relation: https://khub.utp.edu.my/scholars/9666/
title: Study of Various Image De-Noising Methods Used for the Purpose of Traffic Sign Board Recognition in an Intelligent Advanced Driver Assistance System
creator: Deshpande, A.V.
creator: Kannan, R.
creator: Subashini, M.M.
description: As far as the safety of a driver is concerned, more focus should be put on correct interpretation and information which is conveyed by a traffic sign, while driving a vehicle along the road. A sign board can be thought of as an emblem which disseminates important and meaningful information regarding the potential hazards prevailing among road users comprising roadways cladded with snowfall, construction worksites or repairing of roads taking place and telling the people to follow an alternative route. It alerts the person who is passing through the road about the maximum possible extremity that his vehicle is trying to achieve indicating; slowing down the speed of vehicle since chances of having collision cannot be ruled out. With constant increasing of the training database size, not only the recognition accuracy, but also the computation complexity should be considered in designing a feasible recognition approach. The traffic sign images were acquired from the image database and were subjected to some pre-processing techniques such as removing the noise present in a particular image with the help of Arithmetic Mean Filter as well as Geometric Mean Filter. In the future, we will concentrate on detecting, recognizing as well as classifying a particular sign board. © 2018 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2018
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Deshpande, A.V. and Kannan, R. and Subashini, M.M.  (2018) Study of Various Image De-Noising Methods Used for the Purpose of Traffic Sign Board Recognition in an Intelligent Advanced Driver Assistance System.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059751305&doi=10.1109%2fICIAS.2018.8540630&partnerID=40&md5=7bfd2c08bd22f087c7c9ecd9e2f467ea
relation: 10.1109/ICIAS.2018.8540630
identifier: 10.1109/ICIAS.2018.8540630