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基于改进自适应图像分割算法的车牌识别技术研究 被引量:4

Research on License Plate Recognition Technology Based on Improved Adaptive Image Segmentation Algorithm
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摘要 基于CV(Chan-Vese)模型图像分割方法的不足,提出了一种改进的自适应图像分割方法,用于汽车车牌的字符识别.在这一方法中,为了避免初始位置差异对于分割效率的影响,设计了更为合理的分割流程.水平集合理论配合优化迭代算法,给出多个局部初值,大大增强了分割算法的自适应性能.实验结果表明,相比于CV模型图像分割方法,改进自适应图像分割方法的准确率更高,适用于汽车车牌图像的分割. An improved adaptive image segmentation method based on CV model is proposed, which is ap-plied to the character recognition of vehicle license plate. In this method, in order to avoid the influence of the initial position difference on the segmentation efficiency, a more reasonable segmentation process is de-signed. The level set theory is combined with the optimal iterative algorithm, which gives a number of lo-cal initial values, which greatly enhances the performance of the segmentation algorithm. The experimen-tal results show that compared with the CV model image segmentation method, the improved adaptive im-age segmentation method is more accurate and suitable for the segmentation of vehicle license plate image.
作者 刘智
出处 《西南师范大学学报(自然科学版)》 CAS 北大核心 2017年第5期28-33,共6页 Journal of Southwest China Normal University(Natural Science Edition)
基金 国家自然科学基金项目(61462008) 广西教育厅高校科研项目(LX2014187) 广西柳州市科学研究与技术开发计划项目(2016C050205)
关键词 汽车车牌 字符分割 模板匹配 自适应分割 vehicle l icense plate character segmentat ion template matching adaptive segmentation
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