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2018 | 113 | 37-43
Article title

A Vehicle Recognition New Approach with the Application of Graph Network Theory

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EN
Abstracts
EN
A vehicle recognition approach based on graph theory and neural networks is proposed in this paper. In this approach, image threshold method described in this paper based on spectral theory is used for image pre-processing. And after filter of undetermined regions with rules, regions left are unified. These values are input into neural network to recognize vehicle and vehicle types. The experiment proves that this method has high recognition rate and low false rate.
Year
Volume
113
Pages
37-43
Physical description
Contributors
author
  • Krishna Institute of Engineering and Technology, Ghaziabad, India
References
  • [1] Xiaoying Jing, Curt H. Davis. Vector-Guided Vehicle Detection from High-Resolution Satellite Imagery. IEEE 2014, 1095-1098.
  • [2] Zu Whan Kim, Jitendra Malik. Fast Vehicle Detection with Probabilistic Feature Grouping and its Application to Vehicle Tracking. Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV’13).
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  • [5] Tao Wen-Bing, JIN Hai. A New Image Thresholding Method Based on Graph Spectral Theory. Chinese Journal of Computer, Jan. 2017, 110-118.
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  • [7] Peretto, P., An Introduction to the Modeling of Neural Networks, London: Cambridge University Press, 1992.
  • [8] Dahl, E. D., Neural network algorithm for an NP-complete problem: map and graph coloring, Proc. IEEE First International Conference on Neural Networks, Vol. III, June, San Diego, New York: IEEE, 1987, 113–120.
  • [9] Gendreau, M., Picard, J. C., Zubieta, L., An efficient implicit enumeration algorithm for the maximum clique problem, Lecture Notes in Economics and Mathematical Systems (eds. Kurzhanski, A. et. al.), New York: Springer-Verlag, 1988, 304: 79–91.
  • [10] Zhang, Y. J., Ye, Z. X., Neural Networks with Applications—94' New Advance (in Chinese), Wuhan: The Huazhong University of Science and Technological Press, 1994
Document Type
article
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.psjd-2558e6be-85a7-4cc3-82c1-edcb194935b7
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