| Semi-supervised hyperspectral classification from a small number of training samples using a co-training approach M Romaszewski, P Głomb, M Cholewa ISPRS Journal of Photogrammetry and Remote Sensing 121, 60-76, 2016 | 61 | 2016 |
| Application of hyperspectral imaging and machine learning methods for the detection of gunshot residue patterns P Głomb, M Romaszewski, M Cholewa, K Domino Forensic science international 290, 227-237, 2018 | 35 | 2018 |
| Blood stain classification with hyperspectral imaging and deep neural networks K Książek, M Romaszewski, P Głomb, B Grabowski, M Cholewa Sensors 20 (22), 6666, 2020 | 30 | 2020 |
| Estimation of the number of states for gesture recognition with Hidden Markov Models based on the number of critical points in time sequence M Cholewa, P Głomb Pattern Recognition Letters 34 (5), 574-579, 2013 | 30 | 2013 |
| A dataset for evaluating blood detection in hyperspectral images M Romaszewski, P Głomb, A Sochan, M Cholewa Forensic science international 320, 110701, 2021 | 25 | 2021 |
| Quantum hidden Markov models based on transition operation matrices M Cholewa, P Gawron, P Głomb, D Kurzyk Quantum Information Processing 16, 1-19, 2017 | 19 | 2017 |
| A spatial-spectral disagreement-based sample selection with an application to hyperspectral data classification M Cholewa, P Głomb, M Romaszewski IEEE Geoscience and Remote Sensing Letters 16 (3), 467-471, 2019 | 11 | 2019 |
| Natural human gestures classification using multisensor data M Cholewa, P Głomb 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), 499-503, 2015 | 5 | 2015 |
| Detection of emergent leaks using machine learning approaches P Głomb, M Cholewa, W Koral, A Madej, M Romaszewski Water Supply 23 (6), 2370-2386, 2023 | 4 | 2023 |
| Improving autoencoder training performance for hyperspectral unmixing with network reinitialisation K Książek, P Głomb, M Romaszewski, M Cholewa, B Grabowski, K Búza International Conference on Image Analysis and Processing, 391-403, 2022 | 4 | 2022 |
| Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images P Głomb, K Domino, M Romaszewski, M Cholewa arXiv preprint arXiv:1808.03513, 2018 | 4 | 2018 |
| Classification of dynamic sequences of 3d point clouds M Cholewa, P Sporysz International Conference on Artificial Intelligence and Soft Computing, 672-683, 2014 | 4 | 2014 |
| Gesture data modeling and classification based on critical points approximation M Cholewa, P Głomb Computer Recognition Systems 4, 307-315, 2011 | 4 | 2011 |
| Adaptive, hubness-aware nearest neighbour classifier with application to hyperspectral data M Romaszewski, P Głomb, M Cholewa Computer and Information Sciences: 32nd International Symposium, ISCIS 2018 …, 2018 | 3 | 2018 |
| Two stage SVM classification for hyperspectral data M Cholewa, P Glomb International Conference on Pattern Recognition Applications and Methods 2 …, 2016 | 3 | 2016 |
| Deciding of HMM parameters based on number of critical points for gesture recognition from motion capture data M Cholewa, P Głomb arXiv preprint arXiv:1110.6287, 2011 | 3 | 2011 |
| ‘Just One More Sensor is Enough’–Iterative Water Leak Localization with Physical Simulation and a Small Number of Pressure Sensors M Cholewa, M Romaszewski, P Głomb, K Kołodziej, M Gorawski, J Koral, ... IEEE Sensors Journal, 2024 | 1 | 2024 |
| Machine Learning for Water Leak Detection and Localization in the WaterPrime Project P Głomb, M Romaszewski, M Cholewa, W Koral, A Madej, M Skrabski, ... Wojciechowski A.(Ed.), Lipiński P.(Ed.)., Progress in Polish Artificial …, 2023 | 1 | 2023 |
| Rewolucja stanu–fantastyczne wprowadzenie do informatyki kwantowej P Gawron, M Cholewa, K Kara Instytut Informatyki Teoretycznej i Stosowanej Polskiej Akademii Nauk, 2016 | 1 | 2016 |
| Approximation of values of prolate spheroidal wave function M Cholewa Theoretical and Applied Informatics 24 (1), 67-94, 2012 | 1 | 2012 |
