Traffic Signs Color Detection and Segmentation in Poor Light Conditions
Document identifier: oai:dalea.du.se:1025
Keyword: Engineering and Technology,
Civil Engineering,
Transport Systems and Logistics,
Teknik och teknologier,
Samhällsbyggnadsteknik,
Transportteknik och logistik,
Color segmentation,
Color constancy,
Histogram equalization,
Color detection,
Road signs,
Outdoor images.Publication year: 2005Abstract: This paper presents a new algorithm for color detection and segmentation of road signs in poor light conditions. The images were taken by a digital camera mounted in a car. The RGB channels of the digital images were enhanced separately by histogram equalization, and then a color constancy algorithm was applied to extract the true colors of the sign. The resultant image was then converted into HSV color space, and segmented to extract the colors of the road signs. The method was tested on outdoor images in different poor light conditions such as fog and snow, and they show high robustness. This project is part of the research taking place at Dalarna University - Sweden in the field of the Intelligent Transport Systems (ITS).
Authors
Hasan Fleyeh
Högskolan Dalarna; Datateknik
Other publications
>>
Documents attached
|
Click on thumbnail to read
|
Record metadata
Click to view metadata
header:
identifier: oai:dalea.du.se:1025
datestamp: 2021-04-15T14:04:07Z
setSpec: SwePub-du
metadata:
mods:
@attributes:
version: 3.7
recordInfo:
recordContentSource: du
recordCreationDate: 2005-05-02
identifier: http://urn.kb.se/resolve?urn=urn:nbn:se:du-1025
titleInfo:
@attributes:
lang: eng
title: Traffic Signs Color Detection and Segmentation in Poor Light Conditions
abstract: This paper presents a new algorithm for color detection and segmentation of road signs in poor light conditions. The images were taken by a digital camera mounted in a car. The RGB channels of the digital images were enhanced separately by histogram equalization and then a color constancy algorithm was applied to extract the true colors of the sign. The resultant image was then converted into HSV color space and segmented to extract the colors of the road signs. The method was tested on outdoor images in different poor light conditions such as fog and snow and they show high robustness. This project is part of the research taking place at Dalarna University - Sweden in the field of the Intelligent Transport Systems (ITS).
subject:
@attributes:
lang: eng
authority: uka.se
topic:
Engineering and Technology
Civil Engineering
Transport Systems and Logistics
@attributes:
lang: swe
authority: uka.se
topic:
Teknik och teknologier
Samhällsbyggnadsteknik
Transportteknik och logistik
@attributes:
lang: eng
topic: Color segmentation
@attributes:
lang: eng
topic: color constancy
@attributes:
lang: eng
topic: histogram equalization
@attributes:
lang: eng
topic: color detection
@attributes:
lang: eng
topic: road signs
@attributes:
lang: eng
topic: outdoor images.
language:
languageTerm: eng
genre:
conference/other
ref
note:
Published
1
name:
@attributes:
type: personal
authority: du
namePart:
Fleyeh
Hasan
role:
roleTerm: aut
affiliation:
Högskolan Dalarna
Datateknik
nameIdentifier:
hfl
0000-0002-1429-2345
originInfo:
dateIssued: 2005
place:
placeTerm: Tsukuba Science City
relatedItem:
@attributes:
type: host
titleInfo:
title: Machine Vision Applications (MVA2005)
location:
url: http://du.diva-portal.org/smash/get/diva2:521402/FULLTEXT01.pdf
accessCondition: gratis
physicalDescription:
form: electronic
typeOfResource: text