| A genetic algorithm for determining multiple routes and its applications J Inagaki, M Haseyama, H Kitajima 1999 IEEE International Symposium on Circuits and Systems (ISCAS) 6, 137-140, 1999 | 181 | 1999 |
| Fast and robust ellipse extraction from complicated images E Kim, M Haseyama, H Kitajima Proceedings of IEEE information technology and applications, 2002 | 101 | 2002 |
| The simplest measurement matrix for compressed sensing of natural images Z He, T Ogawa, M Haseyama 2010 IEEE International Conference on Image Processing, 4301-4304, 2010 | 80 | 2010 |
| Image classification device and image classification program M Haseyama US Patent 8,180,161, 2012 | 76 | 2012 |
| A soccer field tracking method with wire frame model from TV images T Watanabe, M Haseyama, H Kitajima 2004 International Conference on Image Processing, 2004. ICIP'04. 3, 1633-1636, 2004 | 67 | 2004 |
| Soft-label anonymous gastric x-ray image distillation G Li, R Togo, T Ogawa, M Haseyama 2020 IEEE International Conference on Image Processing (ICIP), 305-309, 2020 | 55 | 2020 |
| A map matching method with the innovation of the Kalman filtering T Jo, M Haseyama, H Kitajima IEICE transactions on fundamentals of electronics, communications and …, 1996 | 51 | 1996 |
| Favorite video classification based on multimodal bidirectional LSTM T Ogawa, Y Sasaka, K Maeda, M Haseyama IEEE Access 6, 61401-61409, 2018 | 50 | 2018 |
| Image inpainting based on sparse representations with a perceptual metric T Ogawa, M Haseyama EURASIP Journal on Advances in Signal Processing 2013, 1-26, 2013 | 50 | 2013 |
| Convolutional sparse coding‐based deep random vector functional link network for distress classification of road structures K Maeda, S Takahashi, T Ogawa, M Haseyama Computer‐Aided Civil and Infrastructure Engineering 34 (8), 654-676, 2019 | 48 | 2019 |
| [Survey paper] a review of video retrieval based on image and video semantic understanding M Haseyama, T Ogawa, N Yagi ITE Transactions on Media Technology and Applications 1 (1), 2-9, 2013 | 48 | 2013 |
| Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography R Togo, N Yamamichi, K Mabe, Y Takahashi, C Takeuchi, M Kato, ... Journal of gastroenterology 54, 321-329, 2019 | 46 | 2019 |
| A filter coefficient quantization method with genetic algorithm, including simulated annealing M Haseyama, D Matsuura IEEE Signal Processing Letters 13 (4), 189-192, 2006 | 46 | 2006 |
| Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps R Togo, K Hirata, O Manabe, H Ohira, I Tsujino, K Magota, T Ogawa, ... Computers in biology and medicine 104, 81-86, 2019 | 45 | 2019 |
| Compressed gastric image generation based on soft-label dataset distillation for medical data sharing G Li, R Togo, T Ogawa, M Haseyama Computer Methods and Programs in Biomedicine 227, 107189, 2022 | 44 | 2022 |
| Missing intensity interpolation using a kernel PCA-based POCS algorithm and its applications T Ogawa, M Haseyama IEEE Transactions on Image Processing 20 (2), 417-432, 2010 | 40 | 2010 |
| Fractal interpolation for natural images H Honda, M Haseyama, H Kitajima Proceedings 1999 International Conference on Image Processing (Cat …, 1999 | 39 | 1999 |
| A cross-modal approach for extracting semantic relationships between concepts using tagged images M Katsurai, T Ogawa, M Haseyama IEEE transactions on multimedia 16 (4), 1059-1074, 2014 | 38 | 2014 |
| Synthetic gastritis image generation via loss function-based conditional pggan R Togo, T Ogawa, M Haseyama IEEE access 7, 87448-87457, 2019 | 34 | 2019 |
| Fast line extraction from digital images using line segments E Kim, M Haseyama, H Kitajima Systems and Computers in Japan 34 (10), 76-89, 2003 | 34 | 2003 |
Takahiro OgawaHokkaido UniversityDirección de correo verificada de lmd.ist.hokudai.ac.jp