Publikacje
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Filters: Autor is Przemysław Głomb [Clear All Filters]
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2019. A Spatial-Spectral Disagreement-Based Sample Selection With an Application to Hyperspectral Data Classification. IEEE Geoscience and Remote Sensing Letters. 16:467-471.
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2018. Adaptive, Hubness-Aware Nearest Neighbour Classifier with Application to Hyperspectral Data. Computer and Information Sciences.
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2018. Application of hyperspectral imaging and Machine Learning methods for the detection of gunshot residue patterns. Forensic Science International. 290
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2018. Automatic pigment identification from hyperspectral data. Journal of Cultural Heritage. 31:1-12.
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2018. Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images. arXiv:1808.03513v1.
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2017. Quantum hidden Markov models based on transition operation matrices. Quantum Information Processing. 16:101.
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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.
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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.
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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.
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2016. Two Stage SVM Classification for Hyperspectral Data. Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM.
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2015. Classification of LPG clients using the Hurst exponent and the correlation coeficient. Theoretical and Applied Informatics. 27(1):13–24.
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2015. Experimental Evaluation of Selected Approaches to Covariance Matrix Regularization. Artificial Intelligence and Soft Computing. 9120:391-401.
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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.
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2014. Surface Mixture Models for the Optimization of Object Boundary Representation. Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science 8467. :703–714.
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2013. Aplikacje Sieci Świadomej Treści. Inżynieria Internetu przyszłości część 2.
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2012. Aplikacje sieci świadomej treści. Przegląd telekomunikacyjny. :1491–1502.
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2012. Choosing and Modeling the Hand Gesture Database for a Natural User Interface. Gesture and Sign Language in Human-Computer Interaction and Embodied Communication.
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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.
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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.
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2011. Choosing and modeling hand gesture database for natural user interface. GW 2011: the 9th International Gesture in Embodied Communication and Human-Computer InteractionWorkshop.
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2011. The Effect of Multiple Training Sequences on HMM Classification of Motion Capture Gesture Data. Computer Recognition Systems. 4:365–373.
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2011. Eigengestures for natural human computer interface. Man-Machine Interactions 2. :49–56.
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2011. Gesture data modeling and classification based on critical points approximation. Advances in Intelligent and Soft Computing, 2011, Volume 95, Computer Recognition Systems 4,. 95:365–373.
