mgr inż.

Paweł Ksieniewicz


Konsultacje
C-16: P2.8 room

Pon. 15.00—17.00 schedule

Pia. 13.00—15.00 schedule


Prowadzi w tym semestrze
Cyfrowe przetw.sygn. i obrazw
Metody sztucznej inteligencji
Programowanie obiektowe
Zobacz plan zajęć link

Ta strona nie została jeszcze uzupełniona.



local_library
Zainteresowania naukowe
machine learning
hyperspectral imaging
classification
ensembles
image segmentation
representation learning
multiple classifier systems
imbalanced data
data streams
computer vision
concept drift

  • insert_drive_fileBlurred Labeling Segmentation Algorithm for Hyperspectral Images (2015)

    Ksieniewicz Paweł, Graña Manuel and Woźniak Michał in Computational Collective Intelligence

    This work is focusing on the hyperspectral imaging classification, which is nowadays a focus of intense research. The hyperspectral imaging is widely used in agriculture, mineralogy, or food processing to enumerate only a few important domains. The main problem of such image classification is access to the ground truth, because it needs the experienced experts. This work proposed a novel three-stage image segmentation method, which prepares the data for the classification and employs the active learning paradigm which reduces the expert works on image. The proposed approach was evaluated on the basis of the computer experiments carried out on the benchmark hyperspectral datasets.

    Springer International Publishing, 2015 Cytuj

  • insert_drive_fileA novel hyperspectral segmentation algorithm—concept and evaluation (2014)

    Ksieniewicz, Pawel and Jankowski, Dariusz and Ayerdi, Borja and Jackowski, Konrad and Grana, Manuel and Wozniak, Michal in Logic Journal of IGPL

    The article presents a novel Hyperspectral Segmentation Algorithm which is a part of a general framework used for image classification. The algorithm is based on an image decomposition into homogeneous regions using a novel similarity measure. Three different region representations are proposed using the matrix notation. An additional procedure merges similar regions into larger ones to reduce human expert engagement in region labelling. The algorithm has been evaluated on the number of benchmark datasets to investigate the influence of algorithm parameters on the final performance. Comparison with competing methods proved that the considered algorithm is an interesting proposition in hyperspectral image analysis tasks.

    Oxford Univ Press, 2014 Cytuj

  • insert_drive_fileHyperspectral Image Analysis Based on Color Channels and Ensemble Classifier (2014)

    Krawczyk Bartosz, Ksieniewicz Paweł and Woźniak Michał in International Conference on Hybrid Artificial Intelligence Systems

    In this paper, we introduce a novel ensemble method for classification of hyperspectral data. The pool of classifiers is built on the basis of color decomposition of the given image. Each base classifier corresponds to a single color channel that is extracted. We propose a new method for decomposing hyperspectral image into 11 different color channels. As not all of the channels may bear as useful information as other, we need to promote the most relevant ones. For this, our ensemble uses a weighted trained fuser, which uses a neural methods for establishing weights. We show, that the proposed ensemble can outperform other state-of-the-art classifiers in the given task.

    , 2014 Cytuj

  • insert_drive_fileArtificial Photoreceptors for Ensemble Classification of Hyperspectral Images (2016)

    Ksieniewicz Paweł and Woźniak Michał in Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

    This work proposes a set of functions, based on abstraction of natural photoreceptors. The proposed method was employed as the feature extraction for the classification system based on combined approach and compared with other state-of-art methods on the basis of the selected benchmark images.

    , 2016 Cytuj