Advanced search
1 file | 1.36 MB Add to list

Feature extraction for license plate location based on L0-norm smoothing

(2019) OPEN COMPUTER SCIENCE. 9(1). p.128-135
Author
Organization
Abstract
We propose a simple feature extraction algorithm for license plate location, which can reduce the occurrence of pseudo-licenses significantly. Our scheme arises from a novel L-0 -norm image smoothing, in which the multiple local textures in the complex backgrounds can be suppressed remarkably without changing the structures and edges of the license objects. Due to this "edgeaware" property, we then combine a feature filtering with an efficient binarized image, a simple multi-scale image analysis algorithm, to remove the potential false license plates. Finally, we extract license plates with a projection method. Experimental results show the proposed method provides a flexible and powerful way to the license plate location in complex backgrounds.
Keywords
license plate location, L-0-norm minimization, feature filtering, binarized image, WAVELET TRANSFORM, RECOGNITION

Downloads

  • Open Computer Science Feature extraction for license plate location based on L0-norm smoothing.pdf
    • full text (Published version)
    • |
    • open access
    • |
    • PDF
    • |
    • 1.36 MB

Citation

Please use this url to cite or link to this publication:

MLA
Huang, Junqing, et al. “Feature Extraction for License Plate Location Based on L0-Norm Smoothing.” OPEN COMPUTER SCIENCE, vol. 9, no. 1, 2019, pp. 128–35, doi:10.1515/comp-2019-0007.
APA
Huang, J., Ruzhansky, M., Feng, H., Zheng, L., Huang, X., & Wang, H. (2019). Feature extraction for license plate location based on L0-norm smoothing. OPEN COMPUTER SCIENCE, 9(1), 128–135. https://doi.org/10.1515/comp-2019-0007
Chicago author-date
Huang, Junqing, Michael Ruzhansky, Haoxiang Feng, Lingfang Zheng, Xin Huang, and Haihui Wang. 2019. “Feature Extraction for License Plate Location Based on L0-Norm Smoothing.” OPEN COMPUTER SCIENCE 9 (1): 128–35. https://doi.org/10.1515/comp-2019-0007.
Chicago author-date (all authors)
Huang, Junqing, Michael Ruzhansky, Haoxiang Feng, Lingfang Zheng, Xin Huang, and Haihui Wang. 2019. “Feature Extraction for License Plate Location Based on L0-Norm Smoothing.” OPEN COMPUTER SCIENCE 9 (1): 128–135. doi:10.1515/comp-2019-0007.
Vancouver
1.
Huang J, Ruzhansky M, Feng H, Zheng L, Huang X, Wang H. Feature extraction for license plate location based on L0-norm smoothing. OPEN COMPUTER SCIENCE. 2019;9(1):128–35.
IEEE
[1]
J. Huang, M. Ruzhansky, H. Feng, L. Zheng, X. Huang, and H. Wang, “Feature extraction for license plate location based on L0-norm smoothing,” OPEN COMPUTER SCIENCE, vol. 9, no. 1, pp. 128–135, 2019.
@article{8636223,
  abstract     = {We propose a simple feature extraction algorithm for license plate location, which can reduce the occurrence of pseudo-licenses significantly. Our scheme arises from a novel L-0 -norm image smoothing, in which the multiple local textures in the complex backgrounds can be suppressed remarkably without changing the structures and edges of the license objects. Due to this "edgeaware" property, we then combine a feature filtering with an efficient binarized image, a simple multi-scale image analysis algorithm, to remove the potential false license plates. Finally, we extract license plates with a projection method. Experimental results show the proposed method provides a flexible and powerful way to the license plate location in complex backgrounds.},
  author       = {Huang, Junqing and Ruzhansky, Michael and Feng, Haoxiang and Zheng, Lingfang and Huang, Xin and Wang, Haihui},
  issn         = {2299-1093},
  journal      = {OPEN COMPUTER SCIENCE},
  keywords     = {license plate location,L-0-norm minimization,feature filtering,binarized image,WAVELET TRANSFORM,RECOGNITION},
  language     = {eng},
  number       = {1},
  pages        = {128--135},
  title        = {Feature extraction for license plate location based on L0-norm smoothing},
  url          = {http://dx.doi.org/10.1515/comp-2019-0007},
  volume       = {9},
  year         = {2019},
}

Altmetric
View in Altmetric