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Feature-preserving reduction of industrial volume data using gray level co-occurrence matrix texture analysis and mass-spring model

[+] Author Affiliations
Seongtae Kang

Seoul National University, School of Computer Science and Engineering, 599 Kwanak-ro, Kwanak-gu, Seoul 151-742, Republic of Korea

Jeongjin Lee

Soongsil University, School of Computer Science & Engineering, 369 Sangdo-Ro, Dongjak-Gu, Seoul 156-743, Republic of Korea

Ho Chul Kang

Seoul National University, School of Computer Science and Engineering, 599 Kwanak-ro, Kwanak-gu, Seoul 151-742, Republic of Korea

Juneseuk Shin

Sungkyunkwan University, Department of Systems Management Engineering, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeong gi-do 440-746, Republic of Korea

Yeong-Gil Shin

Seoul National University, School of Computer Science and Engineering, 599 Kwanak-ro, Kwanak-gu, Seoul 151-742, Republic of Korea

J. Electron. Imaging. 23(1), 013022 (Feb 14, 2014). doi:10.1117/1.JEI.23.1.013022
History: Received September 19, 2013; Revised January 14, 2014; Accepted January 20, 2014
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Abstract.  We propose an innovative method that reduces the size of three-dimensional (3-D) volume data while preserving important features in the data. Our method quantifies the importance of features in the 3-D data based on gray level co-occurrence matrix texture analysis and represents the volume data using a simple mass-spring model. According to the measured importance value, blocks containing important features expand while other blocks shrink. After deformation, small features are exaggerated on deformed volume space, and more likely to survive during the uniform volume reduction. Experimental results showed that our method well preserved the small features of the original volume data during the reduction without any artifact compared with the previous methods. Although an additional inverse deformation process was required for the rendering of the deformed volume data, the rendering speed of the deformed volume data was much faster than that of the original volume data.

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Citation

Seongtae Kang ; Jeongjin Lee ; Ho Chul Kang ; Juneseuk Shin and Yeong-Gil Shin
"Feature-preserving reduction of industrial volume data using gray level co-occurrence matrix texture analysis and mass-spring model", J. Electron. Imaging. 23(1), 013022 (Feb 14, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.1.013022


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