graph source information list to a front-end interface for selection by a user. The method comprises the following steps that a predefined interactive format template file is determined according to primitive features of primitive data; the primitive features include: dynamic data, pie charts, curves, lists, and dynamic trees.
The step b of generating the available graph source information list by reading the predefined configuration file comprises the following steps:
the background graph access service loads access dynamic libraries corresponding to a plurality of graph sources according to a predefined configuration file, decouples the loaded graph sources and the access dynamic libraries corresponding to the graph sources, and generates an available graph source information list.
In step S3, the searching for other primitives with the same feature value as the source graph in the graph layer range where the source graph is located, and the grouping according to the primitive types includes: setting the number of types of the primitives, respectively carrying out clustering analysis on other primitives with the same characteristic value as the source graph by using standard software system SRS software and selecting a coefficient clustering method, or carrying out clustering analysis on two or more primitives, and checking whether the clustering analysis result conforms to normal distribution; if yes, outputting grouping results of similar primitives; otherwise, performing clustering analysis again by adopting other methods of the coefficient clustering method until the clustering analysis result obeys normal distribution.
The primitive types comprise straight lines, circles, arcs, ellipses, multi-segment lines, tables and texts.
Example 1:
firstly, selecting points in a drawing to form a graphic primitive;
next, click determination is performed, and the system automatically identifies all the graphs (including rotation) similar to the selected graph in the drawing, as shown in fig. 2(a) and (b), and the implementation principle is as follows:
in a two-dimensional plane, each geometric object has its own feature points, and the feature points determine the shape of the geometric object, such as a line segment having a starting point and an ending point; the circle has a center and a radius; the arc has a circle center, a radius, an initial angle and a termination angle; the character has a central point and content; the rectangle has corner points, a center point, etc.
After the object is picked up, the program automatically extracts the positional relationship (expressed by the feature point distance) between the graphic feature points and converts it into a numerical value. The program circularly searches other objects with the same characteristic points of each primitive in the designated range according to the selected primitives, groups the objects according to the types in the source, and extracts a primitive object from each group according to the position relationship to form a new graph, so that the new graph is found.
It is particularly noted that the positional relationship (feature point distance) must be the same when extracting the primitive from each group. Otherwise, highly symmetric pattern recognition errors cannot be guaranteed.
As shown in FIG. 3, assume that A is the source pattern, defined by the line a0And c0Arc b0And d0And (4) forming. Then, a in the A pattern0And b0The positional relationship of (a) can be expressed as: a is01 b01、a01 b02、a01 b03、a02 b01、a02 b02、 a03 b03(wherein a)01And a02Respectively representing two end points of a straight line, b01、b02And b03Two end points and the circle center of the circular arc are respectively represented) 6 characteristic point distance values. In turn, can determine b0And c0，c0And d0，d0And a0The value of the characteristic point distance of (2).
Straight lines a, b, e, f, i, j, k, l and m and circular arcs c, d, g and h exist in the drawing. Wherein, the straight line a0、c0、a、b、e、f、i、j. k has the same length and is arc b0、d0C, d, g, h are the same in arc length and radius.
And two graphs of B (consisting of a primitive a, B, C and d) and C (consisting of a primitive e, f, g and h) can be accurately identified during automatic identification.
During identification, according to the primitives in the graph A, other primitives with the same characteristic points in the drawing are sequentially found and grouped, as shown in the following:
| source graphics primitives | Similar primitives |
| a0 | a、b、e、f、i、j、k |
| b0 | c、d、g、h |
| c0 | a、b、e、f、i、j、k |
| d0 | c、d、g、h |
And after the corresponding graphic elements are identified, circularly extracting one corresponding graphic element from each graphic element group from the first graphic element in the A graphic, and calculating a characteristic point distance value to compare with the corresponding characteristic point distance in the A graphic. If the distance between each feature point can be the same, the part of the new graph between the current primitive and the next primitive can be determined. When the number of the primitives which can be found in the equal circulation and can compose the new graph is equal to the number of the primitives in the graph A, determining that one primitive is foundAnd (6) a new graph. Determining the total number of times of calculation of the new graph as follows: n is a radical ofTotal number of times＝N a0×N b0×N c0×N d0,d。
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart 1 flow or flows and/or block 1 block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows of FIG. 1 and/or block diagram block or blocks of FIG. 1.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart 1 flow or flows and/or block 1 block or blocks.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application and not for limiting the protection scope thereof, and although the present application is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: numerous variations, modifications, and equivalents will occur to those skilled in the art upon reading the present application and are within the scope of the claims appended hereto.
Claims (8)
1. A method for automatically identifying similar vector diagrams in CAD drawings is characterized by comprising the following steps:
collecting a source graph, and obtaining a plurality of primitive feature points in the source graph;
determining a characteristic value of the source graph according to the characteristic point distance of each primitive in the source graph, and defining the characteristic value of the source graph as a characteristic identifier of the source graph;
searching other primitives with the same characteristic value as the source graph in the graph layer range of the source graph, and grouping according to the primitive types;
extracting other primitives with the coordinate position centered on one primitive and within the size range of the source graphics from each group to form a new graphic;
obtaining a characteristic value of a new graph by calculating characteristic point distances among the extracted primitives;
taking the characteristic value of the new graph as a characteristic identifier of the new graph, and comparing the characteristic value with the characteristic identifier of the source graph; if the two images are consistent, obtaining similar images; otherwise, continuing extraction calculation until all the primitives in each group are extracted.
2. The method of claim 1, wherein said acquiring a source graph comprises: sending the graph source information selected by the user to the CAD graphic service of the host computer;
the CAD graph service matches a graph source of a corresponding type according to the received graph source information, calls a uniform service interface corresponding to the graph source to read the graph source information, and obtains a source graph; wherein the graph source types comprise a relational type, a label type and a universal query string type.
3. The method of claim 2, wherein said acquiring a source pattern further comprises, prior to: a predefined configuration file is read.
4. The method of claim 3, wherein reading the predefined configuration file comprises:
searching a configuration file path related to the graph source;
generating an available graph source information list by reading a predefined configuration file according to the configuration file path;
and feeding back the available graph source information list to a front-end interface for selection by a user.
5. The method of claim 4, wherein the generating the list of available graph source information by reading a predefined configuration file comprises:
the background graph access service loads access dynamic libraries corresponding to a plurality of graph sources according to a predefined configuration file, decouples the loaded graph sources and the access dynamic libraries corresponding to the graph sources, and generates an available graph source information list.
6. The method of claim 5, wherein the predefined configuration file is determined based on primitive characteristics of the primitive data; wherein the content of the first and second substances,
the primitive features include: dynamic data, pie charts, curves, lists, and dynamic trees.
7. The method of claim 1, wherein the searching for other primitives with the same characteristic value as that of the source graph in the graph layer range of the source graph and the grouping according to the primitive types comprises: setting the number of primitive types, respectively carrying out cluster analysis on other primitives with the same characteristic value as the source graph by using a standard software system (SRS) software and selecting a coefficient clustering method, or carrying out cluster analysis on two or more primitives, and checking whether the cluster analysis result conforms to normal distribution:
if yes, outputting grouping results of similar primitives; otherwise, performing clustering analysis again by adopting other methods of the coefficient clustering method until the clustering analysis result obeys normal distribution.
8. The method of claim 7, wherein the primitive types include straight lines, circles, arcs, ellipses, polylines, tables, and text.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010442575.4A CN111611935B (en) | 2020-05-22 | 2020-05-22 | Automatic identification method for similar vector diagrams in CAD drawing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010442575.4A CN111611935B (en) | 2020-05-22 | 2020-05-22 | Automatic identification method for similar vector diagrams in CAD drawing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111611935A CN111611935A (en) | 2020-09-01 |
| CN111611935B true CN111611935B (en) | 2021-08-27 |
Family
ID=72199578
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010442575.4A Active CN111611935B (en) | 2020-05-22 | 2020-05-22 | Automatic identification method for similar vector diagrams in CAD drawing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111611935B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112990092B (en) * | 2021-04-09 | 2022-03-11 | 福建晨曦信息科技集团股份有限公司 | Legend identification method, computer device and readable storage medium |
| CN113177995A (en) * | 2021-04-30 | 2021-07-27 | 广联达科技股份有限公司 | Text recombination method for CAD drawing and computer readable storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101630335A (en) * | 2008-07-18 | 2010-01-20 | 纬衡浩建科技（深圳）有限公司 | Method for comparing similarity and difference between drawings |
| CN105224706A (en) * | 2014-06-30 | 2016-01-06 | 上海神机软件有限公司 | Based on engineering drawing management system and method, row's modular system and method for workspace |
| CN107045526A (en) * | 2016-12-30 | 2017-08-15 | 许昌学院 | A kind of pattern recognition method of electronics architectural working drawing |
| CN110399509A (en) * | 2019-06-10 | 2019-11-01 | 万翼科技有限公司 | It is a kind of intelligently to know drawing system and method |
| US10573034B2 (en) * | 2018-04-13 | 2020-02-25 | Honeywell International Inc. | System and method for translation of graphics to newer format using pattern matching |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8774526B2 (en) * | 2010-02-08 | 2014-07-08 | Microsoft Corporation | Intelligent image search results summarization and browsing |
| CN105302931B (en) * | 2014-06-30 | 2019-01-04 | 上海神机软件有限公司 | Construction project drawing identifying system and method, row's modular system and method |
| CN109145174A (en) * | 2018-08-01 | 2019-01-04 | 山东省国土测绘院 | A kind of emergency geography information quick draw graphics system and its drafting method |
| CN109636887B (en) * | 2018-11-29 | 2023-06-06 | 北京宇航系统工程研究所 | Two-dimensional electronic technology drawing format conversion and vectorization interaction system |
-
2020
- 2020-05-22 CN CN202010442575.4A patent/CN111611935B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101630335A (en) * | 2008-07-18 | 2010-01-20 | 纬衡浩建科技（深圳）有限公司 | Method for comparing similarity and difference between drawings |
| CN105224706A (en) * | 2014-06-30 | 2016-01-06 | 上海神机软件有限公司 | Based on engineering drawing management system and method, row's modular system and method for workspace |
| CN107045526A (en) * | 2016-12-30 | 2017-08-15 | 许昌学院 | A kind of pattern recognition method of electronics architectural working drawing |
| US10573034B2 (en) * | 2018-04-13 | 2020-02-25 | Honeywell International Inc. | System and method for translation of graphics to newer format using pattern matching |
| CN110399509A (en) * | 2019-06-10 | 2019-11-01 | 万翼科技有限公司 | It is a kind of intelligently to know drawing system and method |
Non-Patent Citations (2)
| Title |
|---|
| Algorithms Generating Primitive Objects for CAD;S Furukawa;《Reports of the Faculty of Engineering, Yamanashi University》;19781231;第79-86页 * |
| CAD图纸自动比对算法研究;谭裴等;《电子技术》;20111231(第4期);第16-18页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111611935A (en) | 2020-09-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP5916886B2 (en) | Image indexing based on similarity of image features | |
| JP6188400B2 (en) | Image processing apparatus, program, and image processing method | |
| US7450763B2 (en) | Reshaping freehand drawn lines and shapes in an electronic document | |
| CN111611936B (en) | Automatic identification system for similar vector diagrams in CAD drawings | |
| CN111611935B (en) | Automatic identification method for similar vector diagrams in CAD drawing | |
| CN110503682B (en) | Rectangular control identification method and device, terminal and storage medium | |
| CN113223013B (en) | Method, device, equipment and storage medium for pulmonary vessel segmentation positioning | |
| CN114926699A (en) | Indoor three-dimensional point cloud semantic classification method, device, medium and terminal | |
| CN109871743B (en) | Text data positioning method and device, storage medium and terminal | |
| JP5226651B2 (en) | Similar image retrieval device, similar image retrieval method, and similar image retrieval program | |
| CN115131363A (en) | Positioning method and device based on semantic information and terminal equipment | |
| Álvarez et al. | Junction assisted 3d pose retrieval of untextured 3d models in monocular images | |
| CN112613367A (en) | Bill information text box acquisition method, system, equipment and storage medium | |
| CN112084103B (en) | Interface test method, device, equipment and medium | |
| CN116661786A (en) | Design page generation method and device | |
| CN111783180B (en) | Drawing splitting method and related device | |
| CN113362287B (en) | Man-machine cooperative remote sensing image intelligent interpretation method | |
| CN114387600A (en) | Text feature recognition method and device, computer equipment and storage medium | |
| CN111368674B (en) | Image recognition method and device | |
| Kong et al. | SimLocator: robust locator of similar objects in images | |
| KR20100117986A (en) | Method for measuring the location similarity of spatial object on digital maps and map matching using the same | |
| CN111598835A (en) | Matching method, system and matching device for colors of clothing pictures | |
| CN116166889B (en) | Hotel product screening method, device, equipment and storage medium | |
| CN113689444A (en) | Building point cloud monomer segmentation method and device | |
| CN115273116A (en) | Table detection and identification method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |