Fast Sign Recognition with Weighted Hybrid K-Nearest Neighbors Based on Holistic Features from Local Feature Descriptors-Reference-Cited by-同舟云学术

Fast Sign Recognition with Weighted Hybrid K-Nearest Neighbors Based on Holistic Features from Local Feature Descriptors

Published:2017-09 Issue:5 Volume:31 Page:04017034
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Hu Zhaozheng¹,Li Bing²,Hu Yuezhi³

Affiliation:

1. Professor, School of Electronic and Information Engineering, Hebei Univ. of Technology, Tianjin 300401, China; Professor, ITS Research Center, Wuhan Univ. of Technology, Wuhan 430063, China (corresponding author).

2. Master Student, School of Electronic and Information Engineering, Hebei Univ. of Technology, Tianjin 300401, China.

3. Master Student, ITS Research Center, Wuhan Univ. of Technology, Wuhan 430063, China.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000673

Reference28 articles.

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4. Balali V. and Golparvar-Fard M. (2014). “Video-based detection and classification of US traffic signs and mile markers using color candidate extraction and feature-based recognition.” Computing in Civil and Building Engineering International Society for Computing in Civil and Building Engineering Orlando FL 858–866.

5. Evaluation of multiclass traffic sign detection and classification methods for U.S. roadway asset inventory management;Balali V.;Comput. Civ. Eng.,2016

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