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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.

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  • 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

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COPYRIGHT: © Global Science Press

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@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:
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