表格型票据中框线检测与去除算法

字符笔画与表格线的粘连或交叠是表格型票据中普遍存在的现象,严重影响了后期票据自动识别处理的性能.现有方法大多基于二值图像,未能充分利用灰度图中的框线特征.基于票据图像中的框线特征,提出一种表格型票据预处理中的框线检测与去除算法,首先充分利用票据灰度图像的特点准确地检测出框线,再采用一种连通链结构描述叠加后的框线区域,然后对交叠进行判断和标记,根据标记保留字符笔划去除框线干扰.经过实际银行支票图像测试证明了算法的有效性和鲁棒性.     

用于支票印鉴鉴别的图像预处理及配准算法

为了消除支票印鉴自动鉴别系统中由于不同的盖印条件引起的印鉴质量变化,该文提出了一种印鉴图像预处理及配准算法,即首先利用彩色信息将印鉴灰度图从支票背景中分割出来,然后利用基于梯度值的动态阈值实现印鉴图像的二值化。为了消除印鉴中存在的笔划连接和笔划残缺,该文提出了一种笔划填充和骨架校正算法。实验证明该算法具有较强的适应性、可靠性和实用性。     

中文手写文稿的二值化与行列切分

灰度图象的二值化与行列切分是预处理中的重要环节,对识别系统有很大的影响。针对带有框线的文稿图象,本文提出了双重阈值法的二值化方法,有效地去除了框线。在字符分割部分。本文提出了先三行后单行列切分的方法,准确地提取了字符。     

基于直方图分析和OTSU算法的文字图像二值化

在基于灰度图像的文字提取中,文本图像的二值化算法的优劣直接影响到字符识别的性能。本文对比几种常用的二值化算法,提出一种改进的直方图分析和OTSU算法相结合的算法,该算法利用对直方图信息的区间分析和分类,缩小了查找阈值的范围,最后再利用OTSU求出最优阈值。实验结果表明,该方法能够有效降低光线对OTSU算法的影响,具有较好的分割普适性。     

快速二维Renyi熵阈值分割方法

提出一种基于二维Renyi熵的阈值二值化方法,该方法通过引入二维直方图不仅利用了图像像元点的灰度分布信息,而且充分考虑了像元点之间的空间相关信息,对于低对比度、低信噪比的物体,该方法具有良好的分割效果.运用二维Renyi熵的原理选择灰度阁值对图像进行分割,并且引入量化图像直方图概念讨论了一种快速二维Renyi熵阚值分割算法,该算法能够递推运算和自动寻找阈值,将计算复杂性大大降低.仿真对比实验结果表明,该算法提高了计算效率并具有更佳的分割效果.     

文档处理中背景字符的去除

识别域图像的提取是文档自动处理系统中一个重要的预处理过程。在实际应用中，用户填写的信息常常与版面中的框线和背景字符存在交叠现象，严重影响了系统的性能。本文提出了基于点边距离分析的背景字符去除算法。首先通过灰度图像匹配的方法精定位背景字符子图像；然后利用形态学方法结合笔画的宽度信息对背景字符子图像进行二值化；最后分析像素点到边界距离的变化确定需要填充的像素位置，并通过形态学方法计算像素的填充值。实验采用了真实票据图像中的日期域，实验结果表明本文的方法获得了基本令人满意的效果，背景字符像素被成功去除。     

结合边缘增强的零水印

鲁棒性和不可见性是水印的两个重要特性。为实现该目的,利用奇异值的稳定性和图像梯度提出了一种新的零水印算法。该算法先对图像做分块奇异值分解,提取每块的最大奇异值构成原图像的缩略图;然后计算缩略图的梯度模值并二值化,得到边缘增强的二值图像,即图像的特征信息。最后将处理过的水印信息与图像特征信息做异或运算,其结果将作为提取水印的密钥。实验证明,本算法对滤波、锐化、直方图均衡、压缩、剪切等图像处理操作具有很强的鲁棒性。     

模糊理论在光弹性图像处理中的应用

在光弹性图像处理中，从包含等差线的图像场中提取等倾线一直是有待解决的问题，在对图像的灰度直方图分析的基础上，该文提出了应用模糊理论对图像进行增强，结合二值化和滤波算法，最终得到单纯的等倾线曲线。仿真结果表明，效果良好。     

遗传算法粒在二维最大熵值图像分割中的应用

研究图像分割,针对从图像中提取用户要求的特征目标,最优阈值的选取是图像准确分割的关键技术.传统二维最大熵值算法的最优阈值采用穷举方式进行寻优,耗时长,分割效率较低,易产生误分割.为了提高图像分割效率和准确性,提出一种遗传算法的二维最大熵值图像分割方法.先对原始图像进行灰度转换,绘制出图像的二维直方图.根据二维直方图信息...     

一种基于多阈值的灰度图像去噪算法

对灰度图像的去噪是图像处理技术中的重要组成部分,能够将图像中目标部分的主题信息凸显出来是一项具有重要意义的工作.提出一种灰度图像的去噪算法.本算法首先利用区域增长技术将图像分为不同的区域,这些区域里的像素之间存在一定的相似性,满足一定的相似规则;然后,根据每个区域的像素,计算出各自的适合区域特征的阈值;最后,根据每个阈值分别对每个区域二值化,得到一二值化图像.本算法区别于全局阈值法和局部阈值法,实验结果证明,本算法在提取灰度图像主题信息具有较为明显的效果.     

Iterative model-based binarization algorithm for cheque images

Binarization of document images with poor contrast, strong noise, complex patterns, and variable modalities in the gray-scale histograms is a challenging problem. A new binarization algorithm has been developed to address this problem for personal cheque images. The main contribution of this approach is optimizing the binarization of a part of the document image that suffers from noise interference, referred to as the Target Sub-Image (TSI), using information easily extracted from another noise-free part of the same image, referred to as the Model Sub-Image (MSI). Simple spatial features extracted from MSI are used as a model for handwriting strokes. This model captures the underlying characteristics of the writing strokes, and is invariant to the handwriting style or content. This model is then utilized to guide the binarization in the TSI. Another contribution is a new technique for the structural analysis of document images, which we call “Wavelet Partial Reconstruction” (WPR). The algorithm was tested on 4,200 cheque images and the results show significant improvement in binarization quality in comparison with other well-established algorithms. Received: October 10, 2001 / Accepted: May 7, 2002 This research was supported in part by NCR and NSERC's industrial postgraduate scholarship No. 239464. A simplified version of this paper has been presented at ICDAR 2001 [3].     

Efficient adaptive thresholding algorithm for in-homogeneous document background removal

Image binarization refers to convert gray-level images into binary ones, and many binarization algorithms have been developed. The related algorithms can be classified as either high quality computation or high speed performance. This paper presents an algorithm that ensures both benefits at the same time. The proposed algorithm intelligently segments input images into several different sized sub-images by using a Sobel like matrix. After which each sub-image will be classified into background set or foreground set according to it’s feature. Finally the foreground set sub-images will be binarized by Otsu’s method independently. Experimental results reveal that our algorithm provides the appropriate quality with the medium speed.     

Unsupervised measures for parameter selection of binarization algorithms

In this paper, we propose a mechanism for systematic comparison of the efficacy of unsupervised evaluation methods for parameter selection of binarization algorithms in optical character recognition (OCR). We also analyze these measures statistically and ascertain whether a measure is suitable or not to assess a binarization method. The comparison process is streamlined in several steps. Given an unsupervised measure and a binarization algorithm we: (i) find the best parameter combination for the algorithm in terms of the measure, (ii) use the best binarization of an image on an OCR, and (iii) evaluate the accuracy of the characters detected. We also propose a new unsupervised measure and a statistical test to compare measures based on an intuitive triad of possible results: better, worse or comparable performance. The comparison method and statistical tests can be easily generalized for new measures, binarization algorithms and even other accuracy-driven tasks in image processing. Finally, we perform an extensive comparison of several well known measures, binarization algorithms and OCRs, and use it to show the strengths of the WV measure.     

基于小波包分解的数字水印算法

从小波包域水印算法的现状出发,提出了一种小波包域的图像水印算法,采用较大灰度级图像作为水印,实现了一种小波包域灰度图像水印算法,该算法在水印类型和信息量方面是一个较大的突破,对比实验结果表明,在大多数鲁棒性能攻击测试中,与传统小波包域水印算法相比,该算法提取水印的归一化相关系数NC值更大,水印性能更优越.实验结果证明了...     

改进二值化算法在QR码识中的应用

研究了QR码图像二值化技术,针对光照不均的QR图像提出了一种改进的基于背景灰度估计的校正算法.该算法首先根据原图像大小进行分块处理,在此基础上利用改进的灰度估计公式计算分块的灰度值,然后利用双线性内插法设置区块灰度值为估计值,最后用原图像减去估计背景图像得到校正图像,以此克服了光照不均对二值化阈值选取的影响.实验证明,...     

基于阈值及像素聚类的分割算法性能比较

图像分割是图像处理到分析的关键步骤,阈值分割方法因其计算简单而被广泛应用,聚类算法也因其准确性成为图像分割领域中一类极其重要的算法。选取几种经典阈值分割算法和几种聚类算法对几幅毫米波图像进行分割实验,并引入错分类误差、均匀测度、区域间灰度对比度作为算法测评标准,比较了各种算法对毫米波图像的分割性能。     

一种改进的文本图像二值化算法

分析了常用的局部阈值法-Bemsen算法中存在的问题，对此算法进行了有效的改进，提出了一种改进的文本图像二值化算法。由于改进后的算法考虑了图像的整体特征，并采用快速算法，因此具有更广泛的适应性和实用价值。实验结果表明了此算法的有效性。     

结合Canny算子的图像二值化

对经典的二值化方法Ostu算法和Bernsen算法中存在的缺点进行了分析,提出一种结合Canny算子的图像二值化方法,该方法综合考虑了边缘信息和灰度信息,通过边缘附近种子点在高阈值二值化图像中的填充和低阈值图像对它的修补而得到二值化结果图像,较好地解决了经典二值化方法中存在的抗噪能力差、边缘粗糙、伪影现象等缺点,实验结果证明,该方法能够较好地解决低对比度图像和目标像素灰度不均匀图像的二值化问题。     

图像分割技术在小麦干旱监测系统中的应用

本文采用数字图像处理技术,以小麦群体绿色面积和绿色叶面积指标信息的获取为例,结合干旱胁迫状态下小麦叶片干枯的性状特征,利用形态学的边缘检测算法和自适应选取阀值的二值化算法对采集到的小麦叶片图像和小麦田间图像进行图像分割和特征提取,计算绿色叶面积的大小,并将绿色麦田区域和黄色土壤地面区域进行分离,提取出特征区域面积大小,计算小麦农田绿色植被覆盖率,从而为自动估算干旱胁迫对小麦群体的危害程度提供定量分析的依据,实验表明,利用图像分割技术对小麦群体特征图像识别是可行的。     

Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization,Mahalanobis distance and post-segmentation correction

In this paper, we propose an improvement method for image segmentation using the fuzzy c-means clustering algorithm (FCM). This algorithm is widely experimented in the field of image segmentation with very successful results. In this work, we suggest further improving these results by acting at three different levels. The first is related to the fuzzy c-means algorithm itself by improving the initialization step using a metaheuristic optimization. The second level is concerned with the integration of the spatial gray-level information of the image in the clustering segmentation process and the use of Mahalanobis distance to reduce the influence of the geometrical shape of the different classes. The final level corresponds to refining the segmentation results by correcting the errors of clustering by reallocating the potentially misclassified pixels. The proposed method, named improved spatial fuzzy c-means IFCMS, was evaluated on several test images including both synthetic images and simulated brain MRI images from the McConnell Brain Imaging Center (BrainWeb) database. This method is compared to the most used FCM-based algorithms of the literature. The results demonstrate the efficiency of the ideas presented.