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Search Results: Conference Papers
 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 6件中 1~6件目  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
PRMU 2019-12-20
10:00
Oita   Analysis method and results of solution diversity about topic model
Toshio Uchiyama, Tsukasa Hokimoto (HIU) PRMU2019-54
Probabilistic Latent Semantic Analysis and Latent Dirichlet analysis are
known as topic models to analyze text data an... [more]
PRMU2019-54
pp.49-54
PRMU, SP 2018-06-28
16:30
Nagano   Analysis of solution diversity about topic model
Toshio Uchiyama (HIU) PRMU2018-26 SP2018-6
A Topic model is known as a useful statistical model for analyzing text
data and images. Many different parameters (s... [more]
PRMU2018-26 SP2018-6
pp.25-30
PRMU, SP 2017-06-22
17:00
Miyagi   Investigation of solution diversity about probabilistic latent semantic analysis using normalized mutual information
Toshio Uchiyama (Hokkaido Info. Univ.) PRMU2017-31 SP2017-7
Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet
analysis are known as topic models to analyze text ... [more]
PRMU2017-31 SP2017-7
pp.33-38
PRMU, MI, IE, SIP 2015-05-15
15:40
Mie   Elastic matching method using NMI and application to chest CT-MR registration
Mitsuki Watanabe, Yuma Iwao, Toshiyuki Gotoh (YNU), Tae Iwasawa (Kanagawa Cardiovascular and Respiratory Center) SIP2015-29 IE2015-29 PRMU2015-29 MI2015-29
NMI(Normalized Mutual Information) is considered to be effective for a multi-modality medical image registration. Howeve... [more] SIP2015-29 IE2015-29 PRMU2015-29 MI2015-29
pp.155-160
MI 2013-01-25
10:00
Okinawa Bunka Tenbusu Kan Multimodal Image Registration Using Improved Normalized Mutual Information
Ayako Kobayashi, Hiroyuki Kudo, Taizo Suzuki (Univ. of Tsukuba) MI2012-96
Medical image registration technique is necessary. Behind this we can digitize images from kind of modalities at medical... [more] MI2012-96
pp.179-184
MI 2012-01-19
11:20
Okinawa   Accelerating Normalized Mutual Information based Nonrigid Registration using CUDA
Kei Ikeda, Fumihiko Ino, Kenichi Hagihara (Osaka Univ.) MI2011-98
This paper presents an acceleration method using the compute unified device architecture (CUDA), aiming at accelerating ... [more] MI2011-98
pp.115-120
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