| Multi-viewpoint silhouette extraction with 3D context-aware error detection, correction, and shadow suppression S Nobuhara, Y Tsuda, I Ohama, T Matsuyama IPSJ Transactions on Computer Vision and Applications 1, 242-259, 2009 | 20 | 2009 |
| Content output device, content output method, program, program recording medium, and content output integrated circuit H Iida, I Ohama, S Ohtsubo US Patent 8,732,149, 2014 | 18 | 2014 |
| An extension of the infinite relational model incorporating interaction between objects I Ohama, H Iida, T Kida, H Arimura Pacific-Asia Conference on Knowledge Discovery and Data Mining, 147-159, 2013 | 8 | 2013 |
| Multi-layered framework for modeling relationships between biased objects I Ohama, T Kida, H Arimura Proceedings of the 2015 SIAM International Conference on Data Mining, 819-827, 2015 | 5 | 2015 |
| On the model shrinkage effect of gamma process edge partition models I Ohama, I Sato, T Kida, H Arimura Advances in Neural Information Processing Systems, 397-405, 2017 | 4 | 2017 |
| 多視点画像を用いた誤り検出・訂正機能を持つ高精度対象領域抽出法 大浜郁， 延原章平， 松山隆司 MIRU2006, 688-693, 2006 | 4 | 2006 |
| The relevance dependent infinite relational model for discovering co-cluster structure from relationships with structured noise I Ohama, H Iida, T Kida, H Arimura IEICE TRANSACTIONS on Information and Systems 99 (4), 1139-1152, 2016 | 3 | 2016 |
| Co-clustering apparatus, co-clustering method, recording medium, and integrated circuit I Ohama US Patent App. 14/054,890, 2014 | 3 | 2014 |
| Discovering Relevance-Dependent Bicluster Structure from Relational Data. I Ohama, T Kida, H Arimura IJCAI, 2578-2584, 2017 | 2 | 2017 |
| Demand prediction method, demand prediction apparatus, and non-transitory computer-readable recording medium R Fujimura, I Ohama, H Umetani, Y Shoda US Patent App. 15/184,985, 2017 | 2 | 2017 |
| Discovering Co-Cluster Structure from Relationships between Biased Objects I Ohama, T Kida, H Arimura IEICE Transactions on Information and Systems 101 (12), 3108-3122, 2018 | 1 | 2018 |
| Discovering Relevance-Dependent Bicluster Structure from Relational Data: A Model and Algorithm I Ohama, T Kida, H Arimura Transactions of the Japanese Society for Artificial Intelligence 33 (6), B …, 2018 | 1 | 2018 |
| A machine learning approach for lighting perception analysis via crowdsourcing Y Minoda, I Ohama, E Muramoto Adjunct Proceedings of the 2015 ACM International Joint Conference on …, 2015 | 1 | 2015 |
| Information classification device, information classification method, and program for classifying information I Ohama, H Iida US Patent App. 13/393,840, 2012 | 1 | 2012 |
| Anomaly detection method, learning method, anomaly detection device, and learning device T Oba, I Ohama US Patent App. 16/218,975, 2019 | | 2019 |
| Relevance-dependent Biclustering Models for Relational Data Analysis 大濵郁 | | 2019 |
| Method for estimating use state of power of electric devices Y Shoda, I Ohama, R Fujimura, H Umetani US Patent App. 15/251,794, 2017 | | 2017 |
| Data analysis method, data analysis apparatus, and recording medium having recorded program H Umetani, I Ohama, R Fujimura, Y Shoda US Patent App. 15/169,767, 2016 | | 2016 |
| Display device and method of switching display direction R Kamei, N Matsui, T Matsumoto, S Ohtsubo, I Ohama US Patent 9,507,379, 2016 | | 2016 |
| 大規模関係データからのフルランクでない共クラスタ構造発見のための効率的なテンソルスペクトル分解手法の提案 (情報論的学習理論と機械学習) 梅谷英生， 大濱郁 電子情報通信学会技術研究報告= IEICE technical report: 信学技報 115 (511), 39-46, 2016 | | 2016 |
Hiroki ArimuraGraduate School of Information Science and Technology, Hokkaido University, ProfessorVerified email at ist.hokudai.ac.jp