Effectiveness of Approaches for Rail Candidates Detection and Verification of the SVM

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F16%3APU121636" target="_blank" >RIV/00216305:26230/16:PU121636 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.ictic.sk/archive/?vid=1&aid=2&kid=50501-285" target="_blank" >http://www.ictic.sk/archive/?vid=1&aid=2&kid=50501-285</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18638/ictic.2016.5.1" target="_blank" >10.18638/ictic.2016.5.1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Effectiveness of Approaches for Rail Candidates Detection and Verification of the SVM

Popis výsledku v původním jazyce

Rail candidates detection is the primary task in railway recognition systems based on recognition in images taken from the camera mounted on the board of the locomotive. In order to reduce the classifier complexity, effective and responsible rail candidates generation plays an important role without placing big decision responsibility on a further classifier stage. There are two basic options. Due to the rich complex environment along the track, pixel-per-pixel methods are often omitted. The second option involving a thorough investigation around a pixel is preferred. In this paper, we present comparison between two different approaches to rail candidates detection, each representing one of the basic groups, furthermore consequences in rail hypotheses generation. We introduce the finding that using the SVM is more efficient than the method based on pixel-per-pixel.

Název v anglickém jazyce

Effectiveness of Approaches for Rail Candidates Detection and Verification of the SVM

Popis výsledku anglicky

Rail candidates detection is the primary task in railway recognition systems based on recognition in images taken from the camera mounted on the board of the locomotive. In order to reduce the classifier complexity, effective and responsible rail candidates generation plays an important role without placing big decision responsibility on a further classifier stage. There are two basic options. Due to the rich complex environment along the track, pixel-per-pixel methods are often omitted. The second option involving a thorough investigation around a pixel is preferred. In this paper, we present comparison between two different approaches to rail candidates detection, each representing one of the basic groups, furthermore consequences in rail hypotheses generation. We introduce the finding that using the SVM is more efficient than the method based on pixel-per-pixel.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

ICTIC - Proceedings in Conference of Informatics and Management Sciences

ISBN

978-80-554-1196-5

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

152-156

Název nakladatele

University of Žilina

Místo vydání

Žilina

Místo konání akce

Žilina

Datum konání akce

21. 3. 2016

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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