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Journals / Ejovoc (Electronic Journal of Vocational Colleges) / 2012 / Cilt: 2 - Sayı: 2

Obtaining Three Dimensional Point Clouds from the Digital Images of Rectangular Prism Shaped Objects

Gürcan SAMTAŞ
Mahmut GÜLESİN
Pages
160–170
Publication Date
2012-12-31
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this reverse engineering study, the developed system is explained and sample parts are given

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The processes applied on the pixels by the image processing methods provide us information about images.
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Key Findings & Data:Image processing is a computer programming area which can be used in computer integrated industrial applications and it is one of the research areas of reverse engineering.
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Conclusion & Impact:In this reverse engineering study, the developed system is explained and sample parts are given
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Reverse engineering (RE) · 3D point cloud · image processing

Abstract

The processes applied on the pixels by the image processing methods provide us information about images. Image processing is a computer programming area which can be used in computer integrated industrial applications and it is one of the research areas of reverse engineering. In this study, a new system in which color digital images are interpreted by using image processing method was developed. The purpose of this study is to improve a system to interpret the images, which are taken with a hand camcorder or digital camera, and to obtain point clouds by evaluating with image processing and to convert these point clouds surface or solid model in a computer aided design program. In accordance with these purposes, the three dimensional point clouds are obtained from a rectangular prism shaped object in the specified area. These point clouds can easily be converted into three dimensional solid or surface models in a Computer Aided Design (CAD) program. In this reverse engineering study, the developed system is explained and sample parts are given

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Cite This Article

SAMTAŞ, G., GÜLESİN, M. (2012). Obtaining Three Dimensional Point Clouds from the Digital Images of Rectangular Prism Shaped Objects. Ejovoc (Electronic Journal of Vocational Colleges), Cilt: 2 - Sayı: 2, 160–170. https://ajindex.com/articles/252956

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References (12)

  1.  As the number of points in point clouds which are obtained for the object is too much, approximately 1-4 million, in the marked image to obtain its solid model needs some time, In image processing which is done pixel to pixel according to the color analysis, color loss in the image cause regional point loss, which causes some problems to model the part, If the size of the object in the image and processed image is big, the size of the obtained point cloud file becomes big. It is aimed to obtain three dimensional point clouds for circular, curved and complex forms of objects in further studies. Azuma, R., (1995). A survey of augmented reality. Computer Graphics SIGGRAPH Proc., 1-38.
  2. Barbero B.R. and Ureta, E.S. (2011). Comparative study of different digitization techniques and their accuracy. Comp. Aided Design, 43, 188-206.
  3. Benlamri, R. and Al-Marzooqi, Y. (2004). Free-from object segmentation and representation from registered range and color images. Img. and Vis. Comp., 22, 703-717.
  4. Bleser, G., Pastarmov, Y. and Stricker, D. (2005). Real-time 3D camera tracking for industrial augmented reality applications. Journal of Winter Sch. of Compt. Graphics, 47–54.
  5. Bockholt, U., Bisler, A. and Becker, M., Müller-Wittig, W.K. and Voss, G. (2003). Augmented reality for enhancement of endoscopic interventions. Proc. IEEE Virtual Reality Conf., (pp. 97-101).
  6. Los Angeles, USA. Bosche,F. (2010). Automated recognition of 3D CAD model objects in laser scans and calculation. Adv. Eng. Informatics, 24, 107-118.
  7. Boufama, B. and Habed, A. (2004). Three-dimensional structure calculation: achieving accuracy without calibration. Img. and Vis. Comp., 22, 1039-1049.
  8. Byne, J. H. M and Anderson, J. A. D. W. (1998). A CAD-based computer vision system. Img. and Vis. Comp., 16, 533-539.
  9. Dornaika, F. and Chung, R. (2001). An algebraic approach to camera self-calibration Comp. Vis. and Img. Under., 83, 195-215.
  10. Frere, D., Vandekerckhove, J., Moons, T. and Van Gool, L. (1998). Automatic modeling and 3D reconstruction of urban buildings from aerial imagery. IEEE Int. Geoscience and Rem. Sensing Symp. Proc., (pp. 2593-2596).
  11. Seattle. Frueh, C. and Zakhor, A. (2001). 3D model generation for cities using aerial photograps and ground level laser scans. IEEE Conf. on Compt. Vis. and Pat. Recog., (2:2, pp. 31-38).
  12. Kauai, USA. Gaspar, J., Grossmann, E. and Santos-Victor, J. (2001). Interactive reconstruction from an omnidirectional image. 9th Int. Symp. on Intel. Robotic Systems (SIRS’01), (pp