模糊推理论文：基于模糊逻辑的车牌识别系统研究

1、模糊推理论文：基于模糊逻辑的车牌识别系统研究【中文摘要】随着科学技术与国民经济的发展,智能交通系统的研究成为近年来的研究热点,车牌识别系统是其重要的组成部分之一,它被广泛应用于道路监控、车辆管理以及高速公路自动收费等领域中。模糊推理是一种模仿人类处理问题的推理算法。其优点是处理问题时直接根据所给的模糊规则对问题进行求解,无需考虑所求问题的数学模型。此外由于其具有擅长处理模糊信息的特性,使其在众多领域都有广泛应用,如模糊控制,模糊神经网络、模糊决策等。车牌识别系统依次可分为图像预处理、车牌定位、字符分割和字符识别这四个模块。本文在分析研究了模糊推理和国内外车牌识别系统的基础上,根据我国车牌的特点

2、提出了一套基于模糊逻辑的车牌识别系统,通过实验仿真取得了较理想的效果。本文的主要工作有:1.在图像预处理中提出一种基于直觉模糊熵的图像二值化方法,并将其与传统的Otsu二值化方法进行比较,此外根据灰度图像中车牌区域的像素标准差在一定的范围内这一特征,提出一种基于标准差的车牌图像简化方法。2.分别对基于颜色特征与灰度特征的车牌定位方法进行研究,根据其各自的优缺点将两种方法进行结合,提出了一种基于相似度的模板匹配定位方法。该方法通过构造车牌模板,计算出车牌中各候选区与模板的相似度,选取其中相似度最大值所对应的候选区作为粗定位结果。3.对传统的三层BP神经网路进行了研究,针对其存在的缺陷,提出一种改

3、进的BP神经网络,该方法是在已有的基于模糊推理的自适应学习率BP神经网络的基础上针对其规则获取方面存在的困难,提出了一种基于遗传算法的模糊规则自动提取算法,并将该算法与传统的BP算法进行了比较。4.将上述的BP神经网络用于车牌字符的识别中,并对几种常用的字符特征抽取方法进行了比较。【英文摘要】Following the development of science and economy, intelligent transport system has become a focus of research attention. The license plate recognition sy

4、stem is the one of its important parts. It has been widely used in road monitoring, vehicle management and highway automatic fee, etc. Fuzzy Reasoning is the algorithm which can imitate the way of human deal with the problem. It can solve the problem directly by using Fuzzy rules and unnecessary to

5、construct the mathmatic model of the problem. Since it is good at dealing with the fuzzy information, it has been used in many fields, for example, fuzzy control, fuzzy neural network and fuzzy decision-making etc.License plate recognition system can be divided into to four parts: image pre-processi

6、ng, vehicle license plate location, character segmentation and character recognition. By analysing the fuzzy reasoning algorithm and vehicle plate recognition system at home and abroad, we present a Fuzzy Logic-based license plate recognition system. The experimental results show that the method is

7、effective for car license plate location.The major research of this paper can be follow as:1. In the image pre-processing, a method of binary-valuation based on concept of intuition fuzzy entropy is proposed, and we compared it with the Otsu algorithm. Assumed that the standard deviation of the pixe

8、ls in the license plate within a certain range, based on which a method to simplify the license plate image is proposed.2. By researching the color-based and the texture-based method of the location license plate, respectively. A new algorithm which based on similarity measure template matching is p

9、resented. This method assumed the license plate image is consisted by candidate license plates. In this method we construct the template for representing license plates, then we construct the fuzzy sets for representing the template car pictures, respectively. The similarity for each template size l

10、ocation of each photo is estimated by using the method of sliding windows, choose the one with biggest value of similarity as the range of interesting.3. By research the Traditional Back-Propagation(TBP) algorithm, a improve BP(Back-Propagation) neural network has been presented. The learning rate o

11、f this new BP neural network is adjusted by fuzzy reasoning method and the Genetic algorithm is used for getting the fuzzy rules. The simulation results indicate that the new algorithm has improved TBP algorithm effectively.4. Using the improve BP neural network for character recognition, and compar

12、e the effectively with different feature extraction methods.【关键词】模糊推理 车牌识别 BP神经网络 字符识别 遗传算法【备注】索购全文在线加好友：1.3.9.9.3.8848 同时提供论文写作一对一指导和论文发表委托服务【英文关键词】Fuzzy reasoning License Plate Recognize Back-Propagation algorithm Genetic algorithm【目录】基于模糊逻辑的车牌识别系统研究摘要4-5Abstract5-6第一章 绪论9-131.1 研究背景及意义91.2 相关技术介绍

13、9-101.3 国内外研究现状及发展趋势10-111.4 论文的研究内容及主要工作111.5 论文的结构11-121.6 车牌系统的实现平台12-13第二章 预备知识13-272.1 模糊集的概念13-142.2 模糊推理14-202.2.1 模糊命题142.2.2 模糊规则14-152.2.3 模糊推理的基本形式152.2.4 模糊推理的CRI 算法15-182.2.5 相似度推理模型18-202.3 图像的预处理技术20-272.3.1 图像的灰度化21-222.3.2 图像的二值化22-232.3.3 图像的增强23-252.3.4 图像的去噪25-27第三章 车牌图像的定位方法研究27

14、-453.1 车牌区域的特征分析27-283.2 基于相似度的车牌定位算法的步骤28-293.3 车牌图像的灰度化29-313.4 基于标准差的车牌图像简化算法31-323.5 车牌图像的边缘检测32-343.6 基于直觉模糊熵的图像二值化34-393.6.1 基于REF(restricted equivalence function)的直觉模糊熵35-373.6.2 基于REF 函数的图像二值化37-393.7 基于HSV 颜色空间的车牌图像分割方法39-413.8 基于相似度的车牌定位方法41-443.8.1 模板的构造41-423.8.2 相似度的计算423.8.3 车牌区域的判别与精确

15、定位42-443.9 本章小结44-45第四章 车牌区域的字符分割方法45-514.1 车牌的倾斜校正45-484.1.1 基于水平投影的倾斜校正45-464.1.2 基于Hough 变换的倾斜校正46-474.1.3 图像旋转47-484.2 去除边框及铆钉48-494.3 字符分割49-504.4 字符归一化504.5 本章小结50-51第五章 字符识别51-685.1 常用的字符识别方法51-525.2 BP 神经网络的基本原理52-545.3 基于模糊推理的在线调整学习率算法的研究54-575.3.1 基于CRI 推理模型的自适应步长BP 算法55-565.3.2 基于相似度推理的自适应步长BP 算法56-575.4 遗传算法57-595.5 基于遗传算法的模糊规则库自动获取59-615.5.1 基于遗传算法的CRI-模糊规则构造方法59-605.5.2 基于遗传算法的相似度推理规则构造方法60-615.5.3 遗传算法中的适应度函数615.6 车牌字符识别61-645.6.1 车牌字符的特征提取61-635.6.2 基于BP 神经网络的字符识别63-645.7 字符识别