0500658 20240103221447.020190129235959.9 85060729194 000456723400001 10.1186/s13634-018-0598-9 15 s. E cav_un_epca*03085981687-61802019Springer Signal segmentation Signal smoothing Signal approximation cav_un_auth*0371530 Novosadová M. CZ cav_un_auth*0298515 Rajmic P. CZ cav_un_auth*0108377 Šorel Michal Zpracování obrazové informace Department of Image Processing ZOI ZOI UTIA-B Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2019/ZOI/sorel-0500658.pdf https://asp-eurasipjournals.springeropen.com/articles/10.1186/s13634-018-0598-9 cav_un_auth*0338628 GA16-13830S GA ČR CZ Segmentation and denoising of signals often rely on the polynomial model which assumes that every segment is a polynomial of a certain degree and that the segments are modeled independently of each other. Segment borders (breakpoints) correspond to positions in the signal where the model changes its polynomial representation. Several signal denoising methods successfully combine the polynomial assumption with sparsity. In this work, we follow on this and show that using orthogonal polynomials instead of other systems in the model is beneficial when segmenting signals corrupted by noise. The switch to orthogonal bases brings better resolving of the breakpoints, removes the need for including additional parameters and their tuning, and brings numerical stability. Last but not the least, it comes for free! WOS JD 20000 20200 20206 2020 3 4 A hod 4ah 20231122143800.0 RVO:67985556 http://hdl.handle.net/11104/0293326 1 cav_un_auth*0314450 Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií 1 Department of Image Processing UTIA-B 10200 COMPUTER SCIENCE, THEORY & METHODS S 6 2 Article Astronomy Astrophysics A ENGINEERING.ELECTRICAL&ELECTRONIC 1.406 0.419 9.4 0.00275 0.384 3297 101 0.383 37.77 1.57 558 Q2 1.067 62 Q4 27.424 20.9 Q3 Q4 0.4 Q3 20.865 2019 Soubory v repozitáři: sorel-0500658.pdf 85060729194 SCOPUS PUBMED 000456723400001 WOS cav_un_epca*0308598 EURASIP Journal on Advances in Signal Processing 1687-6180 1687-6180 Roč. 2019 č. 1 2019 Springer