| Project/Area Number |
23500225
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Hosei University |
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Principal Investigator |
WAKAHARA Toru 法政大学, 情報科学部, 教授 (40339510)
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| Project Period (FY) |
2011 – 2013
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Fiscal Year 2013: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2012: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2011: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | パターン認識 / 変形耐性画像マッチング / 画像マッチング / 変形耐性 |
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| Research Abstract |
1. A new, affine-invariant image matching technique via KL (Kullback-Leibler) divergence minimization was proposed. First, we represented a grayscale image as a probability distribution. Second, we introduced KL divergence between an affine-transformed input image and a template using their corresponding probabilty distributions. Finally, we determined optimal affine parameters that minimize KL divergence using an iterative method.
2. A drastic acceleration of GAT correlation to realize distortion-tolerant image matching with far less computational burden was proposed. The key ideas were separation of variables and generation of lookup tables in the original GAT computational model. A powerful combination of k-NN classification and accelerated GAT correlation techniques achieved the state-of-the-art recognition accuracy in handwritten numeral recognition.
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