QR码图像几何校正算法的研究

用摄像机拍摄QR码图像时,由于拍摄角度的偏差,可能造成所采集到的QR码图像产生几何失真的情况,给QR码的识别带来了困难。对拍摄到的QR码图像进行预处理,把采集到的光照不均匀的QR码图像采用局部阈值法和数学形态学的方法进行二值化。通过Harris角点检测算法和凸包算法相结合找到QR码的轮廓以及轮廓上的点,再利用该角点检测算法找到QR码的角点,最后使用透视变化法对畸变图像进行恢复。实验结果证明,该方法有效解决了QR码的几何失真问题。     

一种快速响应码的图像预处理方法

QR码(快速响应矩阵码)作为一种便捷、安全的信息载体,已经在各行业得到了广泛的应用.首先改进了传统算法,将光照不均下的图像进行二值化;接着利用双线性变换与几何变换结合,矫正图像在采集过程中的几何失真,并简化了运算;最后使用二值形态学插值修补.实验证明,该方法简单有效,可用于提高QR码识别率.     

基于自适应匹配的QR码取样方法

通常由相机获取的QR码图像都带有一些失真,所以在译码前需要对获取的QR码图像进行识别以得到标准规格的QR码。针对QR码识别中的失真和校正进行了分析研究,解决了某些QR码经过倾斜校正和几何校正后仍存在一些无法避免的失真而无法被传统方法准确取样的问题,提出了一种自适应匹配取样法,根据相邻行（列）像素的匹配度准确获取QR码的模块有效取样区域。实验证明该方法稳定性好,能够快速准确地对QR码进行取样。     

快速响应码的识别和解码

研究了快速响应码(QR Code)的识别方法和解码方法。在识别过程中采用Hough变换检测QR码的倾斜角度,通过几何计算纠正失真,并利用双线性插值算法将QR码旋转至水平,然后用Sobel边缘检测算子以及投影算法计算出QR码单元模块的边界。提供的方法能够快速、准确地识别出QR码,有效地抑制了拍摄QR码时产生的失真、噪声、倾斜等因素对QR码识读过程中的影响,提高了QR码的识别率。同时研究给出了QR码进行RS纠错译码过程中应注意的有关在有限域中的运算、如何求解伴随多项式、确定错误位置值和计算错误值的问题。     

基于符号特征的QR码识别算法

在传统QR码识别算法基础上,提出一种利用QR码自身的符号特征进行识别定位的改进算法。对采集到的图像进行二值化处理,由符号特征定位QR码在采集图像中的位置,通过改进的双线性变换校正在采集过程中引起的图像几何形变,并通过逐模块垂直投影提取条码信息。实验结果表明,该算法具有识别时间短、识别率高等特点。     

基于不变矩和神经网络的交通标志识别方法研究

在交通标志实时识别过程中，由于参考图像与实测图像不是同时获取的，因此摄像机与被摄交通标志之间的位置难以保证完全相同。于是，所获取的参考交通标志图像与实测交通标志图像之间就可能产生几何失真。几何失真将对于图像识别的结果带来很大的影响。因此，需要寻找一种具有旋转和比例不变性的图像识别方法，以满足实际应用中的需要。针对上述问题，提出了一种基于不变矩和神经网络的交通标志识别算法。实验结果表明，所提出的识别算法具有很好的识别能力。     

DataMatrix变种码边界提取

本课题基于原有的DataMatrix编码,引入新的铁道线和L边界,极大方便便携摄像设备对图像的定位以及对特征点坐标的提取,并考虑在便携摄像设备像素不高,在拍摄环境下图像容易受到各种环境因素的影响,对该二维变种码特殊图像预处理。其次针对新的DataMatrix码的特征,通过图像特征提取得到变种码图像中包括变种码在内的各连通区域的长度与宽度,利用边缘检测及铁道线查找方法,设计出一种提取铁道线边界各段线中点坐标的方法新的基于DataMatrix的二维条码变种码,更易于被解析、定位可以识别具有一定倾斜度的条码,具有较好鲁棒性,以及较大提高图像识别速度,具有相当的社会实用性。     

一种基于虚拟键盘图像坐标变换的几何失真校正方法

虚拟键盘图像采集过程中出现的几何失真情况会直接导致按键落点识别错误.针对这一问题,提出一种基于数字图像坐标变换的几何失真校正方法.该方法针对矩形虚拟键盘,用几何坐标变换的方法,分别对拍摄装置垂直俯拍时出现的倾斜失真情况和水平侧拍时出现的透视失真情况作了校正.同时提出对图像使用二分法提取有效校正区域,从而减少计算量、提高处理速度.经实践对比与分析,该方法在准确率与效率方面均体现了可行性.     

基于亚像素边缘检测的二维条码识别

提出了一种基于亚像素边缘检测的二维条码识别算法、能有效地解决边缘模糊对条码识别的影响。以PDF417条码为例研究了基于亚像素边缘检测的二维条码识别算法。首先定位条码位置并在条码中分割出单个码字符号图像。然后根据分割出来的单个码字符号图像讨论r基于亚像素边缘检测的识别算法。实验结果表明基于亚像素边缘检测的识别算法具有良好的性能，显著地提高了条码的识别率，满足了实际使用的要求。     

基于中点检测的二维条码识别

条码边缘模糊会导致其识别率下降，本文提出了一种基于中点检测的识别算法，能有效地解决边缘模糊对条码识别的影响．文中以PDF417条码为例研究了基于中点检测的二维条码识别算法．首先定位出图像上的条码，然后再在条码中分割出单个码字符号图像．文中最后根据分割出来的单个码字符号图像着重讨论了基于中点检测的识别算法．实验结果表明基于中点检测的识别算法具有良好的性能，显著地提高了条码的识别率，满足了实际使用的要求．     

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.     

Performance Estimation of Speech Recognition System Under Noise Conditions Using Objective Quality Measures and Artificial Voice

It is essential to ensure quality of service (QoS) when offering a speech recognition service for use in noisy environments. This means that the recognition performance in the target noise environment must be investigated. One approach is to estimate the recognition performance from a distortion value, which represents the difference between noisy speech and its original clean version. Previously, estimation methods using the segmental signal-to-noise ratio (SNRseg), the cepstral distance (CD), and the perceptual evaluation of speech quality (PESQ) have been proposed. However, their estimation accuracy has not been verified for the case when a noise reduction algorithm is adopted as a preprocessing stage in speech recognition. We, therefore, evaluated the effectiveness of these distortion measures by experiments using the AURORA-2J connected digit recognition task and four different noise reduction algorithms. The results showed that in each case the distortion measure correlates well with the word accuracy when the estimators used are optimized for each individual noise reduction algorithm. In addition, it was confirmed that when a single estimator, optimized for all the noise reduction algorithms, is used, the PESQ method gives a more accurate estimate than SNRseg and CD. Furthermore, we have proposed the use of artificial voice of several seconds duration instead of a large amount of real speech and confirmed that a relatively accurate estimate can be obtained by using the artificial voice.     

笔迹鉴别的字符予处理与匹配

笔迹鉴别多用匹配方法比较字并的书写风格, 而字符困像的预处理和归一化对匹配是昨常重要的本文介绍笔迹鉴别的字符图像预处理和一种形状匹配方法。预处理主要介绍二值图像的噪声消除和归一化方法。嗓声消除的方法是平滑、轮廓跟踪和填充为保持字符中的书写特征, 点阵的归一化是线性的, 但字符位五和尺度的确定昨常重要。本文给出了三种归一化方法四边定界法、重心对准法和单边定界法, 并在此基拙上用图像匹配方法进行书写人识别的实验。匹配方法是通过距离变换快速实现的。实验结果表明, 重心对·准归一化最适合于笔迹鉴别问题, 距离变换匹配得到的识别率也比较令人满意     

检测不规则图形的改进广义Hough变换

广义Hough变换作为一种检测不规则图形的有效方法,具有抗噪、不怕遮挡等优点。由于R表的计算对断点和变形非常敏感,影响了广义Hough变换抗断点和变形的特性。针对这一问题,提出了一种计算图形上每个点的法线方向的新方法。利用法线方向作为R表索引项,对点进行分类,使得广义Hough变换不仅具有抗噪、不怕遮挡的优点,还具有抗断点、受变形影响小的特点。实验结果证明,该方法在检测不规则图形时具有很好的抗干扰能力,并在鞋样设计CAD系统中有效解决了鞋样匹配的问题。     

基于TMS320DM642静脉识别的储物柜系统设计

在现有静脉识别算法的基础上,提出了一种基于TMS320 DM642的静脉识别控制储物柜的方法.该系统主要由电磁锁、电锁控制器和中央处理器组成.存储过程中采集静脉,生成的ID需要与储物柜号绑定,柜门打开;取出过程中,需要静脉验证,打开与其绑定的柜门并删除对应的静脉信息.在原有的基础上,静脉验证取代条形码,这种方法比条形码验证更安全,不易丢失.     

A Robust Viterbi Algorithm Against Impulsive Noise With Application to Speech Recognition

The Viterbi algorithm has been successfully applied to different pattern recognition and communication tasks. However, if some observations are corrupted by unknown impulsives noise which are not accounted for by the distortion measures, recognition performance can degrade significantly. In this paper, we propose a robust Viterbi algorithm to handle short impulsive noises with unknown characteristics by means of joint decoding and detection during the Viterbi search. To make the algorithm applicable to different noisy conditions with varying amounts of impulsive noise, we further proposed an approach to efficiently estimate the number of corruptions. We demonstrate the effectiveness of the proposed robust algorithms using spoken digit recognition experiments under two different impulsive noise environments. Under random Gaussian replacement noise, the proposed algorithm reduced digit error by more than 65%. Under the GSM network environment in which lost frames are replaced by interpolated neighboring frames, the robust algorithm reduced digit error by 20%. Furthermore, the proposed algorithm does not degrade performance when impulsive noise is not present.     

基于数学形态学的二维条码边缘检测算法

边缘模糊会导致二维条码识别率下降,提出了一种基于数学形态学边缘检测的二维条码识别算法,该算法最大的特点是用具有特定形态的结构元素去度量和提取图像中的对应形状以达到对图像分析和识别的目的,从而有效地降低边缘模糊对条码识别的影响。选取PDF417二维条码为应用对象,采用基于数学形态学对二维条码的识别算法选择合适的结构元素。实验结果表明与传统的几种边缘检测算法相比,基于数学形态学对二维条码的识别算法能够更有效地识别条码边界,显著地提高了条码的识别率。     

Recognition of consonant-vowel (CV) units under background noise using combined temporal and spectral preprocessing

This paper proposes hybrid classification models and preprocessing methods for enhancing the consonant-vowel (CV) recognition in the presence of background noise. Background Noise is one of the major degradation in real-time environments which strongly effects the performance of speech recognition system. In this work, combined temporal and spectral processing (TSP) methods are explored for preprocessing to improve CV recognition performance. Proposed CV recognition method is carried out in two levels to reduce the similarity among large number of CV classes. In the first level vowel category of CV unit will be recognized, and in the second level consonant category will be recognized. At each level complementary evidences from hybrid models consisting of support vector machine (SVM) and hidden Markov models (HMM) are combined for enhancing the recognition performance. Performance of the proposed CV recognition system is evaluated on Telugu broadcast database for white and vehicle noise. The proposed preprocessing methods and hybrid classification models have improved the recognition performance compared to existed methods.