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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 68件中 1~20件目  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
NC, MBE
(Joint)
2020-03-06
16:10
Tokyo University of Electro Communications Sparse modeling of deep classification networks with layer-wise greedy learning and various regularization terms
Masumi Ishikawa (Kyutech) NC2019-116
Training of deep networks is difficult due to vanishing gradients. To overcome this difficulty, layer-wise greedy learni... [more] NC2019-116
pp.231-236
NLC, IPSJ-NL, SP, IPSJ-SLP
(Joint) [detail]
2019-12-06
13:55
Tokyo NHK Science & Technology Research Labs. [Poster Presentation] Time-Varying Complex AR speech analysis based on l2-norm regularization
Keiichi Funaki (Univ. of the Ryukyus) SP2019-41
Linear prediction (LP) is a mathematical operation estimating an all-pole spectrum from the speech
signal. It is an ess... [more]
SP2019-41
pp.73-77
ITE-BCT, SIS 2019-10-24
15:30
Fukui Fukui International Activities Plaza Image Regularization with Total Variation and Morphological Gradient Priors Using Optimization of Structuring Element for Each Pixel
Shoya Oohara, Hirotaka Oka, Mistuji Muneyasu, Soh Yoshida (Kansai Univ.), Makoto Nakashizuka (CIT) SIS2019-17
As an image prior for image restoration, a method using the sum of morphological gradients has been proposed. Optimizati... [more] SIS2019-17
pp.47-52
IMQ, IE, MVE, CQ
(Joint) [detail]
2019-03-14
13:55
Kagoshima Kagoshima University Multi Frame Super-Resolution Magnification method using TV Regularization and Learning-based Method
Taiki Kondo, Hiroto Kizuna, Hiromasa Takeda, Hiroyuki Sato (Iwate Pref. Univ.) IMQ2018-38 IE2018-122 MVE2018-69
A method combining a Based learning method and ShockFilter for Total Variation (TV) regularization, which is one of supe... [more] IMQ2018-38 IE2018-122 MVE2018-69
pp.91-96
IMQ, IE, MVE, CQ
(Joint) [detail]
2019-03-14
14:20
Kagoshima Kagoshima University Implementation and Evaluation of Total Variation Regularization Decomposition for Super Resolution using an Inexpensive Single Board Computer
Hiromasa Takeda, Taiki Kondo, Hiroto Kizuna, Hiroyuki Sato, Eiji Sugino (Iwate Pref. Univ.) IMQ2018-39 IE2018-123 MVE2018-70
With the advent of large and high resolution displays in recent years, large screen electronic signage such as digital s... [more] IMQ2018-39 IE2018-123 MVE2018-70
pp.97-102
EA, SIP, SP 2019-03-15
13:30
Nagasaki i+Land nagasaki (Nagasaki-shi) [Poster Presentation] F0 estimation using TV-CAR speech analysis based on Regularized LP
Keiichi Funaki (Univ. of the Ryukyus) EA2018-152 SIP2018-158 SP2018-114
Linear Prediction (LP) analysis is speech analysis to estimate AR(Auto-Regressive) coefficients to represent the all-pol... [more] EA2018-152 SIP2018-158 SP2018-114
pp.311-316
IT, ISEC, WBS 2019-03-08
10:15
Tokyo University of Electro-Communications Typical performance of the L1 regularization regression from linear measurements with measurement noise and large coherence
Minori Ihara, Kazunori Iwata, Kazushi Mimura (Hiroshima City Univ.) IT2018-117 ISEC2018-123 WBS2018-118
We evaluate typical performance of compressed sensing in the case where iterative recovery algorithms fail to converge. ... [more] IT2018-117 ISEC2018-123 WBS2018-118
pp.257-262
RCS, SIP, IT 2019-01-31
10:15
Osaka Osaka University A Study on Regularization Parameter in OFDM Communication Using Sparse Channel Estimation
Kenta Kawahara, Takahiro Natori (Tokyo Univ. of Science), Takashi Yoshida (TMCIT), Akira Nakamura, Makoto Itami, Naoyuki Aikawa (Tokyo Univ. of Science) IT2018-38 SIP2018-68 RCS2018-245
In recent years, sparse estimation using signal sparsity, which is one solution to the inverse problem, attracts attenti... [more] IT2018-38 SIP2018-68 RCS2018-245
pp.19-24
RCS, SIP, IT 2019-02-01
15:35
Osaka Osaka University A study of physical layer security using L1 regularized channel estimation techniques
Yasuhiro Takano (Kove Univ.) IT2018-69 SIP2018-99 RCS2018-276
An L1 regularized channel estimation technique can, even with a short training sequence (TS), achieve estimation perform... [more] IT2018-69 SIP2018-99 RCS2018-276
pp.197-201
NLP, NC
(Joint)
2019-01-24
15:20
Hokkaido The Centennial Hall, Hokkaido Univ. A New Method for Deriving Multiplicative Update Rules for NMF with Error Functions Containing Logarithm
Akihiro Koso, Norikazu Takahashi (Okayama Univ.) NLP2018-122
Nonnegative Matrix Factorization (NMF) is an operation that decomposes a given nonnegative matrix X into two nonnegative... [more] NLP2018-122
pp.137-142
CAS, SIP, MSS, VLD 2018-06-14
14:10
Hokkaido Hokkaido Univ. (Frontier Research in Applied Sciences Build.) A Study on Reflection Removal Using Depth Map
Toshihiro Shibata, Yuji Akai, Ryo Matsuoka (Kagawa Univ.) CAS2018-8 VLD2018-11 SIP2018-28 MSS2018-8
In this paper, we propose a novel reflection removal method for RGB-D images that achieves reflection removal and depth ... [more] CAS2018-8 VLD2018-11 SIP2018-28 MSS2018-8
pp.39-43
SIS, IPSJ-AVM, ITE-3DIT [detail] 2018-06-08
11:10
Hokkaido Jozankei View Hotel Image Regularization with Morphological Gradients Priors Using Optimization of Multiple Structuring Elements
Hirotaka Oka, Shoya Oohara, Mitsuji Muneyasu, Soh Yoshida (Kansai Univ.), Makoto Nakashizuka (C.I.T.) SIS2018-7
An image restoration method using morphological gradients as an image prior and optimizing a structuring element by a ge... [more] SIS2018-7
pp.63-68
PRMU, MI, IE, SIP 2018-05-17
15:15
Gifu   On OCT Volumetric Data Restoration via Hierarchical Sparsity and Hard Constraint
Shogo Muramatsu, Satoshi Nagayama, Samuel Choi (Niigata Univ.), Shunsuke Ono (Tokyo Institute of Tech.), Takeru Ota, Fumiaki Nin, Hiroshi Hibino (Niigata Univ.) SIP2018-3 IE2018-3 PRMU2018-3 MI2018-3
This work proposes a novel restoration method for optical coherence tomography (OCT) data. OCT is a measurement techniqu... [more] SIP2018-3 IE2018-3 PRMU2018-3 MI2018-3
pp.7-12
SIP, IT, RCS 2018-01-22
13:55
Kagawa Sunport Hall Takamatsu Hyperspectral Image Restoration
Ryuji Kurihara, Masahiro Okuda (Kitayu U.) IT2017-73 SIP2017-81 RCS2017-287
We propose a new regularization function for hyperspectral image (HSI) restoration. Spatial-smoothness-based regularizat... [more] IT2017-73 SIP2017-81 RCS2017-287
pp.107-111
SIS 2017-12-14
14:30
Tottori Tottori Prefectural Center for Lifelong Learning A Method for Image Regularization with Morphological Gradient Priors Considering Optimization of SE
Yudai Ikeshita, Mitsuji Muneyasu (Kansai Univ.), Makoto Nakashizuka (CIT), Soh Yoshida (Kansai Univ.) SIS2017-40
An image restoration by image regularization with morphological gradient priors has been proposed. In the method, the se... [more] SIS2017-40
pp.39-44
IBISML 2017-11-10
13:00
Tokyo Univ. of Tokyo [Poster Presentation] Proposal of λ-scan Method in Spectral Deconvolution
Yohachi Mototake (Univ of Tokyo), Yasuhiko Igarashi (NIMS), Hikaru Takenaka (Univ of Tokyo), Kenji Nagata (AIST), Masato Okada (Univ of Tokyo) IBISML2017-80
Spectral deconvolution is a method to fit spectral data as the sum of unimodal basis functions and is a useful method in... [more] IBISML2017-80
pp.325-332
IBISML 2017-11-10
13:00
Tokyo Univ. of Tokyo [Poster Presentation] Effect of maximum likelihood estimation after L1 regularization in learning of log-linear models
Kazuya Takabatake, Shotaro Akaho (AIST) IBISML2017-86
$L_1$ regularization has two functions.
One function is the structure learning by parameter reduction, and another func... [more]
IBISML2017-86
pp.369-375
WBS, MICT 2017-07-13
13:45
Shizuoka ACT CITY [Poster Presentation] Performance evaluation of blind time-variant channel estimation using L1 regularization for OFDM systems
Naoto Murakami, Teruyuki Miyajima (Ibaraki Univ.) WBS2017-8 MICT2017-10
In this article, we propose a blind channel estimation method for time-variant channels in OFDM transmission. The propos... [more] WBS2017-8 MICT2017-10
pp.1-6
SIS 2017-06-01
10:30
Oita Housen-Sou (Beppu) Image Regularization with Morphological Gradient Priors Using Optimization of Structure Element
Shoya Oohara, Yuudai Ikeshita, Mitsuji Muneyasu, Soh Yoshida (Kansai Univ.), Makoto Nakashizuka (C.I.T) SIS2017-3
An image restoration method based on morphological gradients has been proposed. In this method, the sum of the morpholog... [more] SIS2017-3
pp.13-18
NLP 2017-05-11
16:00
Okayama Okayama University of Science Multiplicative Update Rules for Nonnegative Matrix Factorization with Regularization Terms
Akihiro Koso, Norikazu Takahashi (Okayama Univ.) NLP2017-13
Nonnegative Matrix Factorization (NMF) is an operation that decomposes a given nonnegative matrix into two nonnegative f... [more] NLP2017-13
pp.63-68
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