Study on Algorithms of Graphic Element Recognition for Precise Vectorization of Industrial Computed Tomographic Image

α
Bing He
Bing He
σ
Dr. Fenglin Liu
Dr. Fenglin Liu
ρ
Bi Bi1
Bi Bi1
α Chongqing University Chongqing University

Send Message

To: Author

Study  on  Algorithms  of  Graphic  Element  Recognition  for  Precise Vectorization of Industrial Computed Tomographic Image

Article Fingerprint

ReserarchID

H752M

Study  on  Algorithms  of  Graphic  Element  Recognition  for  Precise Vectorization of Industrial Computed Tomographic Image Banner

AI TAKEAWAY

Connecting with the Eternal Ground
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu

Abstract

Circle, line and circular arc are the common basic graphic elements in industrial computed tomography (ICT) image. The algorithm of recognizing such elements is the key to industrial CT image precise vectorization. An industrial CT image vectorization system has been studied, including different recognition methods for these elements. Firstly, based on facet model, the sub-pixel edge of an industrial CT image is extracted. Then, the circles are recognized by an improved algorithm based on probability of existence map, while the lines are recognized with the set intersection algorithm of fitting a straight line, and the circular arcs are recognized by the combination of the perpendicular bisector tracing algorithm and least squares function. Finally, the graphic element parameters are measured according to recognition results, and the drawing exchange file (DXF) is produced and transmitted into the computer aided design (CAD) system to be edited and consummated. The experimental results show that these methods are capable of recognizing graphic elements in industrial CT image with an excellent accuracy, besides, the absolute errors of circles are less than 0.1 mm, and the relative errors are less than 0.5%. It can satisfy the industrial CT vectorization requirements of higher precision, rapid speed and non-contact.

References

11 Cites in Article
  1. F Liu,G Qiao,B Zou (2009). Precise measurement of circles in industrial computed tomographic images.
  2. N Lee,S Jung,J Kim (2009). Evaluation of the measurement geometries and data processing algorithms.
  3. B Cheng,S Zhang,Y Shi (2008). Research on Vectorization and Recognition for Engineering Drawings: a Survey.
  4. Vijay Nagasamy,Noshir Langrana (1990). Engineering drawing processing and vectorization system.
  5. Junwei Tian,Y Shu,D Tian,Y Huang (2006). IMAGE SUB-PIXEL FEATURE LOCATION ALGORITHM ON VISION MEASUREMENT SYSTEM.
  6. S Zheng,J Tian,J Liu (1990). Efficient facetbased edge detection approach.
  7. Y Zhang,H Wang,Z Liang,M Tan,W Ye,B Lian (2006). Existence Probability Map Based Circle Detection Method.
  8. Q Li,D Wang,Z Chen (1995). Research on Image Vectorization Methods and Algorithms.
  9. H Guan,J Zhang,Q Hu,L Zhong (2008). Line Extraction of industrial parts based on least square template matching.
  10. D Dori,W Liu (1998). Stepwise Recovery of Arc Segmentation in Complex Line Environment.
  11. F Liu,G Qiao (2008). A Fast Algorithm of Circle Detection for Industrial CT Images Based on Probability of Existence.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Bing He. 2011. \u201cStudy on Algorithms of Graphic Element Recognition for Precise Vectorization of Industrial Computed Tomographic Image\u201d. Global Journal of Research in Engineering - A : Mechanical & Mechanics GJRE-A Volume 11 (GJRE Volume 11 Issue A5): .

Download Citation

Journal Specifications

Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

Version of record

v1.2

Issue date

September 7, 2011

Language
en
Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

Article Matrices
Total Views: 5815
Total Downloads: 2895
2026 Trends
Related Research

Published Article

Circle, line and circular arc are the common basic graphic elements in industrial computed tomography (ICT) image. The algorithm of recognizing such elements is the key to industrial CT image precise vectorization. An industrial CT image vectorization system has been studied, including different recognition methods for these elements. Firstly, based on facet model, the sub-pixel edge of an industrial CT image is extracted. Then, the circles are recognized by an improved algorithm based on probability of existence map, while the lines are recognized with the set intersection algorithm of fitting a straight line, and the circular arcs are recognized by the combination of the perpendicular bisector tracing algorithm and least squares function. Finally, the graphic element parameters are measured according to recognition results, and the drawing exchange file (DXF) is produced and transmitted into the computer aided design (CAD) system to be edited and consummated. The experimental results show that these methods are capable of recognizing graphic elements in industrial CT image with an excellent accuracy, besides, the absolute errors of circles are less than 0.1 mm, and the relative errors are less than 0.5%. It can satisfy the industrial CT vectorization requirements of higher precision, rapid speed and non-contact.

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

Quote and Order Details

Contact Person

Invoice Address

Notes or Comments

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

Study on Algorithms of Graphic Element Recognition for Precise Vectorization of Industrial Computed Tomographic Image

Dr. Fenglin Liu
Dr. Fenglin Liu
Bing He
Bing He Chongqing University
Bi Bi1
Bi Bi1

Research Journals