An exploration of robust model structures for image variations based on statistical approaches
| Project/Area Number |
19700165
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Nagoya Institute of Technology |
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Principal Investigator |
NANKAKU Yoshihiko Nagoya Institute of Technology, 大学院・工学研究科, 助教 (80397497)
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| Project Period (FY) |
2007 – 2009
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| Project Status |
Completed (Fiscal Year 2009)
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| Budget Amount *help |
¥3,710,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥510,000)
Fiscal Year 2009: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2008: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2007: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | パターン認識 / 画像認識 / 統計モデル / 隠れマルコフモデル / 特徴抽出 / 確率的主成分分析 / 因子分析 |
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| Research Abstract |
The objective of this research is to find general model structures which are robust to image variations for image recognition. Separable lattice HMMs (SL-HMM) have been proposed as statistical models which can deal with location and size variations. This research proposed a new model structure which integrates linear feature extraction based on probabilistic principal component analysis and factor analysis into SL-HMMs. Furthermore, SL-HMMs were extended to models which can represent rotation variations and which include explicit state duration models.
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Report
(4 results)
Research Products
(14 results)
