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Michał Cholewa
Michał Cholewa
Institute of Theoretical and Applied Informatics
Zweryfikowany adres z iitis.pl
Tytuł
Cytowane przez
Cytowane przez
Rok
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
562016
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
342018
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
312013
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
242020
A dataset for evaluating blood detection in hyperspectral images
M Romaszewski, P Głomb, A Sochan, M Cholewa
Forensic science international 320, 110701, 2021
202021
Quantum hidden Markov models based on transition operation matrices
M Cholewa, P Gawron, P Głomb, D Kurzyk
Quantum Information Processing 16, 1-19, 2017
182017
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
112019
Natural human gestures classification using multisensor data
M Cholewa, P Głomb
2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), 499-503, 2015
52015
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
42022
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
42018
Classification of dynamic sequences of 3d point clouds
M Cholewa, P Sporysz
International Conference on Artificial Intelligence and Soft Computing, 672-683, 2014
42014
Gesture data modeling and classification based on critical points approximation
M Cholewa, P Głomb
Computer Recognition Systems 4, 307-315, 2011
42011
Two stage SVM classification for hyperspectral data
M Cholewa, P Glomb
International Conference on Pattern Recognition Applications and Methods 2 …, 2016
32016
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
22023
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
22018
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
22011
Rewolucja stanu–fantastyczne wprowadzenie do informatyki kwantowej
P Gawron, M Cholewa, K Kara
Instytut Informatyki Teoretycznej i Stosowanej Polskiej Akademii Nauk, 2016
12016
Approximation of values of prolate spheroidal wave function
M Cholewa
Theoretical and Applied Informatics 24 (1), 67-94, 2012
12012
Continual learning of a time series model using a mixture of HMMs with application to the IoT fuel sensor verification
P Głomb, M Cholewa, P Foszner, J Bularz
2023 18th Conference on Computer Science and Intelligence Systems (FedCSIS …, 2023
2023
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
2023
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