0562370 - ÚI 2023 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
Kalina, Jan - Vidnerová, Petra - Janáček, Patrik
Sparse Versions of Optimized Centroids.
2022 International Joint Conference on Neural Networks (IJCNN) Proceedings. Piscataway: IEEE, 2022, s. 1-7. ISBN 978-1-7281-8671-9.
[IJCNN 2022: International Joint Conference on Neural Networks /35./. Padua (IT), 18.07.2022-23.07.2022]
Grant CEP: GA ČR(CZ) GA22-02067S
Institucionální podpora: RVO:67985807
Klíčová slova: image processing * templates * sparsity * variable selection * robustness * computational efficiency
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Centroid-based methods have an established place in a variety of tasks including object localization in images. A sophisticated method for constructing optimal centroids and corresponding weights has been proposed only recently. In order to reduce the computational demands of applying the optimal centroid, several novel sparse versions of the optimal centroids are proposed here, which are based on trimming away some of their pixels. Suitable novel sparse versions bring improvements compared to available optimal centroids. At the same time, some of the sparse optimal centroids (especially the method with thresholded optimal weights) turn out to be robust to noise in the images.
Trvalý link: https://hdl.handle.net/11104/0334709