Orthogonality is superiority in piecewise-polynomial signal segmentation and denoising

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F19%3A00500658" target="_blank" >RIV/67985556:_____/19:00500658 - isvavai.cz</a>

Výsledek na webu

<a href="https://asp-eurasipjournals.springeropen.com/articles/10.1186/s13634-018-0598-9" target="_blank" >https://asp-eurasipjournals.springeropen.com/articles/10.1186/s13634-018-0598-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s13634-018-0598-9" target="_blank" >10.1186/s13634-018-0598-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Orthogonality is superiority in piecewise-polynomial signal segmentation and denoising

Popis výsledku v původním jazyce

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!

Název v anglickém jazyce

Orthogonality is superiority in piecewise-polynomial signal segmentation and denoising

Popis výsledku anglicky

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!

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20206 - Computer hardware and architecture

Projekt

<a href="/cs/project/GA16-13830S" target="_blank" >GA16-13830S: Perfuzní zobrazování v magnetické rezonanci pomocí komprimovaného snímání</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

EURASIP Journal on Advances in Signal Processing

ISSN

1687-6180

e-ISSN

—

Svazek periodika

2019

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

6

Kód UT WoS článku

000456723400001

EID výsledku v databázi Scopus

2-s2.0-85060729194