对汽车牌照的定位与分割技术进行了详细的论述。
Introduced the technology of orientation and segmentation of car plate in detail.
该文在对收费站汽车牌照特征分析的基础上，提出了一种改进的基于扫描行的灰度跳变点特征的车牌定位算法。
Under the basis of the analysis to characteristic of toll station vehicle number-plate, this paper presents an improved number-plate locating method based on the characteristics of gray beep points.
最后对牌照进行行扫描和列扫描，完成车牌定位。
Finally with the line scan and row scan, finishing the License Plate Location.
尽管如此，目前国内尚无一个完善和通用的汽车牌照定位方法。
Nevertheless, there was not a perfect license plate location at home.
汽车牌照自动识别系统的核心技术主要包括车辆定位、字符分割和车牌字符识别。
The kernel technique of the license plate Auto recognition System is mainly composed of license plate locating, character segmentation and character recognition.
在复杂背景下，实现汽车牌照的快速定位是目前车牌识别领域亟待解决的问题。
Under complex background, a problem need to be solved urgently is to realize fast license plate locating.
汽车牌照识别系统主要包括三个部分：车牌预处理和定位、字符的分割以及单个字符的识别。
License plate Recognition system includes three parts: plate pretreatment and positioning, character segmentation and identification of a single character.
通过车牌定位、车牌倾斜校正、字符切分和字符识别四个步骤完成了汽车牌照的识别，介绍了每一步骤的关键技术和实验结果。
License plate recognition includes four steps, that is, license plate orientation, license plate lean revise, character segmentation and character recognition. It introduces the above four steps.
今朝国内外汽车牌照定位与识别技术主要采取软硬结合方式和软件方式两种技术方案。
At home and abroad location and license plate recognition software and hardware integration in mainly two kinds of technical solutions and software means.
本文主要对车辆牌照识别系统中的车牌定位与字符分割技术进行研究。
This paper has studied on license plate localization and character segmentation in LPR system.
本文提出了一种基于门限化和宽线检测算子的自适应牌照定位方法，解决了复杂背景下的汽车牌照定位问题。
In this paper, an effective approach based on adaptive thresholding and wide line detector to locate and segment the license plate from a capturing image with complicated background is proposed.
针对复杂背景及不同光照条件下的车牌图像，提出了一种基于RG B色度空间的车牌定位及校正的新方法，建立了基于RG B色度空间的牌照检测模型。
A new approach is presented for vehicle license plate location and rectification based on RGB chroma space in the complicated-background and different illumination images.
本论文的研究内容包括汽车牌照的定位、字符分割和字符识别三个部分。
The whole process of the system can be divided into three stages: locate license plate, character segmentation and character recognition.
作为一个综合的实时计算机视觉系统，汽车牌照识别技术主要包括牌照定位和牌照识别两个部分。
Being a special computer vision system in the real-time case, the LPR system mainly includes the subsystem of license plate detection and character recognition.
汽车牌照定位是一个较难解决的图像分割问题，神经网络为此问题的解决提供了一个有力工具。
Automatic location of vehicle license plates is a difficult image segmentation problem. Neural network is a powerful tool for solving the problem.
车牌识别算法一般可以分为车牌定位、牌照上字符的分割和字符识别三个主要组成部分。
License plate recognition algorithm consists of three modules in general, those are: license plate location, character segmentation and character recognition.
基于粗集（RS）理论和神经网络的车辆牌照识别系统由车牌定位和字符识别两部分组成。
Recognition system of license plate based on rough set and neural network is consists of 2 parts for orientation and character recognition of license plate.
车牌定位其实就是在车辆图像中定位车牌牌照区域的技术。
Actually, license plate location technology is the specific technology to locate the license plate area in the image of vehicles.
汽车牌照定位是汽车牌照自动识别系统中第一个环节，定位的准确与否对系统起关键性的作用。
The location of the vehicle license plate is the first step in the automotive vehicle license plate recognition system and the accuracy of locating plays an important role in this system.
提出了基于参数自校正纹理分析的牌照定位方法，提高了车牌定位的精度。
Research and Implementation of Algorithms for Vehicle License Plate Positioning & Characters Segmentation Based on Image Analysis;
车牌图像定位是车牌照识别系统的关键，该文提出了一种在高速公路复杂背景下的车牌定位与车牌字符分割方法。
Vehicle license plate locating is of vital importance in license plate recognition system. This paper proposes a new license plate detection and character segmentation method.
车牌图像定位是车牌照识别系统的关键，该文提出了一种在高速公路复杂背景下的车牌定位与车牌字符分割方法。
Vehicle license plate locating is of vital importance in license plate recognition system. This paper proposes a new license plate detection and character segmentation method.
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