| Paper Abstract and Keywords |
| Presentation |
2020-05-28 10:50
[Special Talk]
High-dimensional Signal Restoration by Convolutional Networks Driving Fusion Across Multiple Disciplines
-- Sparse Modeling and Convolutional Dictionary Learning --
Shogo Muramatsu (Niigata Univ.) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
This talk outlines a restoration process of high-dimensional signals such as image and volumetric data. With the development of measurement technology, it is now possible to acquire a large amount of physical data, and the demand for high performance signal restoration is increasing. In order to achieve high performance signal restoration, a generative model that can effectively represent the physical data of interest is required. The convolutional network model is one of the most powerful existing generative models. The model exploits the local relationships of signals and provides significant performance improvements in image recognition and restoration. However, it is difficult to reflect domain knowledge in the network structure, and theoretically supported systematic and strategic structure setting remains a challenge. Emergent issues occur particularly for data in unknown fields. Therefore, in this project, it is proposed to introduce the results of filter banks and optimization theory into a convolutional network. The purpose is to enable physically interpretable structural settings and to make the design and implementation efficient. We are attempting to create a convolutional network reflecting domain knowledge for various real data such as biological tomographic images, vehicle-mounted millimeter-wave radar images, and river observation data. In this talk, the construction of generative models using filter banks, their parametric learning designs and nonlinear extensions are reviewed. In addition, a comparison with existing methods based on convolutional neural networks (CNNs) is explained. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Sparse modeling / Signal restoration / Signal estimation / Dictionary learning / Convolutional structure / / / |
| Reference Info. |
ITE Tech. Rep., vol. 44, no. 12, ME2020-81, pp. 13-13, May 2020. |
| Paper # |
ME2020-81 |
| Date of Issue |
2020-05-21 (IST, ME) |
| ISSN |
Print edition: ISSN 1342-6893 Online edition: ISSN 2424-1970 |
| Download PDF |
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| Conference Information |
| Committee |
IEICE-MI IEICE-IE IEICE-SIP IEICE-BioX IST ME |
| Conference Date |
2020-05-28 - 2020-05-29 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
Online |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
Image and signal processing/analysis/AI technology, and their application |
| Paper Information |
| Registration To |
ME |
| Conference Code |
2020-05-MI-IE-SIP-BioX-IST-ME |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
High-dimensional Signal Restoration by Convolutional Networks Driving Fusion Across Multiple Disciplines |
| Sub Title (in English) |
Sparse Modeling and Convolutional Dictionary Learning |
| Keyword(1) |
Sparse modeling |
| Keyword(2) |
Signal restoration |
| Keyword(3) |
Signal estimation |
| Keyword(4) |
Dictionary learning |
| Keyword(5) |
Convolutional structure |
| Keyword(6) |
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| Keyword(7) |
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| 1st Author's Name |
Shogo Muramatsu |
| 1st Author's Affiliation |
Niigata University (Niigata Univ.) |
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| Speaker |
Author-1 |
| Date Time |
2020-05-28 10:50:00 |
| Presentation Time |
50 minutes |
| Registration for |
ME |
| Paper # |
IST2020-30, ME2020-81 |
| Volume (vol) |
vol.44 |
| Number (no) |
no.12 |
| Page |
p.13 |
| #Pages |
1 |
| Date of Issue |
2020-05-21 (IST, ME) |
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