| Project/Area Number |
21700127
|
|---|
| Research Category |
Grant-in-Aid for Young Scientists (B)
|
| Allocation Type | Single-year Grants |
|---|
| Research Field |
Media informatics/Database
|
|---|
| Research Institution | The Open University of Japan |
|---|
Principal Investigator |
SUZUKI Motofumi The Open University of Japan, ICT活用・遠隔教育センター, 准教授 (90332147)
|
|---|
| Project Period (FY) |
2009 – 2010
|
| Project Status |
Completed (Fiscal Year 2010)
|
| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2010: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2009: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
|
|---|
| Keywords | 類似検索 / 3次元物体モデル / 3次元高次局所自己相関特徴 / フラクタル次元 / ソリッドテクスチャ / テクスチャ特徴 / 高次局所自己相関特徴 / ボリュームデータ / ポリゴンモデル |
|---|
| Research Abstract |
Various kinds of research have been conducted on similarity retrievals of 3D models since the late 1990's. Although most of the retrieval techniques focus on shape similarity of the 3D models, our technique allows users to retrieve and classify 3D models based on texture pattern similarity. To test our texture similarity retrieval technique, a set of a textured 3D model database was synthesized from 3D polygonal models and 2D texture images. The database was analyzed by software programs, and texture features were extracted from each 3D model. The extracted texture features were computed based on HLAC (higher order local autocorrelation) and fractal dimensions. Often, both kinds of texture features were used for analyzing 2D texture images. However, we extended the techniques to handle three dimensional volumetric data for extracting features from textured 3D models. Our experimental web-based retrieval system successfully retrieved textured 3D models with fairly acceptable recall-precision rates. This retrieval technique which is based on texture patterns can be used in conjunction with traditional shape similarity retrieval techniques, and the technique can enhance similarity retrieval performances.
|
