车牌识别系统关键技术研究

倾斜角度、边框清晰度影响着车牌的校正，边框、铆钉和间隔符等也影响字符的提取，该文提出了一种改进的Harris角点检测算法，该方法不受倾斜角度影响，直接定位车牌内角点，可将内角点作为校正基准点。校正后的车牌不存在边框，字符串位于图像的中心位置，可以根据车牌字符串的整体位置值提取字符。实验证明，该算法在车牌校正与字符提取方面是有效的。     

车牌图像的局部倾斜畸变校正方法

针对车牌图像的倾斜畸变会对后继字符切割和识别结果带来不利影响的问题,提出使用局部技术和适度缩放技术对车牌图像倾斜畸变进行校正。根据车牌图像不同位置倾斜畸变程度不一样的特点将车牌图像分成若干份;分别对每份图像使用“连接点”法进行倾斜畸变校正,根据字符高度的变化对字符宽度进行同样的缩放;将校正之后的各个子图像连接在一起成为校正之后的车牌图像。实验表明,使用局部法对倾斜畸变车牌图像进行校正的效果要优于使用全局法。     

基于颜色对特征点主成分分析的车牌校正方法

为了在车牌的倾斜校正过程中减少车牌边框和噪声干扰的影响,并简化计算过程,提出了一种基于颜色对特征点主成分分析(PCA)的车牌水平校正方法。该方法根据车牌背景与字符交界处的颜色具有固定搭配这一特点,首先在原始车牌图像中提取颜色对特征点,并将所有颜色对特征点视为待分析的样本;然后构建这些样本特征点的2维散布矩阵,并通过主成分分析求出其主成分方向,该主成分方向就是车牌的水平倾斜方向;最后再进行相应的旋转,即可获得校正后图像。由于这种方法将车牌的颜色信息和边缘信息融合后共同使用于车牌的校正过程,同时将搜索图像倾斜角度转换为2维实对称矩阵进行计算,从而简化了计算。通过编程对实际车牌图像进行的实验结果证明,该方法对于边框不清或含有噪声干扰的图像仍然能取得较好的校正结果。     

基于投影和固有特征结合的车牌字符分割方法

根据车牌字符的固有特征,提出一种新的基于投影的车牌字符分割方法.该方法首先对车牌图像进行预处理,检测车牌倾斜角度,如果倾斜角大于指定角度则进行车牌倾斜校正,然后利用车牌的水平方向投影去除车牌的上下边框以及铆钉,对处理得到的图像进行二值化.再根据车牌字符的排列规则和字符间距的关系,利用车牌的垂直投影定位字符,先分割出第二个和第三个字符,从第三个字符开始分割出后五个字符,再利用已分割字符的知识来分割前两个字符,然后对分割出来的候选字符块进行处理,有效解决字符粘连和断裂的情况,最终实现车牌字符的准确分割.实验结果证明,该方法有较好的分割效果.     

基于字符投影最小距离的车牌校正方法

提出一种利用字符投影距离进行车牌校正的方法。根据伴生与互补颜色特征提取车牌中的字符区域,将车牌旋转计算字符区域在垂直坐标轴上的投影,利用投影的最小距离获取垂直倾斜角度。将车牌水平错切计算字符区域在水平方向投影,利用投影最小距离获得水平错切角度,采用双线性插值对图像进行校正。该方法不依赖车牌边框特征,抗干扰性强。实测车牌图像试验证明,该方法具有较高的鲁棒性和工程实用性。     

基于最小字符投影的车牌图像错切校正方法

为解决车牌图像倾斜对字符分割与识别带来的不利影响,提出一种基于字符投影的车牌图像错切校正方法.对旋转后的车牌字符区域向水平坐标上投影,利用最小投影距离获取水平错切角,采用二维几何变换对图像进行校正.试验结果表明,该方法不依赖车牌边框特征,抗干扰性强,具有较高的鲁棒性.     

基于灭点的倾斜畸变车牌图像快速校正方法研究*

由于相机与汽车牌照之间的方位关系导致了拍摄图像发生倾斜畸变,极大影响了车牌识别率。针对这一问题,利用畸变图像所蕴涵的灭点信息,提出了一种快速校正方法：首先对图像进行预处理,提取车牌的四条边框来产生灭点;然后根据灭点信息快速标定相机内参数并计算空间平面重建矩阵;最后进行空间坐标变换和灰度插值完成畸变图像的校正。实验表明,该方法对于倾斜畸变车牌有良好的校正效果,并有效提高了识别率。     

基于先验信息和射影几何变换的车牌图像矫正*

为了便于字符分割、识别,车牌识别系统需要将车牌的倾斜图像矫正为无倾斜和旋转的标准图像。目前多数文献采用Hough变换检测倾斜角度并直接进行矫正,但存在的缺陷。为此从射影几何的观点提出了新算法：利用车牌固有的先验信息,将车牌与车牌图像之间的透视变换矫正为仿射变换,再将仿射变换矫正为相似变换,将此相似变换看做是对原车牌进行旋转和全局缩放的结果,消除其中的旋转因素,就得到一个与车牌仅差一个缩放因子的标准车牌图像,以便于后续的字符处理。     

基于子区域投影分析的车牌倾斜校正

提出一种基于子区域投影分析的车牌倾斜校正方法.首先进行水平倾斜校正,将车牌图像划分为水平方向上的子区域,进行投影分析后确定子区域内字符区域中心点坐标,然后将各中心点拟合为一条直线,直线的倾角即为水平倾角.对于检测垂直倾角,该方法将车牌图像划分为垂直方向上的子区域,利用子区域的投影来快速计算车牌图像的错切变换和垂直投影,确定垂直倾角.实验结果表明,与常用的旋转投影法相比,该方法运算时间少且鲁棒性好.     

基于最小二乘和最小投影距离的车牌倾斜校正

在车牌图像的采集过程中,经常会有车牌倾斜的现象发生,这种倾斜给后续的字符分割和字符识别造成了很多不利影响。为此,文中提出了一种基于最小二乘和最小投影距离的车牌倾斜校正方法。该方法将车牌倾斜分成水平倾斜和垂直倾斜两部分:对于水平倾斜,首先对二值化后的车牌去边框和铆钉,再对车牌利用最小二乘拟合直线求取倾斜角;而对于垂直倾斜,则引入分块查找法来降低查找最小投影距离的执行次数,从而提高算法的执行效率。实验结果表明:该算法简单实用,能够准确地对车牌进行校正。     

Vehicle license plate tilt correction based on the straight line fitting method and minimizing variance of coordinates of projection points

Tilt correction is a very crucial and inevitable task in the automatic recognition of the vehicle license plate (VLP). In this paper, according to the least square fitting with perpendicular offsets (LSFPO), the VLP region is fitted to a straight line. After the line slope is obtained, rotation angle of the VLP is estimated. Then the whole image is rotated for tilt correction in horizontal direction by this angle. Tilt correction in vertical direction by minimizing the variance of coordinates of the projection points is proposed. Character segmentation is performed after horizontal correction and character points are projected along the vertical direction after shear transform. Despite the success of VLP detection approaches in the past decades, a few of them can effectively locate license plate (LP), even when vehicle bodies and LPs have similar color. A common drawback of color-based VLP detection is the failure to detect the boundaries or border of LPs. In this paper, we propose a modified recursive labeling algorithm for solving this problem and detecting candidate regions. According to different colored LP, these candidate regions may include LP regions. Geometrical properties of the LP such as area, bounding box and aspect-ratio are then used for classification. Various LP images were used with a variety of conditions to test the proposed method and results are presented to prove its effectiveness.     

A new method for correcting vehicle license plate tilt

In the course of vehicle license plate （VLP） automatic recognition, tilt correction is a very crucial process. According to Karhunen-Loeve （K-L） transformation, the coordinates of characters in the image are arranged into a two-dimensional covariance matrix, on the basis of which the centered process is carried out. Then, the eigenvector and the rotation angle α are computed in turn. The whole image is rotated by -α. Thus, image horizontal tilt correction is performed. In the vertical tilt correction process, three correction methods, which are K-L transformation method, the line fitting method based on K -means clustering （LFMBKC）, and the line fitting based on least squares （LFMBLS）, are put forward to compute the vertical tilt angle θ. After shear transformation （ST） is imposed on the rotated image, the final correction image is obtained. The experimental results verify that this proposed method can be easily implemented, and can quickly and accurately get the tilt angle. It provides a new effective way for the VLP image tilt correction as well.     

一种有效的新闻视频主题字幕检测方法

提出一种新的有效的新闻视频主题字幕检测方法。利用像素的空间量化亮度值计算局部帧差变化,以便检测新闻视频字幕的出现帧和消失帧,并建立4条规则来进一步区分主题字幕和非主题字幕,然后对同一主题字幕所在帧利用多帧结合的方法生成两个候选的主题字幕文字检测帧,选择其中之一进行文字区域的定位和提取;最后用投影法分割字符,并对投影法产生的错误分割字符的常见情况,提出相应的解决方法。实验结果表明了该方法的有效性。     

Vehicle license plate character segmentation

Vehicle license plate (VLP) character segmentation is an important part of the vehicle license plate recognition system (VLPRS). This paper proposes a least square method (LSM) to treat horizontal tilt and vertical tilt in VLP images. Auxiliary lines are added into the image (or the tilt-corrected image) to make the separated parts of each Chinese character to be an interconnected region. The noise regions will be eliminated after two fusing images are merged according to the minimum principle of gray values.Then, the characters are segmented by projection method (PM) and the final character images are obtained. The experimental results show that this method features fast processing and good performance in segmentation.     

Vehicle license plate super-resolution using soft learning prior

This paper proposes a new algorithm to perform single-frame image super-resolution (SR) of vehicle license plate (VLP) using soft learning prior. Conventional single-frame SR/interpolation methods such as bi-cubic interpolation often experience over-smoothing near the edges and textured regions. Therefore, learning-based methods have been proposed to handle these shortcomings by incorporating a learning term so that the reconstructed high-resolution images can be guided towards these models. However, existing learning-based methods employ a binary hard-decision approach to determine whether the prior models are fully relevant or totally irrelevant. This approach, however, is inconsistent with many practical applications as the degree of relevance for the prior models may vary. In view of this, this paper proposes a new framework that adopts a soft learning approach in license plate super-resolution. The method integrates image SR with optical character recognition (OCR) to perform VLP SR. The importance of the prior models is estimated through relevance scores obtained from the OCR. These are then incorporated as a soft learning term into a new regularized cost function. Experimental results show that the proposed method is effective in handling license plate SR in both simulated and real experiments.     

含运动目标的红外图像非均匀校正算法

通过分析运动目标对基于图像配准的非均匀校正算法校正效果的影响,基于相邻帧之间运动目标辐射强度不变的假设,提出含运动目标的红外图像非均匀校正算法。通过临近帧补全运动目标遮挡的场景,消除运动目标的影响,进行非均匀校正得到场景校正帧,再利用运动目标的辐射信息对各帧运动目标进行非均匀校正,最终得到全图校正帧。仿真和实测红外图像校正实验表明：算法能够很好地解决含运动目标的红外序列图像的非均匀校正问题。     

维吾尔文扫描页的倾斜校正

为了方便维文图像的切分和识别工作, 提出将基于凸多边形的最小面积外接矩形法和基线拟合法相结合的检测和校正图像方法。首先提取边缘像素生成凸多边形, 利用基于凸多边形的最小面积外接矩形法实现初步的倾斜校正, 再根据维文沿基线书写的特点, 将图像细化后采用基线拟合的方法完成维文文本的精确校正。实验结果表明, 该方法能够精确地检测出文本图像的倾斜角并校正, 是行之有效的方法。     

一种基于区域综合特征的彩色图像检索方法

提出了一种基于区域综合特征的彩色图像检索算法.该算法首先结合MPEG-7视觉内容描述对真彩色图像进行量化处理,并将量化后的图像划分成若干个子区域.然后选取子区域的主要颜色及其所占百分率作为颜色特征,选取子区域的熵、能量和对比度作为纹理特征.再综合利用上述颜色、纹理两个特征计算图像间内容的相似度,并进行彩色图像检索.仿真实验表明,该算法能够准确和高效地查找出用户所需内容的彩色图像,并且具有较好的查准率、查全率和较快的检索速度.