A Nighttime Vehicle License Character Segmentation Algorithm

Abstract:

Chan-Vese model often leads to poor segmentation results for images with intensity inhomogeneity. 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 for the proposed model consists of three terms, i.e., 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 that our model can segment the nighttime plate images efficently. Moreover, comparisons with recent popular local binary fitting (LBF) model also show that our LCV model can segment images with few iteration times.

• Periodical: Advanced Materials Research (Volumes 532-533)
• Pages: 1583-1587
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.532-533.1583
• Online since: June 2012
• Authors: Shang Bing Gao, Dong Jin
• Keywords: CV Model, Intensity Inhomogeneity, LBF, License Character Segmentation

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