@inproceedings{23b242f99f054a78a7a997327a1565ba,
title = "Ford motor side-view recognition system based on wavelet entropy and back propagation neural network and levenberg-marquardt algorithm",
abstract = "(Aim) Automatic identification of the car manufacturer in the side-view position can be used for the intelligent traffic monitoring system. Currently, the side-view car recognition did not attract too much attention. (Method) We proposed a novel Ford Motor recognition system. We first captured the car image from the side view. Second, we used wavelet entropy to extract texture features. Third, we employed a back propagation neural network (BPNN) as the classifier. Finally, we employed the Levenberg-Marquardt algorithm to train the classifier. In the experiment, we utilized the 3 × 3-fold cross validation. (Result) This method achieved an overall accuracy of 80% in detecting Ford motors. (Conclusion) This method can detect Ford Motors from the side view effectively. In the future, it may also be used to detect cars of other brands.",
keywords = "Back propagation neural network, Cross validation, Ford motor, Levenberg-Marquardt algorithm, Pattern recognition, Recognition, Wavelet entropy",
author = "Jia, {Wen Juan} and Shuihua Wang and Huimin Lu and Ying Shao and Elizabeth Lee and Zhang, {Yu Dong}",
note = "Publisher Copyright: {\textcopyright} 2017, Springer Nature Singapore Pte Ltd.; 8th International Symposium on Parallel Architectures, Algorithms, and Programming, PAAP 2017 ; Conference date: 17-06-2017 Through 18-06-2017",
year = "2017",
doi = "10.1007/978-981-10-6442-5_1",
language = "English",
isbn = "9789811064418",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "3--12",
editor = "Hong Shen and Guoliang Chen and Mingrui Chen",
booktitle = "Parallel Architecture, Algorithm and Programming - 8th International Symposium, PAAP 2017, Proceedings",
}