DocumentCode :
1937062
Title :
Road Sign Detection and Recognition using Colour Segmentation, Shape Analysis and Template Matching
Author :
Malik, Rabia ; Khurshid, Javaid ; Ahmad, Sana Nazir
Author_Institution :
Pakistan Inst. of Eng. & Appl. Sci., Islamabad
Volume :
6
fYear :
2007
fDate :
19-22 Aug. 2007
Firstpage :
3556
Lastpage :
3560
Abstract :
The paper presents a system for detection and recognition of road signs with red boundaries and black symbols inside. The detection is invariant to varying lighting conditions and shadows. The algorithm is tested on RGB images taken from camera. These images are converted to HSV colour space. Colour segmentation for red regions is applied on the whole image. All red regions are labeled forming objects. Each of the red objects is tested for its shape. Final image contains only those red objects which are triangular or circular in shape. Finding the black regions within the accepted red objects area, results in extraction of pictogram. This pictogram is then matched with templates in database, hence recognizing the meaning of road sign. The paper presents a revised edition of fuzzy shape detector and a recognition module that uses template matching to recognize rotated and affine transformed road signs.
Keywords :
automated highways; fuzzy set theory; image colour analysis; image matching; image segmentation; object detection; visual databases; camera; colour segmentation; fuzzy shape detector; image colour analysis; image database; road sign detection; road sign recognition; shape analysis; template matching; Color; Cybernetics; Detectors; Image databases; Image segmentation; Java; Machine learning; Roads; Shape; Testing; Color segmentation; Fuzzy shape detector; Rotation Invariant; Shadow Invariant; Template matching;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
Type :
conf
DOI :
10.1109/ICMLC.2007.4370763
Filename :
4370763
Link To Document :
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