full
search.png

Search

close.png

Cancel

arrow
Volume 6, Issue 2
Local Chan-Vese Model for Segmenting Nighttime Vehicle License Characters

ShangbingGao, LanfangWang

J. Info. Comput. Sci. , 6 (2011), pp. 123-128.

Preview Purchase PDF 109 931

Cited by

google scholar semantic scholar
Export citation
  • Abstract
Aiming at the gray uneven distribution in the night vehicle images, a new local Chan–Vese (LCV) model is proposed for image segmentation. The energy functional of the proposed model consists of three terms: global term, local term and regularization term. By incorporating the local image information into the proposed model, the images with intensity inhomogeneity can be efficiently segmented. Finally, experiments on nighttime plate images have demonstrated the efficiency and robustness of our model. Moreover, comparisons with recent popular local binary fitting (LBF) model also show that our LCV model can segment images with few iteration times.
  • Keywords

license character segmentation CV model intensity inhomogeneity LBF

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{JICS-6-123, author = {ShangbingGao, LanfangWang}, title = {Local Chan-Vese Model for Segmenting Nighttime Vehicle License Characters}, journal = {Journal of Information and Computing Science}, year = {2024}, volume = {6}, number = {2}, pages = {123--128}, abstract = { Aiming at the gray uneven distribution in the night vehicle images, a new local Chan–Vese (LCV) model is proposed for image segmentation. The energy functional of the proposed model consists of three terms: global term, local term and regularization term. By incorporating the local image information into the proposed model, the images with intensity inhomogeneity can be efficiently segmented. Finally, experiments on nighttime plate images have demonstrated the efficiency and robustness of our model. Moreover, comparisons with recent popular local binary fitting (LBF) model also show that our LCV model can segment images with few iteration times. }, issn = {1746-7659}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jics/22685.html} }
TY - JOUR T1 - Local Chan-Vese Model for Segmenting Nighttime Vehicle License Characters AU - ShangbingGao, LanfangWang JO - Journal of Information and Computing Science VL - 2 SP - 123 EP - 128 PY - 2024 DA - 2024/01 SN - 6 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jics/22685.html KW - license character segmentation KW - CV model KW - intensity inhomogeneity KW - LBF AB - Aiming at the gray uneven distribution in the night vehicle images, a new local Chan–Vese (LCV) model is proposed for image segmentation. The energy functional of the proposed model consists of three terms: global term, local term and regularization term. By incorporating the local image information into the proposed model, the images with intensity inhomogeneity can be efficiently segmented. Finally, experiments on nighttime plate images have demonstrated the efficiency and robustness of our model. Moreover, comparisons with recent popular local binary fitting (LBF) model also show that our LCV model can segment images with few iteration times.
ShangbingGao, LanfangWang. (2024). Local Chan-Vese Model for Segmenting Nighttime Vehicle License Characters. Journal of Information and Computing Science. 6 (2). 123-128. doi:
Copy to clipboard
BibteX RIS TXT
The citation has been copied to your clipboard