本文選題：車牌定位 + 多特征與多方法�。� 參考：《西安電子科技大學》2014年碩士論文

【摘要】：伴隨著科學技術的飛速發(fā)展、日常生活質(zhì)量的全面提高，，汽車的數(shù)量在各個國家的總體數(shù)量都出現(xiàn)出了快速的增長。目前我國的汽車數(shù)量已經(jīng)增加到一億兩千萬輛，是世界汽車總數(shù)量的12%，已經(jīng)成為了世界上第二大的汽車國家，并且其數(shù)量仍然在快速的增長當中。當然，隨著汽車數(shù)量的不斷增多，馬路的不斷加寬加長，對這些汽車的管理也就是現(xiàn)在的交通問題越來越被人們所關注，從而使其成為了一門很重要的學科來研究。在這門學科中主要研究的問題又依賴于對車輛的識別，車輛識別也就是車牌的識別。 一般車牌識別的步驟主要有三部分內(nèi)容：車牌定位，車牌字符分割，車牌字符識別。這三個部分組成了一個完整的車牌識別的過程。由于精力有限，本文只對三個步驟中的車牌定位和字符分割進行相應的仿真研究。 本文用到的定位方法是根據(jù)車牌的字符本身的橫向和縱向掃面特征、車牌的字符和底板的色彩差別特征，再結合其他的特征，通過特定算法的篩選過程來確定車牌位置并提取出車牌圖像。主要算法是通過比較決定使用Prewitt算子邊緣檢測提取車牌的邊緣信息，根據(jù)車牌的垂直方向掃面特征和周邊色彩區(qū)別特點去除一定的干擾邊緣信息，連接邊緣點，標記連通域，根據(jù)車牌的形狀特征并采用Adaboost方法繼續(xù)去除干擾信息凸顯車牌信息以分離出車牌圖像，對分離出來的車牌圖像傾斜校正，去除上下邊界與左右邊界得到精確的車牌字符區(qū)域。經(jīng)過仿真分析，本文算法能夠很好的定位出車牌圖像，對單車牌圖像和多車牌圖像都適用，定位成功率高，通用性好。 本文的另外一部分是車牌分割，這部分內(nèi)容第一步先分析了幾種典型的字符分割的方法，使用了基于車牌垂直方向投影法與先驗知識作為模板匹配結合的車牌字符分割方法來將前面經(jīng)過精確定位的車牌字符圖片分割成單獨的字符。
[Abstract]:With the rapid development of science and technology, the overall improvement of the quality of daily life, the number of cars has increased rapidly in the total number of countries. At present, the number of cars in our country has increased to one hundred and twenty million vehicles, 12% of the total number of cars in the world, and has become the second largest automobile country in the world, and the number of cars in the world has become the second largest car country in the world. The number is still growing rapidly. Of course, with the increasing number of cars and the widening of the road, the management of these cars is becoming more and more concerned about the current traffic problems, which makes it a very important subject to study. Vehicle recognition and vehicle recognition are license plate recognition.
There are three main steps in the general license plate recognition: license plate location, license plate character segmentation, and license plate character recognition. These three parts make up a complete process of license plate recognition. Because of the limited energy, this paper only carries out the corresponding simulation research on the license plate location and character segmentation in the three steps.
The location method used in this paper is based on the characteristics of the transverse and longitudinal sweep of the character of the license plate, the characteristics of the color difference between the character of the license plate and the floor, and then combining the other features, the location of the license plate is determined by the selection process of the specific algorithm and the license plate image is extracted. The main calculation method is to determine the edge detection using the Prewitt operator by comparison. The edge information of the license plate is extracted, the interference edge information is removed according to the characteristics of the vertical sweep surface and the peripheral color, the edge points are connected and the connected domain is tagged. According to the shape feature of the license plate and the Adaboost method, the vehicle license information can be removed to separate the license plate image. The license plate image is inclined to correct, and the accurate license plate character area is obtained by removing the upper and lower boundary and the left and right boundaries. After simulation analysis, this algorithm can locate the license plate image well. It is suitable for both the single license plate image and the multiple license plate image, and the success rate of the location is high and the versatility is good.
The other part of this paper is the license plate segmentation. In this part, the first step is to analyze several typical character segmentation methods, and use the license plate character segmentation method based on the vertical direction projection method and the prior knowledge as template matching to divide the previously accurately located license plate character pictures into separate characters.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：U495;TP391.41

【參考文獻】

中國期刊全文數(shù)據(jù)庫 前10條

1 韓麗萍;藥春暉;張文格;尹王保;;基于小波變換與形態(tài)學的車牌定位方法[J];測試技術學報;2006年01期

2 李波,曾致遠,付祥勝;基于數(shù)學形態(tài)學和邊緣特征的車牌定位算法[J];電視技術;2005年07期

3 王杰;李洪興;王加銀;苗志宏;;一種圖像快速線性插值的實現(xiàn)方案與分析[J];電子學報;2009年07期

4 琚旭;王浩;姚宏亮;;基于Boosting的支持向量機組合分類器[J];合肥工業(yè)大學學報(自然科學版);2006年10期

5 陳寅鵬,丁曉青;復雜車輛圖像中的車牌定位與字符分割方法[J];紅外與激光工程;2004年01期

6 陳黎,黃心漢,王敏,李煒;基于聚類分析的車牌字符分割方法[J];計算機工程與應用;2002年06期

7 賈曉丹;李文舉;王海姣;;一種新的基于Radon變換的車牌傾斜校正方法[J];計算機工程與應用;2008年03期

8 李亞軍;劉曉霞;陳平;;改進的AdaBoost算法與SVM的組合分類器[J];計算機工程與應用;2008年32期

9 張順利;李衛(wèi)斌;吉軍;;基于投影的文檔圖像傾斜校正方法[J];計算機工程與應用;2010年03期

10 王興玲;;最大類間方差車牌字符分割的模板匹配算法[J];計算機工程;2006年19期

中國博士學位論文全文數(shù)據(jù)庫 前1條

1 高鵬毅;BP神經(jīng)網(wǎng)絡分類器優(yōu)化技術研究[D];華中科技大學;2012年

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