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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 10件中 1~10件目  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
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
IMQ 2019-10-04
14:00
Osaka Osaka University 3D CG Image Quality Assessment Including Noise Removal Based on Sparse Dictionary Learning Coding
Norifumi Kawabata (Tokyo Univ. of Science) IMQ2019-6
By appearing of high-definition and high-quality images, it comes to increase many chance to process image big data. If ... [more] IMQ2019-6
pp.1-10
SIS, IPSJ-AVM, ITE-3DIT [detail] 2019-06-13
14:25
Nagasaki Fukue Culture Center Single Image Super-Resolution for being flexible to downsampling kernel by using self-examplers
Shogo Seta, Takuro Yamaguchi, Masaaki Ikehara (Keio Univ) SIS2019-6
We propose a fast single super resolution which is comparable image quality with conventional methods without being bas... [more] SIS2019-6
pp.29-34
EMM 2019-03-14
11:30
Okinawa TBD Image Patch Modeling using Secure Computation of Sparse Dictionary Learning
Takayuki Nakachi (NTT), Hitoshi Kiya (Tokyo Metro. Univ.) EMM2018-115
With the advent of the big data era, digital content continues to increase.
Sparse coding is attracting attention as an... [more]
EMM2018-115
pp.129-134
SIS 2019-03-06
15:10
Tokyo Tokyo Univ. Science, Katsushika Campus Secure Computation of Sparse Dictionary Learning
Takayuki Nakachi, Yukihiro Bandoh (NTT), Hitoshi Kiya (Tokyo Metro. Univ.) SIS2018-43
With the advent of the big data era, all digital contents continue to increase. Sparse modeling is drawing attention as ... [more] SIS2018-43
pp.35-40
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
ITS, IE, ITE-MMS, ITE-HI, ITE-ME, ITE-AIT [detail] 2019-02-20
14:45
Hokkaido Hokkaido Univ. Encrypted Image Classification by Using Secure OMP Computation
Takayuki Nakachi (NTT), Hitoshi Kiya (Tokyo Metro. Univ.) ITS2018-84 IE2018-105
Currently, huge amounts of image/video are being recorded and uploaded every day by surveillance systems or SNS services... [more] ITS2018-84 IE2018-105
pp.227-232
IBISML 2016-03-18
11:05
Tokyo Institute of Statistical Mathematics Block-sparse Extensions of Recovery Conditions of Overcomplete Dictionaries
Yasushi Terazono, Kenji Yamanishi (UTokyo) IBISML2015-101
In overcomplete dictionary learning problems, observed data are modeled as products of overcomplete dictionaries and spa... [more] IBISML2015-101
pp.55-58
SP, IPSJ-MUS 2014-05-25
11:30
Tokyo   A joint restricted Boltzmann machine for dictionary learning in sparse-representation-based voice conversion
Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.) SP2014-34
In voice conversion, sparse-representation-based methods have recently been garnering attention because they are, relati... [more] SP2014-34
pp.343-348
SIP, CAS, CS 2013-03-14
14:40
Yamagata Keio Univ. Tsuruoka Campus (Yamagata) Self-learning super resolution by L2-norm
Daiki Azuma (Keio Univ.), Kazu Mishiba (Tottori Univ.), Masaaki Ikehara (Keio Univ.) CAS2012-106 SIP2012-137 CS2012-112
In this paper, we propose a single image super resolution technique by L2 approximation without any training. Recently i... [more] CAS2012-106 SIP2012-137 CS2012-112
pp.57-61
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