| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2017: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Outline of Final Research Achievements |
In this study, we have proposed various feature transformation methods to enhance the discriminative power of features. In general, the feature to represent the content of input data contains structural information which is derived from the characteristics of feature extractors and input data distribution. The proposed methods leverage the essential structures to improve the discriminativity of the features. Those methods are formulated especially by focusing on the deblurring of histogram, prior probabilistic models, physical structures and invariance to input data perturbation. We can apply the methods in a computationally efficient manner, while contributing to the improvement of feature representation as well as performance of the whole recognition systems.
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