基于游程编码思想的条码定位与识别方法

在复杂背景中的定位条码是图像式条码识别系统中的一个关键步骤,在复杂的背景中快速、自动地检测出条形码是文章研究的主要内容。该文首先介绍了条型码的编码结构,然后提出了一种基于游程编码思想的条码定位与识别方法,最后通过相关实验结果验证了该算法的可行性和实用性。     

复杂图像背景下的一维条码定位研究

复杂背景中的条码自动定位在图像式条码识别系统中是一个关键步骤,如何在复杂的背景中快速、自动地检测出条形码是研究的主要内容。主要利用条码区域的边缘密度高、以及边缘方向单一特征检测条码区域,研究了复杂图像背景中条码定位的步骤并提出一种基于边缘梯度方向的边缘提取法,得到了较好的效果。     

复杂背景和非均匀光照环境下的条码自动定位和识别

提出了一种在复杂背景和非均匀光照环境下的条码自动定位识别算法,用于定位和识别实际应用中的条码图像.该算法首先将灰度图像分成若干个子区域,根据每个子区域的梯度特征和角点特征筛选出可能含有条码的子区域,对这些子区域采用连通区域算法进行合并和分析,定位出条码区域.由于传统的二值化方法对于非均匀光照环境下的条码图像处理效果不佳,提出一种改进的Bernsen二值化算法对条码区域进行二值化处理,有效地减小了光照不均对条码识别的影响.实验结果表明,该算法可以有效去除大量复杂背景,准确定位和识别任意角度的条码区域.特别在非均匀光照环境和含有大量文字等复杂背景的情况下,该方法具有明显优势.     

基于多分辨率的水表用户码定位与识别

本文对数码相机拍摄的水表图像中的用户码进行定位与识别,以获得用户编号信息.采用基于多分辨率的方法,并结合边缘检测、区域分析与hough变换,定位用户码区域,然后识别用户码中的信息.本文的方法实现了条码区域的快速定位,能有效识别复杂水表背景中的条码.     

基于SUSAN检测算子的二维条码定位方法

以二维条码Data Matrix在实际应用中的图像为例,采用一种基于图像最优阈值的SUSAN检测算子,将复杂背景图像中的二维条码图像区域检测出来。使用这种方法可以在最大程度上忽略复杂背景的干扰,实现二维条码的快速定位。     

基于梯度方向评估的条码定位方法研究

提出了一种快速定位条形码的方法,可用于任意倾斜角度、复杂背景的条码定位.通过对图像划分成一个个小的区域(像素块),并计算块内的图像像素平方梯度的平均值,通过统计分析图像块的内部的平方梯度方向的分布情况,来判断是否符合条码的条纹特征,从而得到一系列的疑似区域,并通过形态学操作,消除背景的干扰,从而得到条码区域的准确位置.在判断条码的过程中,结合了图像的边缘信息,使得检测结果更为准确.实验表明,算法在复杂背景下能够准确地将条码区域检测出来,对于复杂图像中包含多个条码的情况也能够准确地定位.     

基于多尺度分割的二维条码定位算法

为正确定位二维条码在图像中的位置,提出一种多尺度分割定位算法。在最初分割中,图像被分成若干分块。根据分块的黑白像素比、高频子带系数方差等特征值,将分块分割为条码和非条码区域。随着尺度的增加,利用多尺度分割模型对下一尺度的分割结果进行修正。以此类推,直至最精细尺度,得到条码在图像中的定位。实验结果表明该算法能较好地分割出图像的条码区域。特别在含有大量文字等复杂背景的情况下,该方法具有一定优势。     

复杂背景PDF417解码相关技术的研究

本文研究了PDF417二维条码解码的相关技术，涉及了条码在复杂背景下的准确定位，条码行数的准确确定，条码列数的准确的确定，以及每个码字的准确位置的确定。定义了一些数据结构来记录相关重要的信息。     

标刻在金属零件上的二维条码识别

直接零件标识(DPM)技术是实现产品标识的重要手段,二维条码技术是其关键技术之一。DPM标识是以金属作为背景用激光在其上面打上二维条码,与印刷品上的二维条码识读相比,基于金属背景下的二维条码识读更为复杂。改进了传统的识别方法,综合运用最大连通域提取、改进的霍夫变换定位边界、基于最大匹配度的网格化分和基于灰度图像的无损信息提取等方法来实现条码图像的粗定位、精定位、校正、条码分割和数据提取。实验表明,本方案对于磨损、轻度污染、畸变及光照不均的金属上的二维条码图像的识读具有较强的抗干扰性,并取得可靠的识读结果。     

基于形态学和梯度特征的二维条码检测定位

针对复杂背景下的二维条码定位问题,单一特征提取已很难满足其需要.为此提出一种将图像二值化,然后采用基于数学形态学和kirsch边缘检测的算法滤去图像背景,最后利用投影算法定位条码区域.实验结果表明,该方法简单、快速、准确,很好地满足了实际使用的需要.     

隐形多彩色条形码打印系统软件的设计与实现

介绍利用VB设计并实现的以稀土能量上转换材料为码元的一种隐形多彩色一维条码的打印系统。该多彩色条码的编码原理是当红外光源通过光学系统照射在上转换材料上时,稀土能量上转换材料会在紫外光、可见光、红外光等光谱频率范围内发光。任意被激发的荧光带都带着丰富的信息码,通过挖掘稀土材料上转换机制所潜在的巨大信息潜能,将被激发不同颜色的稀土材料作为条码中的条码元,利用数学组合方法,该彩色条码可得到远大于目前黑白条码的容量。文中对编码软件系统框架进行整体介绍,实现了条码字符的编码入库,条码字符的打印及条码模块属性设置等关键技术,并附有关键代码。通过对软件系统测试,效果良好。基于此打印软件的打印条码经印刷套印,得到以不同稀土能量上转换材料为码元的隐形条码,具有防伪能力强和信息容量高等特点,具有广泛的应用前景。     

二维条码图像反模糊算法研究

二维条码作为一种重要的自动识别技术,有着极其广泛的应用前景。有效地解决二维条码图像降质问题是其能够广泛应用的关键。实际应用中,图像模糊是常见的降质,给二维条码识别带来了困难。论文研究二维条码图像反模糊算法,首先应用傅里叶变化确定图像模糊类型;然后根据条码信号,获得模糊函数的参数;最后采用用基于迭代技术的算法复原条码图像。实验结果表明,论文设计的算法具有良好的性能和实时性。     

特殊条形码的识读

主要通过对卡片侧面有用信息的识读,克服因光照不均、摄像头分辨率有限等问题,所带来的条码分割以及识别的困难,利用图像处理中几何校正、双线性插值放大、去噪等算法,以及对条码的分割定位,从而判断出卡片正面的信息,很大程度上提高了条形码识别的准确率,实验证明该方法有效并准确地识别出了卡片的正面信息,达到了较高的识别率,具有一定的实用价值,为今后条码的识别开拓了一个新的方向。     

Automated barcode recognition for smart identification and inspection automation

Barcodes have been widely used in many industrial products for automatic identification in data collection and inventory control purposes. It is well known that in many stores laser bar-code readers are used at check-out counters. However, there is a major constraint when this tool is used. That is, unlike traditional camera-based picturing, the distance between the laser reader (sensor) and the target object is close to zero when the reader is applied. This may result in inconvenience in inspection automation because the human operator has to manipulate either the sensor or the objects. For the purpose of in-store or inspection automation, the human operator needs to be removed from the process, i.e. a robot with visual capability is required to play an important role in such a system. Moreover, an automated system is required to find, locate and decode barcodes on various document images even with low resolution compared with laser barcode readers (about 15,000 dots per inch) and can handle damaged bar codes. This paper proposes a smart barcode detection and recognition system (SBDR) based on fast hierarchical Hough transform (HHT). The back-propagation neural network (BPNN) is selected as a powerful tool to perform the recognition process. The paper presents an effective method to utilize the specific graphic features of barcodes for positioning and recognition purposes even in case of distorted barcodes. The first step the system has to perform is to locate the position and orientation of the barcode in the required material document image. Secondly, the proposed system has to segment the barcode. Finally, a trained back-propagation neural network is used to perform the barcode recognition task. Experiments have been conducted to corroborate the efficiency of the proposed method.     

Stabilization and extraction of 2D barcodes for camera phones

With the ubiquity of cellular phones, mobile applications with 2D barcodes have drawn a lot of attentions in recent years. When a user takes a barcode image with the camera in a mobile device, the captured image tends to be blurred due to camera shaking when the user presses the shutter. In addition, the captured image includes part of the complex background of the page with the barcode. In this paper, we point out that the above two issues, which have not been identified in previous works, deteriorate the accuracy of barcode recognition in the mobile computing. We then propose an efficient and effective algorithm to restore and extract 2D barcode from a complex background in a camera-shaken image. Compared with previous approaches, our algorithm outperforms in not only smaller running time but also higher accuracy of the barcode recognition in the mobile computing.     

Real-time automatic recognition of omnidirectional multiple barcodes and DSP implementation

Barcodes have been extensively adopted in daily life, such as in merchandise labels, inventory control, storage/retrieval systems and inspection. Computer-vision-based barcode recognition can definitely facilitate barcode reading, especially for multiple barcodes and free orientation and in complex scenarios. This work, presents an automatic barcode detection and recognition algorithm for multiple and rotation invariant barcode decoding. The proposed system comprises three stages. First, the barcode is extracted by coarse-to-fine segmentation in four steps: background small clutter reduction, candidate barcode segmentation, barcode verification and barcode rotation and regularization. To enhance the barcode region, thin and small background noise clusters are eliminated using Max–Min Differencing. The approach combines several image-processing schemes, namely Gaussian smoothing filtering, connected component analysis, orientation homogeneity, moment analysis and iterative thresholding. The second stage decodes the barcode by scanning multiple traversal lines, thus preventing decoding errors due to minor barcode defects. Finally, the proposed system is implemented and optimized on a DM6437 DSP EVM board. Experimental results indicate that the proposed approach can locate multiple and omnidirectional barcodes, even with a complex background and minor distortion. The recognition rates for 10,395 lottery barcodes and 388 merchandise barcodes are 99.74 and 90.7%, respectively. The proposed system is promising and has been successfully adopted in commercial applications of lottery reading and verification of winning numbers.