Journal of Transportation Technologies

Journal of Transportation Technologies

ISSN Print: 2160-0473
ISSN Online: 2160-0481
www.scirp.org/journal/jtts
E-mail: jtts@scirp.org
"FPGA-Based Traffic Sign Recognition for Advanced Driver Assistance Systems"
written by Sheldon Waite, Erdal Oruklu,
published by Journal of Transportation Technologies, Vol.3 No.1, 2013
has been cited by the following article(s):
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[1] Embedded Real-Time System for Traffic Sign Recognition on ARM Processor
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[2] Towards Next-Generation Vehicles Featuring the Vehicle Intelligence
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[3] ارائه یک الگوریتم پردازش تصویر هوشمند جدید برای تشخیص و شناسایی علائم راهنمایی و رانندگی مبتنی بر منطق فازی‎
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[6] Designing a New Intelligent Image Processing Algorithm for Traffic Sign Detection and Recognition Based on Fuzzy Logic
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[7] Computer vision for driving support systems: automatic traffic signs detection and proximity analysis
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[8] Computer Vision System-On-Chip Designs for Intelligent Vehicles
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[9] Resource Efficient Hardware Implementation for Real-Time Traffic Sign Recognition
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[10] Road Traffic Sign Identification in Weak Illumination for Intelligent Vehicle Based on Machine Vision
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[12] Real-Time Traffic Sign Classification Using Combined Convolutional Neural Networks
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[13] Virtual Driver-Sistema de Reconhecimento de Sinalização Vertical de Trânsito
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[14] Detection and recognition of traffic signs based on RGB to red conversion
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[15] FPGA based real-time implementation of driver assistance system
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[16] An Overview of Traffic Signs Recognition Methods
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[17] A Real Time Traffic Sign Detection and Recognition Algorithm based on Super Fuzzy Set
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[18] Real-Time Embedded Implementation of Robust Speed-Limit Sign Recognition Using a Novel Centroid-to-Contour Description Method
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[19] Real-time embedded system for traffic sign recognition based on ZedBoard
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[20] Evaluation of Traffic Control Systems as ITS Infrastructure for Automated Driving
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[21] Towards the development of flexible, reliable, reconfigurable, and high-performance imaging systems
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[22] Low-complexity hardware architecture of traffic sign recognition with IHSL color space for advanced driver assistance systems
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[23] Embedded system design for traffic sign recognition using machine learning algorithms
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[24] Hardware implementation of fast traffic sign recognition for intelligent vehicle system
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[25] Real-time hardware/software co-design of a traffic sign recognition system using Zynq FPGA
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[26] Hardware implementation and validation of a traffic road sign detection and identification system
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[27] Reconfigurable SRTM System for Road Traffic in Kingdom of Bahrain
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[28] TRAFFIC SIGN DETECTION USING PCA AND ANN
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[29] Real-time Speed Limit Traffic Sign Detection System for Robust Automotive Environments
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[30] High Accuracy and Simple Real-Time Circle Detection on Low-Cost FPGA for Traffic-Sign Recognition on Advanced Driver Assistance System
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[31] Robust traffic sign recognition with feature extraction and k-NN classification methods
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[32] GCC-Plugin for Automated Accelerator Generation and Integration on Hybrid FPGA-SoCs
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[33] Traffic sign recognition based on prevailing bag of visual words representation on feature descriptors
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[34] FPGA based traffic sign detection for automotive camera systems
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[35] Hardware/Software Co-Design of a Traffic Sign Recognition System Using Zynq FPGAs
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[36] Embedded system for road sign detection using MicroBlaze
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[37] 基於田口法與 FPGA 平行運算辨識物件顏色之影像處理
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[38] Novel approach for real time detection and classification based on template matching in video
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[39] New image processing algorithms for traffic signs recognition
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[40] IPPro: FPGA based image processing processor
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[41] Real time road lane markers detection for intelligent vehicles
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[42] Real-time traffic sign recognition based on Zynq FPGA and ARM SoCs
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[43] Morphological Classification for Traffic Sign Recognition
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[44] OpenCV based road sign recognition on Zynq
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