@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}
}