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Technical Committee on Pattern Recognition and Media Understanding (PRMU)  (Searched in: 2011)

Search Results: Keywords 'from:2011-09-05 to:2011-09-05'

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Search Results: Conference Papers
 Conference Papers (Available on Advance Programs)  (Sort by: Date Ascending)
 Results 1 - 20 of 25  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-05
10:00
Hokkaido   Global Solution of Variational Bayesian Matrix Factorization Under Matrix-wise Independence
Shinichi Nakajima (Nikon), Masashi Sugiyama (Tokyo Inst. of Tech.), Derin Babacan (Illinois Univ.) PRMU2011-58 IBISML2011-17
Variational Bayesian matrix factorization (VBMF) efficiently
approximates the posterior distribution of factorized mat... [more]
PRMU2011-58 IBISML2011-17
pp.1-8
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-05
10:30
Hokkaido   On Evaluation of Stochastic Complexity based on Bayes Code and Its Applications to Model Selection
Yoshinari Takeishi, Masanori Kawakita, Jun'ichi Takeuchi (Kyushu Univ./ISIT) PRMU2011-59 IBISML2011-18
We evaluate stochastic complexity of Gaussian mixture by Bayes code length, and apply it to the model selection problem.... [more] PRMU2011-59 IBISML2011-18
pp.9-14
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-05
11:00
Hokkaido   Multiarmed Bandit Algorithms Based on Empirical Moments
Junya Honda, Akimichi Takemura (Univ. of Tokyo) PRMU2011-60 IBISML2011-19
 [more] PRMU2011-60 IBISML2011-19
pp.15-22
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-05
11:30
Hokkaido   Learning of Kernel Classfier based on General Loss Minimization
Masato Ishii, Atsushi Sato (NEC) PRMU2011-61 IBISML2011-20
This paper presents a new method for learning kernel classifiers. First, we formulate a novel learning scheme called ``G... [more] PRMU2011-61 IBISML2011-20
pp.23-30
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-05
10:00
Hokkaido   Extraction method of red algae for fishery investigation in Laminaria fishery
Takeshi Hagisawa, Koichiro Enomoto, Masashi Toda (Future Univ.-Hakodate), Masakatu Tamura (Habomai Fishery Coop.), Masafumi Kimura (Nemuro Fishieries Extension Office), Sakae Takeda (Rishiri District Fisheries Extension Office) PRMU2011-62 IBISML2011-21
Recently, using an underwater video camera has become an increasingly common way to investigate Laminaria beds. However,... [more] PRMU2011-62 IBISML2011-21
pp.31-36
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-05
10:30
Hokkaido   Extraction Method of Scallop Area Using Space Distribution of Localized Shelly Rim Feature from Seabed Image
Koichiro Enomoto, Masashi Toda (Future Univ. Hakodate), Yasuhiro Kuwahara (Hokkaido Abashiri Fish. Exp. Stn.) PRMU2011-63 IBISML2011-22
This paper describes our method to extract scallop areas from fine sand seabed images from the sand field. In the sand ... [more] PRMU2011-63 IBISML2011-22
pp.37-42
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-05
11:00
Hokkaido   A study on estimation for position and rotation in mobile robots by motion parameters based on memorized image and a current image
Tatsuya Shoji, Yoshinobu Hagiwara, Hiroki Imamura (Soka Univ.) PRMU2011-64 IBISML2011-23
In this paper, we describe a method that estimates position and rotation of mobile robots by using a motion parameter be... [more] PRMU2011-64 IBISML2011-23
pp.43-49
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-05
11:30
Hokkaido   Analysis of Expressive Tempos and Rhythms with Spontaneous Facial Expressions -- Influence of Transitory Stress on Facial Expressions --
Takashi Suto, Hiroaki Otsu, Kazuhito Sato, Hirokazu Madokoro (APU), Sakura Kadowaki (SD) PRMU2011-65 IBISML2011-24
This paper presents a new framework based on tempos and rhythms of facial expressions. In order to clarify the relations... [more] PRMU2011-65 IBISML2011-24
pp.51-58
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-05
13:30
Hokkaido   [Invited Talk] Optimal Solution Path Following Algorithm for Pattern Recognition and Machine Learning
Ichiro Takeuchi (NIT) PRMU2011-66 IBISML2011-25
Many pattern classification and machine learning algorithms are formulated as mathematical optimization problems. These ... [more] PRMU2011-66 IBISML2011-25
pp.59-60
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-05
14:50
Hokkaido   Fast Approximate Nearest Neighbor Search Based on Improved Approximate Distance
Tomokazu Sato, Masakazu Iwamura, Koichi Kise (Osaka Pref. Univ.) PRMU2011-67 IBISML2011-26
(To be available after the conference date) [more] PRMU2011-67 IBISML2011-26
pp.61-66
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-05
15:20
Hokkaido   Feature Selection for Object Tracking based on Online Real Boosting
Takayoshi Yamashita (Omron), Hironobu Fujiyoshi (Chubu University.) PRMU2011-68 IBISML2011-27
Recently, Boosting algorithms like AdaBoost and Real AdaBoost are used in online learning. The weak classifiers for onli... [more] PRMU2011-68 IBISML2011-27
pp.67-73
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-05
15:50
Hokkaido   Feature Extraction Based on Adjacency Relationship of Local Binary Patterns
Ryusuke Nosaka, Yasuhiro Ohkawa, Kazuhiro Fukui (Univ. of Tsukuba) PRMU2011-69 IBISML2011-28
In this paper, we propose a new image feature based on adjacency relationship among local region,
where each local regi... [more]
PRMU2011-69 IBISML2011-28
pp.75-80
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-06
10:00
Hokkaido   Acceleration of boosting discrimination and its application to face detection
Ryota Izumi, Masanori Kawakita, Jun'ichi Takeuchi (kyushu Univ/ISIT), Hu Yi, Tetsuya Takamori, Hirokazu Kameyama (Fuji Film) PRMU2011-70 IBISML2011-29
We propose an acceleration technique for boosting classification.
Not only classification
accuracy and/or training cos... [more]
PRMU2011-70 IBISML2011-29
pp.111-117
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-06
10:30
Hokkaido   Artist Agent (A^2): Stroke Painterly Rendering Based on Reinforcement Learning
Ning Xie, Hirotaka Hachiya, Masashi Sugiyama (Tokyo Inst. of Tech.) PRMU2011-71 IBISML2011-30
 [more] PRMU2011-71 IBISML2011-30
pp.119-125
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-06
10:00
Hokkaido   Improvement of Efficiency and Accuracy of 3D Object Recognition Using Numerous Subspaces of Local Features
Takahiro Kashiwagi, Koichi Kise (Osaka Pregecture Univ.) PRMU2011-72 IBISML2011-31
(To be available after the conference date) [more] PRMU2011-72 IBISML2011-31
pp.133-138
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-06
10:30
Hokkaido   Query by Virtual Example: Utilizing Virtual Reality Techniques to Create Virtual Examples for Constructing Video Retrieval Models
Kimiaki Shirahama, Kuniaki Uehara (Kobe Univ.) PRMU2011-73 IBISML2011-32
 [more] PRMU2011-73 IBISML2011-32
pp.139-144
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-06
11:00
Hokkaido   Object detection based on Multiple Instance LVQ
Toshinori Hosoi, Hiroyoshi Miyano, Eiki Ishidera (NEC Infomatec Systems) PRMU2011-74 IBISML2011-33
This report presents a classifier's training method "Multiple Instance LVQ" which can be trained by roughly labeled dat... [more] PRMU2011-74 IBISML2011-33
pp.145-150
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-06
11:30
Hokkaido   Efficient Learning for Successive TFC by Selective Assessment for defect Classification
Yoshikazu Matsuo (Hokkaido Univ.), Takamichi Kobayashi (NSC), Hidenori Takauji (MIT), Shun'ichi Kaneko (Hokkaido Univ.) PRMU2011-75 IBISML2011-34
We had proposed the method Test Feature Classifier(TFC) as a Nonparametoric Classifier and Successive TFC(sTFC). We prop... [more] PRMU2011-75 IBISML2011-34
pp.151-156
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-06
13:30
Hokkaido   [Invited Talk] Analysis of Image and Video Database and Animation Synthesis
Makoto Okabe (UEC) PRMU2011-76 IBISML2011-35
Image and video analysis is an active research field. Recently, this kind of technology has also been applied to image a... [more] PRMU2011-76 IBISML2011-35
pp.157-158
PRMU, IBISML, IPSJ-CVIM [detail] 2011-09-06
14:50
Hokkaido   A Method for Multiple Instance Learning Using Sparse Kernel Machines
Kazuhisa Nagashima, Masato Inoue (Waseda Univ.) PRMU2011-77 IBISML2011-36
Multiple Instance Learning problem (MIL) is roughly one of the classification problems.
In generally classification pr... [more]
PRMU2011-77 IBISML2011-36
pp.159-163
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