| 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 | 21 | 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 | 19 | 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 |
| 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 | 5 | 2017 |
| 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 |
| 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 |
| Publications: Hiroki Arimura T Takagi, S Inenaga Algorithms 11, 128, 2018 | | 2018 |
| 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 |
Hiroki ArimuraGraduate School of Information Science and Technology, Hokkaido University, ProfessorVerified email at ist.hokudai.ac.jp
