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机构地区:[1]集美大学轮机工程学院,福建厦门361021 [2]厦门大学自动化系,福建厦门361005
出 处:《光电工程》2012年第7期115-119,共5页Opto-Electronic Engineering
基 金:国家自然科学基金资助项目(51179074);集美大学李尚大学科建设基金资助项目(ZC2001006/C511012)
摘 要:仪表数字识别是智能仪表应用的关键,针对现有方法对角度倾斜、半字识别效率低的问题,引入硬度特征参数来衡量数字图像目标区域在某方向上的抵抗变形的能力,提出了一种结合数字结构特征和统计特征的识别方法。通过对仪表盘上采集的数字进行分析,建立数字自上而下及自下而上的硬度特征库。并依据每个特征重要程度的不同,引入权重,采用加权特征匹配的方法进行数字识别。实验表明,算法不仅简单高效,对于整字和半字都能够取得很好的分割和识别效果,而且对旋转和畸变有较强的容错。Meter digital identification is crucial for intelligent meter application. Current methods have lower recognition ratio at presence of tilt angle and half-digits. In order to solve this problem, the hardness feature parameters are introduced to measure anti-deformation ability on the direction of the projection target area. A number identification method based on the combination of digital structure feature and statistical feature is presented. By analysising different height and different angle of the figures acquired from Meter, the template library of digital top-down and bottom-up hardness characteristics is established. Weights are set according to the different importance of characteristics and the digital recognition is realized by using the method of weighted feature matching algorithm. Experiments prove that the algorithm is simple and efficient to words or words in half and has strong fault-tolerant with rotation and distortion.
分 类 号:TP391.4[自动化与计算机技术—计算机应用技术]
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