• Title/Summary/Keyword: Drawing recognition

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A Computer Aided Drawing Check System(Local Dimension Check) (컴퓨터지원에 의한 설계도면 검증시스템)

  • ;Ono, T.;Tsujio, S.
    • 제어로봇시스템학회:학술대회논문집
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    • 1992.10a
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    • pp.513-518
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    • 1992
  • We are now doing research for the drawing check of local parts in mechanical drawing made by a CAD system. It needs the recognition of drawing elements with respect to the local parts. Because, we usually abbreviate the dimensioning in the mutually related drawing elements. This paper is concerned with a computer aided supporting system to the dimension check and recognition of local parts in mechanical drawings. This sytem has been applied to some examples and we have confirmed the feasibility of this checking method.

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A basic research for knowledge-based management of feature recognition rules (형상인식 규칙의 지식 베이스 운용에 관한 연구)

  • 박재홍;반갑수;이석희
    • 제어로봇시스템학회:학술대회논문집
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    • 1991.10a
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    • pp.715-719
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    • 1991
  • In manufacturing process, usually 2-dimensional part drawing is used as a basic data. If a designer wants to recognize 2-dimensional drawing and formulate 3-dimensional shape, a proper feature recognition rule is required as a prerequisite step. These rules are converted Into knowledge base, should be ed separately in the recognition program and can be referenced In similar way of database application. In this paper, basic feature recognition rules are addressed in structure type knowledge base, and the application system is formulated which can be operated separately with existing data driven program.

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Automatic Drawing Input by Segmentation of Text Region and Recognltion of Geometric Drawing Element (문자영역의 분리와 기하학적 도면요소의 인식에 의한 도면 자동입력)

  • 배창석;민병우
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.31B no.6
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    • pp.91-103
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    • 1994
  • As CAD systems are introduced in the filed of engineering design, the necessities for automatic drawing input are increased . In this paper, we propose a method for realizing automatic drawing input by separation of text regions and graphic regions, extraction of line vectors from graphic regions, and recognition of circular arcs and circles from line vectors. Sizes of isolated regions, on a drawing are used for separating text regions and graphic regions. Thinning and maximum allowable error method are used to extract line vectors. And geometric structures of line vectors are analyzed to recognize circular arcs and circles. By processing text regions and graphic regions separately, 30~40% of vector information can be reduced. Recognition of circular arcs and circles can increase the utilization of automatic drawing input function.

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Landscape Drawing as a Text: Practical and Theoretical Approach (텍스트로서의 조경드로잉 - 읽기의 틀과 실제 -)

  • 이광빈;조정송
    • Journal of the Korean Institute of Landscape Architecture
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    • v.27 no.1
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    • pp.54-63
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    • 1999
  • The Landscape drawing is used as main media in landscape design process like the language in daily life for human. Designers input many intentions and meaningful words in design process through landscape drawing. The common purpose of landscape drawing is to represent reality effectively, even though it has variable visual forms and materiality. The representation in landscape drawing in metaphorical as well as visual and functional. But current tendency is inclined to use landscape drawing in a functional aspect for visual representation and the landscape drawing is utilized straight-forwardly rather than metaphorically for clear communication. Such recognition on landscape drawing results from the difficulty to accept the symbolic aspect of the drawing. The difficulty makes the utilization and the interpretation of landscape drawing stay at conventional level in following visible factors. For the sake of solving the difficulty this study considers landscape drawing as the text that contains readable objects and symbolic words. This study presents layer-methods for reading a landscape drawing as a text; situational and contextural reading, iconological reading and reading the subject of drawing.

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A Study on the Dynamic Image Drawing Part Information Recognition using Artificial Intelligence (인공지능기법을 이용한 동적 이미지 도면 부품정보 인식에 관한 연구)

  • Lee Joo-Sang;Kang Sung-In;Lee Sang-Bae
    • Journal of the Korean Institute of Intelligent Systems
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    • v.16 no.4
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    • pp.449-453
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    • 2006
  • This paper wishes to present way that can take advantage of parts information of image drawing for efficient maintenance management of facilities efficiently. Information for parts that compose facilities to facilities design drawing has been expressed, and legend character has been written to divide each parts. This paper applies Artificial Intelligence techniques for legend character cognition of image drawing. Finally, apply artificial intelligence techniques to drawing management system to evaluate efficiency of method that propose in this paper that see.

Development of Automation Technology for Structural Members Quantity Calculation through 2D Drawing Recognition (2D 도면 인식을 통한 부재 물량 산출 자동화 기술 개발)

  • Sunwoo, Hyo-Bin;Choi, Go-Hoon;Heo, Seok-Jae
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2022.04a
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    • pp.227-228
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    • 2022
  • In order to achieve the goal of cost management, which is one of the three major management goals of building production, this paper introduces an approximate cost estimating automation technology in the design stage as the importance of predicting construction costs increases. BIM is used for accurate estimating, and the quantity of structural members and finishing materials is calculated by creating a 3D model of the actual building. However, only 2D basic design drawings are provided when making an estimating. Therefore, for accurate quantity calculation, digitization of 2D drawings is required. Therefore, this research calculates the quantity of concrete structural members by calculating the area for the recognition area through 2D drawing recognition technology incorporating computer vision. It is judged that the development technology of this research can be used as an important decision-making tool when predicting the construction cost in the design stage. In addition, it is expected that 3D modeling automation and 3D structural analysis will be possible through the digitization of 2D drawings.

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Development and Application of Science Drawing Program to Improve Science Process Skills (과학 탐구 능력 향상을 위한 과학 그리기 프로그램 개발 및 적용)

  • Park, Mi-Jin;Lee, Yong-Seob
    • Journal of Korean Elementary Science Education
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    • v.29 no.2
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    • pp.186-194
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    • 2010
  • The purpose of this study is developing a Science Drawing Program to improve Science Process Skills and examining the effect of it. For this study two classes (missed classes with boys and girls) of 3th grade students participated and the subject of the instruction was domain of Air and Weather. The result of this study were as follows: First, the Science Drawing Program was effective in improving the science process skills. Development and Application of the Science Drawing Program brought positive effects especially on the observation, measurement, reasoning power. Second, student showed positive recognition on the Science Drawing Program. Many students showed interests and want to use it in class. And they thought Drawing Program helpful in their learning. Therefore the Science Drawing Program is very effective method in science education.

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Interactive drawing with user's intentions using image segmentation

  • Lim, Sooyeon
    • International Journal of Internet, Broadcasting and Communication
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    • v.10 no.3
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    • pp.73-80
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    • 2018
  • This study introduces an interactive drawing system, a tool that allows user to sketch and draw with his own intentions. The proposed system enables the user to express more creatively through a tool that allows the user to reproduce his original idea as a drawing and transform it using his body. The user can actively participate in the production of the artwork by studying the unique formative language of the spectator. In addition, the user is given an opportunity to experience a creative process by transforming arbitrary drawing into various shapes according to his gestures. Interactive drawing systems use the segmentation of the drawing image as a way to extend the user's initial drawing idea. The system includes transforming a two-dimensional drawing into a volume-like form such as a three-dimensional drawing using image segmentation. In this process, a psychological space is created that can stimulate the imagination of the user and project the object of desire. This process of drawing personification plays a role of giving the user familiarity with the artwork and indirectly expressing his her emotions to others. This means that the interactive drawing, which has changed to the emotional concept of interaction beyond the concept of information transfer, can create a cooperative sensation image between user's time and space and occupy an important position in multimedia society.

Automatic Recognition of Symbol Objects in P&IDs using Artificial Intelligence (인공지능 기반 플랜트 도면 내 심볼 객체 자동화 검출)

  • Shin, Ho-Jin;Jeon, Eun-Mi;Kwon, Do-kyung;Kwon, Jun-Seok;Lee, Chul-Jin
    • Plant Journal
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    • v.17 no.3
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    • pp.37-41
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    • 2021
  • P&ID((Piping and Instrument Diagram) is a key drawing in the engineering industry because it contains information about the units and instrumentation of the plant. Until now, simple repetitive tasks like listing symbols in P&ID drawings have been done manually, consuming lots of time and manpower. Currently, a deep learning model based on CNN(Convolutional Neural Network) is studied for drawing object detection, but the detection time is about 30 minutes and the accuracy is about 90%, indicating performance that is not sufficient to be implemented in the real word. In this study, the detection of symbols in a drawing is performed using 1-stage object detection algorithms that process both region proposal and detection. Specifically, build the training data using the image labeling tool, and show the results of recognizing the symbol in the drawing which are trained in the deep learning model.