陕 西 理 工 学 院 毕 业 设 计
致谢
在这里首先要感谢我的导师陈莉老师，从我论文选题开始，她就一直对我进行细心地指导，包括选题方向和设计重点，他都一一进行了单独分析和单独指导。陈老师严肃的科学态度，严谨的治学精神，精益求精的工作作风，深深地感染和激励着我，始终是我工作、学习中的榜样，使我在今后的工作中会更加努力严格的要求自己。在此，我再次向老师表示最衷心的感谢!
其次我要感谢我们同一个小组的同学们，他们平时给了我很多的帮助，帮助我在本次设计中解决了大量的问题。没有他们，我很难顺利完成本次设计。
在这里我衷心的感谢，帮助过我的每一个人。
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参考文献
[1] 陆化普,李瑞敏,朱茵.智能交通系统概论[M].中国铁道出版社,2004.
[2] 何书前,张学平.车牌识别关键技术的应用研究[J].电脑知识与技术, 2013.30(05):339-415. [3]吴学明. 图像分割的算法研究[D].成都:成都理工大学,2006. [4]彭丽. 基于边缘信息的阈值图像分割[D].武汉:中南大学,2009.
[5]杨方方. 彩色图像分割技术的研究—图像边缘检测技术的研究应用[D].无锡:江南大学,2009. [6] 陈虹.基于数字图像处理对汽车牌照自动识别系统的研究[J].上海公路, 2008,32(08):51-52. [7]陈蕾. 基于边缘信息的图像分割技术研究[D].西安:西安电子科技大学,2009.
[8]张玉姣,史忠科.一种新的车牌识别与处理算法[J].西北工业大学学报.2002,20(07):85-86. [9]张俭鸽,李娜.车牌定位在VC中的实现[J].中国科技信息,2009,13(05):22-24.
[10]吕文敏.车牌识别系统中图像的采集和定位问题研究[J].计算机与现代化,2009,03(06):13-15. [11]石朋飞.复杂背景下车牌的定位和字符分割技术研究[J].苏州大学学报,2009,01(04):56-58. [12]陈智斌,黎绍发,余棉水.车辆牌照定位算法研究[J].计算机工程与设计,2006.10(05):66-67. [13]郭捷,施鹏飞.基于彩色和纹理分析的车牌定位方法[J].中国图象图形学报.2002,7(08):78-79. [14]蔡立平.车牌识别系统中的车牌定位算法研究[J]. 西安电子科技大学出版社,2006, 32（03）:11-13.
[15]W.Myint,P.Gober, A.Brazel, S.Grossman-Clarke and Q.Weng,“Perpixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery”, Remote Sensing of Environment,115, pp.1145-1161,2011.
[16]W. Burger and M., Burge. Digital Image Processing: An Algorithmic Introduction Using Java. Texts in Computer Science, Ed. Springer, 45, pp. 45-61,2012.
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附录A 英文文献原文
The Development of A Kind of Online Image Code Recognition System
Abstract: This paper describes the design and the implement of online image coding char recognition system. It analyses and researches the important contents about the system. Then it provides the solutions of main problems. In recognition algorithm, combining template matching with feature recognition, it put forword an improved template matching algorithm based on feature weights. The algorithm can obviously improve the char recognition ratio.
Keyword: image processing; pattern recognition; feature weights; software design 0 .Introduction
Character recognition of image coding is still the subject of intense study at home and abroad, it has broad applications, such as Automatic number plate recognition, postal code of the automatic identification, automatic reading papers, reports, automatic processing, because of this online image coded character recognition has some common, this paper online tire coding character recognition system for the general image coding character recognition system has been elaborated on the key link of the research and analysis, the method of the other online image coded character system Development of guiding significance.
1.An online image coding identification system processes
Online image coding character recognition system includes digital image capture, storage, image preprocessing, encoding the image extraction, feature extraction coding, coding identification and follow-up treatment of some aspects of its flow chart shown in Figure 1.
Coded image capture Image Feature Extraction Follow-up
Figure A1 line character recognition image coding system flowchart
Online tire image coding character recognition system requires the production pipeline through the acquisition of each tire with tire encoded image, and then through image processing, coding to extract features of the tire, using the appropriate recognition algorithm to identify each coded character. Tire
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陕 西 理 工 学 院 毕 业 设 计
coding characters as a certain deformation in the tires, and different camera angles, are also great differences in the coding images, regularity is poor, so coded image preprocessing and recognition algorithms of selection is very important.
2.Image Acquisition and Storage
Line coding commonly used digital camera images, digital cameras, digital video cameras capture and processed in computer, the system uses QuickCamPro4000 tire coding digital camera image capture, directly from JPG format.
Coded images generally must first convert BMP image format, because the BMP format has become the de facto standard PC in the field - almost all of the Windows operating system designed for image processing software to support this format of the image. BMP is the original Windows bitmap format, which can be used to save any type of digital map data, can support all Windows supported screen resolution and color combination. Under normal circumstances, in order to ensure the display of high efficiency, it does not have any compressed image data, so a small bitmap may occupy considerable space.
BMP bitmap file includes the bitmap file header, bitmap information header, palette, bitmap data area of four parts, bitmap file header from 14 bytes constitute the bitmap header from 40 bytes composition, tone color palette depends on the number of monochrome color images.
Board accounted for 8 bytes, 16-color palette images accounted for 64 bytes, 256-color palette image 1024 bytes total, 224-color images without color palette, the bitmap data from the region under the order of the data by row and on the arrangement from left to right. Preprocessing
Image preprocessing includes are: gray image, image noise reduction and enhancement, coding, edge detection, image geometry correction, image coding region of extraction, encoding image binarization, character segmentation, character normalization and so on. Here are some key aspects of the process.
3.gray image processing
Images are usually color coded, the actual identification with the image is grayscale, where the need to convert first color-coded images to grayscale. In the RGB color model, if R = G = B, then color (R, G, B) indicates a Black white color, in which R = G = B is called the value of gray value, gray level processing is to make the color of the R , G, B component value equal to the process. Gray-scale processing methods are commonly used weighted average method, that is,
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R = G = B = (WRR + WGG + WBB) / 3
Which, WR, WG, WB are the R, G, B the weight of experimental and theoretical proof, when WR = 0.3, WG = 0.59, WB = 0.11, that is when R = G = B = 0.30R +0.59 G +0.11 B, can be the most reasonable grayscale.
3.2 image enhancement processing 3.2.1 Direct gray-scale transformation
① linear gray level transformation: if the image gray scale is linear, as in the original image f (x, y) gray-scale range of [a, b], asked the transformed image intensity range of up to [c, d], According to the linear law, the transformed image g (x, y) as:
g(x,y)?d?cf(x,y)?cb?a (A1)
② nonlinear transformation -- log transformation and exponential transformation:When the need to expand low gray zone, gray zone of high compression used on the log transformation, when the need to expand the use of high gray area index transformation. 3.2.2 smoothing filter - Noise Reduction
As the noise in the area corresponding to the edge of the image gray value of such rapid change with a larger part is a high frequency, so the use of low-pass filter (ie, smoothing filter) noise. At the same time can make the image fuzzy smoothing is beneficial to the larger goal of the extraction prior to removal of the smaller details or to target the small interruption link.
Smoothing noise reduction method is to use the template on the image convolution operation, linear smoothing filter is the most commonly used template is shown in Figure 2 of the 3 × 3 template, this template and image in pixels by the following method of convolution , get smooth image noise reduction.
In the figure, roaming the template and the template center and map location of each pixel overlap;the template on the coefficient multiplied with the template under the corresponding pixel;add all the product;It will assign the figure corresponds to the template and the center of the pixel.
The most commonly used non-linear smoothing filter is median filter, it will all of the values of the region are sorted according to size, will be sorted in the middle of the pixel values given to the center pixel. Median filter can effectively remove the random noise, can get a better visual effect. 3.3 Edge detection coding
Edge is the result of discrete gray value can be used to request the first and second derivative method
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