Research on techniques for classifying and searching for large-scale volume datasets using critical point graphs
| Project/Area Number |
15500066
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Media informatics/Database
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| Research Institution | KYOTO UNIVERSITY |
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Principal Investigator |
KOYAMADA Koji KYOTO UNIVERSITY, CENTER FOR THE PROMOTION OF EXCELLENCE IN HIGHER EDUCATION, PROFESSOR, 高等教育研究開発推進センター, 教授 (00305294)
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| Project Period (FY) |
2003 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥3,300,000 (Direct Cost: ¥3,300,000)
Fiscal Year 2004: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2003: ¥2,400,000 (Direct Cost: ¥2,400,000)
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| Keywords | Visualization / Critical point graph / Similarity / 類似度計算 / 文字認識技術 / Critical Point / Isosurface / Visualization |
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| Research Abstract |
In this research, we developed a technique of constructing a critical point graph which can represent a volume dataset using a simple geometry in order to facilitate classifying and searching. Hear, we call a numerical dataset which is generated from computer simulation and measurement device as a volume data.
In 2003, we developed an algorithm for constructing a critical point graph from a scalar volume dataset. When we calculate a critical point graph, we trace a vector line from a point which is located a small distance away from a critical point in the direction of the eigen vector of the critical point. Since, currently, we do not have any knowledge about how small distance is adequate, we carried out a sensitivity analysis about how a geometry of the critical point graph changes by changing a starting location of the vector line.
In 2004, we developed a technique of calculating a similarity measurement between volume datasets using their critical point graphs. First, we defined a feature vector of a critical point graph. The feature vector is composed of information on the critical points and their connectivity. The former includes identifiers of critical points, point coordinates, scalar data values, types of critical points.. The latter includes identifiers of arcs, critical points at the both ends, and volume cells along the arcs. We assumed a critical point graph as a character described in 3D and developed a technique of calculating a similarity between critical point graphs by using pattern recognition techniques.
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Report
(3 results)
Research Products
(14 results)
