Książek K, Głomb P, Romaszewski M, Cholewa M, Grabowski B.  Submitted.  Stable training of autoencoders for hyperspectral unmixing. arXiv preprint 2109.13748.

Romaszewski M, Głomb P, Sochan A, Cholewa M.  2021.  A dataset for evaluating blood detection in hyperspectral images. Forensic Science International. 320

Pałka F, Książek W, Pławiak P, Romaszewski M, Książek K.  2021.  Hyperspectral Classification of Blood-Like Substances Using Machine Learning Methods Combined with Genetic Algorithms in Transductive and Inductive Scenarios. Sensors. 21

Głomb P, Romaszewski M.  2020.  Anomaly detection in hyperspectral remote sensing images. Hyperspectral Remote Sensing: Theory & Applications.

Książek K, Romaszewski M, Głomb P, Grabowski B, Cholewa M.  2020.  Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks. Sensors. 20(Recent Advances in Multi- and Hyperspectral Image Analysis)

Domino M, Romaszewski M, Jasiński T, Maśko M.  2020.  Comparison of surface thermal patterns of horses and donkeys in IRT images [Data set].

Domino M, Romaszewski M, Jasiński T, Maśko M.  2020.  Comparison of the Surface Thermal Patterns of Horses and Donkeys in Infrared Thermography Images. Animals. 10(12):2201.

Romaszewski M, Głomb P, Sochan A, Cholewa M.  2020.  A Dataset for Evaluating Blood Detection in Hyperspectral Images [Data set].

Głomb P, Domino K, Romaszewski M, Cholewa M.  2019.  Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images. PP-RAI'2019. :p.121.

Cholewa M, Głomb P, Romaszewski M.  2019.  A Spatial-Spectral Disagreement-Based Sample Selection With an Application to Hyperspectral Data Classification. IEEE Geoscience and Remote Sensing Letters. 16:467-471.

Grabowski B, Głomb P, Romaszewski M, Ostaszewski M.  2019.  Unsupervised deep learning approach to hyperspectral anomaly detection. PP-RAI'2019.

Romaszewski M, Głomb P, Cholewa M.  2018.  Adaptive, Hubness-Aware Nearest Neighbour Classifier with Application to Hyperspectral Data. Computer and Information Sciences.

Głomb P, Romaszewski M, Cholewa M, Domino K.  2018.  Application of hyperspectral imaging and Machine Learning methods for the detection of gunshot residue patterns. Forensic Science International. 290

Romaszewski M, Sochan A, Skabek K.  2018.  Matrix and Tensor-Based Approximation of 3D Face Animations from Low-Cost Range Sensors. Computer and Information Sciences.

Romaszewski M, Głomb P.  2016.  Parameter Estimation for HOSVD-based Approximation of Temporally Coherent Mesh Sequences. Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications.

Romaszewski M, Głomb P, Cholewa M.  2016.  Semi-supervised hyperspectral classification from a small number of training samples using a co-training approach. ISPRS Journal of Photogrammetry and Remote Sensing. 121:60-76.

Romaszewski M, Gawron P, Opozda S.  2014.  Dimensionality reduction of dynamic animations using HO-SVD. Lecture Notes in Artificial Intelligence. 8467:757–768.

Romaszewski M, Głomb P, Gawron P.  2014.  Natural hand gestures for human identification in a Human-Computer Interface. Image Processing Theory Tools and Applications (IPTA), 2014 4th International Conference on. :404–409.

Cholewa M, Głomb P, Opozda S, Romaszewski M, Sochan A, Kosiedowski M., Kuśmierek E., Bęben A., Krawiec P., Stroiński A..  2013.  Aplikacje Sieci Świadomej Treści. Inżynieria Internetu przyszłości część 2.

Romaszewski M, Gawron P, Opozda S.  2013.  Compression of animated 3D models using HO-SVD. arXiv:1310.1240.

Romaszewski M, Gawron P, Opozda S.  2013.  Dimensionality Reduction of Dynamic Mesh Animations Using HO-SVD. Journal of Artificial Intelligence and Soft Computing Research. 3:277–289.

Kosiedowski M., Kuśmierek E., Stroiński A., Cholewa M, Głomb P, Opozda S, Romaszewski M, Sochan A, Bęben A., Krawiec P..  2012.  Aplikacje sieci świadomej treści. Przegląd telekomunikacyjny. :1491–1502.

Głomb P, Romaszewski M, Opozda S, Sochan A.  2012.  Choosing and Modeling the Hand Gesture Database for a Natural User Interface. Gesture and Sign Language in Human-Computer Interaction and Embodied Communication.

Głomb P, Romaszewski M, Opozda S, Sochan A.  2011.  Choosing and modeling hand gesture database for natural user interface. GW 2011: the 9th International Gesture in Embodied Communication and Human-Computer InteractionWorkshop.

Romaszewski M, Głomb P.  2011.  The Effect of Multiple Training Sequences on HMM Classification of Motion Capture Gesture Data. Computer Recognition Systems. 4:365–373.