Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
MBE, NC (Joint) |
2022-03-02 16:10 |
Online |
Online |
XMCD-CT Reconstruction Using Compressed Sensing Tsukito Takizawa, Hayaru Shouno (The Univ. of Electro-Communications), Masaichiro Mizumaki (JASRI), Motohiro Suzuki (Kwansei Gakuin Univ.) NC2021-58 |
Observation of the magnetic domain structure is important for understanding the magnetic properties of materials includi... [more] |
NC2021-58 pp.62-67 |
NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-23 11:45 |
Online |
Online |
Adversarial Training with Knowledge Distillation considering Intermediate Feature Representation in CNNs Hikaru Higuchi (The Univ. of Electro-Communications), Satoshi Suzuki (former NTT), Hayaru Shouno (The Univ. of Electro-Communications) NC2021-44 |
Adversarial examples are one of the vulnerability attacks to the convolution neural network (CNN). The adversarialexampl... [more] |
NC2021-44 pp.59-64 |
RCS, SIP, IT |
2022-01-21 10:55 |
Online |
Online |
A lossless audio codec based on hierarchical residual prediction Taiyo Mineo, Shouno Hayaru (UEC) IT2021-71 SIP2021-79 RCS2021-239 |
In this study, we propose a novel lossless audio codec that has precise predictive performance from the neural network a... [more] |
IT2021-71 SIP2021-79 RCS2021-239 pp.239-244 |
NC, MBE (Joint) |
2021-03-03 13:50 |
Online |
Online |
Analysis of deep convolutional neural network texture representation using Portilla-Simoncelli statistics Yusuke Hamano, Hayaru Shouno (UEC) NC2020-48 |
Recently, DCNN has achieved significant success in the field of computer vision. It is suggested that the DCNN, which ar... [more] |
NC2020-48 pp.31-36 |
SIP |
2020-08-28 10:30 |
Online |
Online |
Improvement Convergence Rate of the Sign Algorithm by Natural Gradient Method Taiyo Mineo, Hayaru Shouno (UEC) SIP2020-34 |
In lossless audio compression, it is essential to predictive residuals to be sparse, since we apply entropy codings to r... [more] |
SIP2020-34 pp.19-24 |
NLP, NC (Joint) |
2020-01-24 11:10 |
Okinawa |
Miyakojima Marine Terminal |
Proposal of Compression Method for Planetary Surface Image using Sparse Coding Yoshifumi Uesaka, Hayaru Shouno (UEC) NC2019-65 |
In recent years, the demand for space development has been increasing. We treat an efficient image transmitting system f... [more] |
NC2019-65 pp.33-38 |
NLP, NC (Joint) |
2020-01-25 13:30 |
Okinawa |
Miyakojima Marine Terminal |
Estimation of the high-risk area of potential crime using sparse estimation and consideration of crime occurrence mechanism Sho Ichigozaki (NPA/UEC), Takahiro Kawashima, Hayaru Shouno (UEC) NC2019-71 |
[more] |
NC2019-71 pp.69-74 |
NC, MBE |
2019-12-06 14:40 |
Aichi |
Toyohashi Tech |
Implementation of an FPGA-based energy-efficient MCMC method for 2D Lenz-Ising model Patrick Tchicali, Hayaru Shouno (UEC) MBE2019-54 NC2019-45 |
MCMC methods are arguably one of the most useful sampling methods. MCMC while being very useful and practical remains a ... [more] |
MBE2019-54 NC2019-45 pp.55-60 |
MBE, NC |
2019-10-11 15:00 |
Miyagi |
|
Analysis of diffuse lung disease shadows using Bolasso feature selection method Akihiro Endo (UEC), Kenji Nagata (NIMS), Shoji Kido (Osaka Univ.), Hayaru Shouno (UEC) MBE2019-33 NC2019-24 |
Diffuse lung disease is an intractable disease and abnormal shadows appear on lung X-ray CT images.
Since various patte... [more] |
MBE2019-33 NC2019-24 pp.23-27 |
IT, ISEC, WBS |
2019-03-07 16:55 |
Tokyo |
University of Electro-Communications |
[Invited Talk]
Deepning and evolution of Deep learning technology Hayaru Shouno (UEC) IT2018-88 ISEC2018-94 WBS2018-89 |
[more] |
IT2018-88 ISEC2018-94 WBS2018-89 p.83 |
NC, MBE (Joint) |
2019-03-06 09:55 |
Tokyo |
University of Electro Communications |
A study of inner feature continuity of the VGG model Toya Teramoto, Hyaru Shouno (UEC) NC2018-88 |
Deep Convolutional Neural Network (DCNN) is a successful model in the field of computer vision such like image classifi... [more] |
NC2018-88 pp.239-244 |
NC, MBE (Joint) |
2019-03-06 15:50 |
Tokyo |
University of Electro Communications |
PET Image Reconstruction by use of Dictionary Learning Naohiro OKumura, Hayaru Shouno (UEC) NC2018-85 |
Nowadays, Positron Emission Tomography (PET) scan is focused in the field of pathological diagnosis.In order to obtain a... [more] |
NC2018-85 pp.221-226 |
NC, MBE (Joint) |
2019-03-06 16:15 |
Tokyo |
University of Electro Communications |
Examination of Super Resolution and Noise Removal for MicroCT Image Miku Mashimo, Hayaru Shouno (UEC) NC2018-86 |
The purpose of this research is to increase the resolution of MicroCT (Computed Tomography) images.
The MicroCT image i... [more] |
NC2018-86 pp.227-232 |
NC, MBE (Joint) |
2019-03-06 16:40 |
Tokyo |
University of Electro Communications |
Study on data augmentation using Fourier transform for texture image classification Daigo Nitta, Hayaru Shouno (UEC) NC2018-87 |
In the field of medical imaging such like computed tomography analysis, it is difficult to prepare a sufficient amount o... [more] |
NC2018-87 pp.233-238 |
NLP, NC (Joint) |
2019-01-23 15:40 |
Hokkaido |
The Centennial Hall, Hokkaido Univ. |
Measuring the Convolution Neural Network similarities trained with different dataset using SVCCA Toya Teramoto, Hayaru Shouno (UEC) NC2018-40 |
Deep Convolutional Neural Network (DCNN) is a successful model in the field of computer vision such like image classif... [more] |
NC2018-40 pp.11-16 |
MBE, NC (Joint) |
2017-03-13 13:35 |
Tokyo |
Kikai-Shinko-Kaikan Bldg. |
A Generative Model of Textures Using Hierarchical Probabilistic Principal Component Analysis Aiga Suzuki, Hayaru Shouno (UEC) NC2016-83 |
Modeling of natural textures in an important task for microscopic structure of natural images. Portilla and Simon-
cell... [more] |
NC2016-83 pp.115-120 |
PRMU, IPSJ-CVIM, IBISML [detail] |
2016-09-05 16:15 |
Toyama |
|
The Validity of Network In Network as a Visual System
-- From the Point of View of the Orientation Selectivity Map -- Satoshi Suzuki, Hayaru Shouno (UEC) PRMU2016-68 IBISML2016-23 |
In recent years, Deep Convolutional Neural Network (DCNN) has shown excellent performance in image recognition field. DC... [more] |
PRMU2016-68 IBISML2016-23 pp.113-120 |
SIP |
2016-08-25 15:30 |
Chiba |
Chiba Institute of Technology, Tsudanuma Campus |
[Invited Talk]
Applications of Deep learning for image diagnosis Hayaru Shouno (UEC) SIP2016-76 |
The ``deep learning'' is the 3rd generation neural network technology, which is exhibiting its characteristics in the bi... [more] |
SIP2016-76 pp.23-24 |
MBE, NC (Joint) |
2016-03-23 10:50 |
Tokyo |
Tamagawa University |
A Study of Sparse Feature Learning in the Learning Process of Deep Convolutional Neural Network Yoshihiro Kusano, Hayaru Shouno (UEC) NC2015-81 |
A study to make an image and a video high resolution with the improvement of the display and print technology is conduct... [more] |
NC2015-81 pp.65-70 |
MI |
2015-09-08 14:00 |
Tokyo |
Univ. of Electro-communications |
Feature Selection for Diffuse Lung Disease using MCMC Method Makoto Koiwai (UEC), Maki Isogai (Info Techno Asahi), Hayaru Shouno (UEC), Shoji Kido (Yamaguchi Univ.) MI2015-52 |
(To be available after the conference date) [more] |
MI2015-52 pp.19-24 |