摘要
针对不同的图像视频中计算二维空间对象的转换问题,研究了增强现实(Augmented Reality,AR)系统模型中的图像拼接、物体探测、有效跟踪以及虚实物体注册等,基于给定的模板提出了一种在视频序列中进行物体实时探测和有效跟踪的方法。研究表明,该方法可行且效果明显,为军用装备系统创新设计提供了理论依据和技术支持。
To enhance the license plate character recognition rate, a method which uses a two-stage classifier of SVM （Support Vector Machine） is proposed, based on the whole and local features. The first-stage classifier aims at all characters. The characters are sent to the corresponding second-stage classifier for further recognition if their identify results belong to the confused characters. The first-stage classifier extracts the whole grid rates of the character images as the classification features of SVM. The confused characters are divided into five groups, and then five corresponding SVM constitute the second-stage classifier. Through analyzing the local differences of the character stroke features in each group, the features are extracted like vertical projection variances, horizontal projection variances and proportions, which belong to the character outline of the grids. After that, they are processed with feature fusion to make up the classification features of SVM. The experimental results show that the recognition time is 23.45 ms. The method has higher recognition rate of the confused character and the overall recognition rate than the template matching methods, neural network approaches and other previous hierarchical recognition methods.
出处
《电视技术》
北大核心
2014年第11期224-228,243,共6页
Video Engineering
作者简介
焦楷哲（1989-）．硕士生，研究领域为军用电源设备；
程培源（1967-）．硕士生导师．教授。主要从事军用电源设备教学和科研工作。
