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

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.

Masarczyk W, Głomb P, Grabowski B, Ostaszewski M.  2019.  Effective transfer learning for hyperspectral image classification with deep convolutional neural networks.

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

Grabowski B, Masarczyk W, Głomb P, Mendys A.  2018.  Automatic pigment identification from hyperspectral data. Journal of Cultural Heritage. 31:1-12.

Głomb P, Domino K, Romaszewski M, Cholewa M.  2018.  Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images. arXiv:1808.03513v1.

Cholewa M, Gawron P, Głomb P, Kurzyk D.  2017.  Quantum hidden Markov models based on transition operation matrices. Quantum Information Processing. 16:101.

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.

Głomb P, Cholewa M.  2016.  Performance of Interest Point Descriptors on Hyperspectral Images. Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP.

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.

Cholewa M, Głomb P.  2016.  Two Stage SVM Classification for Hyperspectral Data. Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM.

Domino K, Głomb P, Łaskarzewski Z..  2015.  Classification of LPG clients using the Hurst exponent and the correlation coeficient. Theoretical and Applied Informatics. 27(1):13–24.

Głomb P, Cholewa M.  2015.  Experimental Evaluation of Selected Approaches to Covariance Matrix Regularization. Artificial Intelligence and Soft Computing. 9120:391-401.

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.

Głomb P, Sochan A.  2014.  Surface Mixture Models for the Optimization of Object Boundary Representation. Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science 8467. :703–714.

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.

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.

Blachnik M., Głomb P.  2012.  Do we need complex models for gestures? A comparison of data representation and preprocessing methods for hand gesture recognition Artificial Intelligence and Soft Computing Lecture Notes in Computer Science 7267. :477–485.

Cholewa M, Głomb P.  2012.  Estimation of the number of states for gesture recognition with Hidden Markov Models based on the number of critical points in time sequence. Pattern Recognition Letters.

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.