Przemysław Głomb, PhD

Email: 

Orcid ID: 

0000-0002-0215-4674

Position: 

Institute Professor
Research groups

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Publications
Biblio RSS

2022

26. Książek, K., P. Głomb, M. Romaszewski, M. Cholewa, B. Grabowski, and K. Buza, "Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation", 21st International Conference on Image Analysis and Processing, vol. 13231, Lecce, Italy, Springer, Cham, 05/2022.

2021

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

2020

28. Romaszewski, M., P. Głomb, A. Sochan, and M. Cholewa, A Dataset for Evaluating Blood Detection in Hyperspectral Images [Data set], 2020.
29. Książek, K., M. Romaszewski, P. Głomb, B. Grabowski, and M. Cholewa, "Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks", Sensors, vol. 20, issue Recent Advances in Multi- and Hyperspectral Image Analysis, 11/2020.
30. Głomb, P., and M. Romaszewski, "Anomaly detection in hyperspectral remote sensing images", Hyperspectral Remote Sensing: Theory & Applications: Elsevier, 2020.
31. Masarczyk, W., P. Głomb, B. Grabowski, and M. Ostaszewski, "Effective Training of Deep Convolutional Neural Networks for Hyperspectral Image Classification through Artificial Labeling", Remote Sensing, vol. 12, issue 16, 2020.

2019

32. Grabowski, B., P. Głomb, M. Romaszewski, and M. Ostaszewski, "Unsupervised deep learning approach to hyperspectral anomaly detection", PP-RAI'2019, Wrocław, Poland, Wroclaw University of Science and Technology, 2019.
33. Cholewa, M., P. Głomb, and M. Romaszewski, "A Spatial-Spectral Disagreement-Based Sample Selection With an Application to Hyperspectral Data Classification", IEEE Geoscience and Remote Sensing Letters, vol. 16, pp. 467-471, March, 2019.
34. Głomb, P., K. Domino, M. Romaszewski, and M. Cholewa, "Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images", PP-RAI'2019, Wrocław, Poland, Wroclaw University of Science and Technology, pp. p. 121, 2019.

2018

35. Grabowski, B., W. Masarczyk, P. Głomb, and A. Mendys, "Automatic pigment identification from hyperspectral data", Journal of Cultural Heritage, vol. 31, pp. 1 - 12, 2018.
36. Romaszewski, M., P. Głomb, and M. Cholewa, "Adaptive, Hubness-Aware Nearest Neighbour Classifier with Application to Hyperspectral Data", Computer and Information Sciences: Springer International Publishing, 2018.
37. Głomb, P., M. Romaszewski, M. Cholewa, and K. Domino, "Application of hyperspectral imaging and Machine Learning methods for the detection of gunshot residue patterns", Forensic Science International, vol. 290, 07/2018.

2017

38. Cholewa, M., P. Gawron, P. Głomb, and D. Kurzyk, "Quantum hidden Markov models based on transition operation matrices", Quantum Information Processing, vol. 16, pp. 101, 2017.

2016

39. Głomb, P., and M. Cholewa, "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, 2016.
40. Cholewa, M., and P. Głomb, "Two Stage SVM Classification for Hyperspectral Data", Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, 2016.
41. Romaszewski, M., P. Głomb, and M. Cholewa, "Semi-supervised hyperspectral classification from a small number of training samples using a co-training approach", ISPRS Journal of Photogrammetry and Remote Sensing, vol. 121, pp. 60 - 76, 2016.
42. Romaszewski, M., and P. Głomb, "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, 2016.

2015

43. Głomb, P., and M. Cholewa, "Experimental Evaluation of Selected Approaches to Covariance Matrix Regularization", Artificial Intelligence and Soft Computing, vol. 9120: Springer International Publishing, pp. 391-401, 2015.
44. Domino, K., P. Głomb, and Z. Łaskarzewski, "Classification of LPG clients using the Hurst exponent and the correlation coeficient", Theoretical and Applied Informatics, vol. 27, issue 1, pp. 13–24, 2015.

2014

45. Głomb, P., and A. Sochan, "Surface Mixture Models for the Optimization of Object Boundary Representation", Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science 8467, pp. 703–714, 2014.
46. Romaszewski, M., P. Głomb, and P. Gawron, "Natural hand gestures for human identification in a Human-Computer Interface", Image Processing Theory Tools and Applications (IPTA), 2014 4th International Conference on: IEEE, pp. 404–409, 10, 2014.

2013

47. Cholewa, M., P. Głomb, S. Opozda, M. Romaszewski, A. Sochan, M. Kosiedowski, E. Kuśmierek, A. Bęben, P. Krawiec, and A. Stroiński, "Aplikacje Sieci Świadomej Treści", Inżynieria Internetu przyszłości część 2: Oficyna Wydawnicza PW, 2013.

2012

48. Cholewa, M., and P. Głomb, "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, 2012.
49. Blachnik, M., and P. Głomb, "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: Springer Berlin Heidelberg, pp. 477–485, 2012.
50. Głomb, P., M. Romaszewski, S. Opozda, and A. Sochan, "Choosing and Modeling the Hand Gesture Database for a Natural User Interface", Gesture and Sign Language in Human-Computer Interaction and Embodied Communication, Lecture Notes in Artificial Intelligence: Springer Berlin Heidelberg, 2012.

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Data aktualizacji: 10/06/2022 - 10:42; autor zmian: Łukasz Zimny (lzimny@iitis.pl)