基于Android平台的视频字符实时识别研究

针对移动终端处理能力低、内存小等影响系统效率的问题,提出了三阶段视频字符实时识别方法：视频采集及图像预处理、字符区域定位和字符识别。对于字符区域定位,提出了基于感兴趣区域（ROI,Region of Interesting）运动检测的相似帧过滤算法,并通过数学形态学与连通区域相结合的方法进行字符定位;对于字符识别,提出了基于误差阈值筛选的多模板字符识别算法,保证较高识别率。算法均采用NDK开发框架实现。实验结果表明,该方法在每个阶段都提高了处理效率,达到了对视频字符实时识别的效果。     

光学字符识别技术与图书馆信息资源的数字化

光学字符识别技术是自动识别技术中针对印刷体字符的一种识别技术,它可以对海量传统信息资源进行加工,存储,检索和利用,是实现图书馆信息资源数字化的先进技术和重要手段。在对传统图书馆信息资源数字化加工过程中应用光学字符识别技术,可以准确、快捷、高效地实现信息资源数字化,加快数字图书馆的建设步伐。     

Fast 3D video stabilization using ROI-based warping

With the rapid development of portable digital video equipment, such as camcorders, digital cameras and smart phones, video stabilization techniques for camera de-shaking are strongly required. The cutting-edge video stabilization techniques provide outstanding visual quality by utilizing 3D motion, while early video stabilization is based on 2D motion only. Recently, a content-preserving warping algorithm has been acknowledged as state-of-the-art thanks to its superior stabilization performance. However, the huge computational cost of this technique is a serious burden in spite of its excellent performance. Thus, we propose a fast video stabilization algorithm that provides significantly reduced computational complexity over the state-of-the-art with the same stabilization performance. First, we estimate the 3D information of the feature points in each input frame and define the region of interest (ROI) based on the estimated 3D information. Next, if the number of feature points in the ROI is sufficient, we apply the proposed ROI-based pre-warping and content-preserving warping sequentially to the input frame. Otherwise, conventional full-frame warping is applied. From intensive simulation results, we find that the proposed algorithm reduces computational complexity to 14% of that of the state-of-the-art method, while keeping almost equivalent stabilization performance.     

基于多重卷积神经网络的大模式联机手写文字识别

联机手写识别在日常生产生活中有着广阔的应用,模式识别也一直把其作为研究的重点。传统的识别方法是利用普通卷积神经网络技术,该方法在对小规模字符集联机手写文字识别时有着较高识别率,总体性能高,但在对大规模字符集识别时,识别率则大大降低。提出一种基于多重卷积神经网络的识别方法,旨在克服以往方法对大规模字符集识别时识别效率不高的问题,提高大规模字符集联机手写文字的识别率。系统使用随机对角Levenberg-Marquardt方法来优化训练,通过使用UNIPEN训练集测试该方法识别准确率可达89%,是一个有良好前景的联机手写识别方法。     

CSM neural network for degraded printed character optical recognition

In pattern recognition applications, the classification power of a system can be improved by combining several classifiers. Obviously performance of the system cannot be improved if the individual classifiers make all the same mistakes, thus it is important to use different features and different structures in the individual classifiers. In this context, we propose a two subnets neural network called CSM net. The first subnet, or similarity layer, is operating as a similarity measure neural network; it is based on the complementary similarity measure method (CSM). The second subnet is a competitive neural network (CNN) based on the winner takes all algorithm (WTA) that is used for the classification. In the proposed neural architecture, the statistical CSM method is analyzed, and implemented in the form of a feed forward neural network, it is named “similarity measure neural network” (SMNN). We show that the resulting SMNN synaptic weights are modified versions of the model patterns used in the training set, and that they can be considered as a memory network. We introduce a relative distance data calculated from the SMNN output, and we use it as a quality measurement tool of the degraded characters, what makes the SMNN classifier very powerful, and very well-suited for features rejections. This relative distance is used by the SMNN and compared to a first rejection threshold to accept, or reject, the incoming characters. In order to guarantee a higher recognition and reliability rates for the cascaded method, the SMNN is combined with a second subnet based on the WTA for classification using a second specific rejection threshold. These two submits combination (CSM net) boost the performance of the SMNN classifier. This is resulting in a robust multiple classifiers that can be used for setting the entire rejection threshold. The experimental results that we introduce are related to the proposed method, but the tests are introduced with various impulse noise levels, as well as the tests with broken and manually corrupted characters, and characters with various levels of additive Gaussian noise. The experiments show the effective ability of the model to yield relevant and robust recognition on poor quality printed checks, and show that the CSM net outperforms the previous works, both in efficiency and accuracy.     

基于ARM11的视频采集系统及二维码识别

随着信息技术的发展,越来越多的领域应用ARM作为嵌入式系统的核心。因此,深入研究ARM核心以及基于ARM核心的应用使其更好的符合实际需要,便显得尤为重要。本文通过对ARM核心以及嵌入式Linux的研究,利用QT编程,实现了基于三星S3C6410处理器的视频采集以及二维码识别。文章重点描述了嵌入式Linux的移植、视频采集以及二维码识别等应用程序的流程。     

基于MQDF的车牌字符识别

文中提出了一种新的基于MQDF的车牌字符识别算法,该算法在QDF的基础上进行K-L变换,并且用常量代替小的特征值改善计算速度和分类的正确率。该方法基于统计模型和中心极限定理,便于设计和实现,广泛应用于手写体识别,具有很好的鲁棒性和较高的识别准确率。用2142幅白天、晚上的蓝牌、黄牌车牌图像做实验,实验结果表明,对于数字、字母、汉字字符,平均识别率达到98%以上,具有较好的应用前景。     

Depth sensor assisted real-time gesture recognition for interactive presentation

In this paper, we present a gesture recognition approach to enable real-time manipulating projection content through detecting and recognizing speakers gestures from the depth maps captured by a depth sensor. To overcome the limited measurement accuracy of depth sensor, a robust background subtraction method is proposed for effective human body segmentation and a distance map is adopted to detect human hands. Potential Active Region (PAR) is utilized to ensure the generation of valid hand trajectory to avoid extra computational cost on the recognition of meaningless gestures and three different detection modes are designed for complexity reduction. The detected hand trajectory is temporally segmented into a series of movements, which are represented as Motion History Images. A set-based soft discriminative model is proposed to recognize gestures from these movements. The proposed approach is evaluated on our dataset and performs efficiently and robustly with 90% accuracy.     

Feature extraction circuit for optical character recognition

A feature set for the encoding of optical images of handwritten characters is presented. It allows good recognition rates when coupled with a neural network classifier and can be easily implemented in analogue CMOS VLSI technology. A circuit for the extraction of such features is described     

实时性MPEG视频源的图片组内平滑算法

本文对实时性的ＭＰＵＥＧ视频源进行了平滑研究,通过分析ＭＰＥＧ视频编码的特点和实时性ＶＢＲ视频对延迟的严格要求,提出实时性ＭＰＥＧ视频在图片组（ＧＯＰ）内平滑已接近最佳平滑的观点,并推导了图片组内平滑的上限速率和下限速率公式,设计了选择最佳速率的算法,最后使用两组代表不同应用的ＭＰＥＧ视频样本数据进行了仿真实验。实验表明：图片组内平滑算法与文献上其它算法相比,在理论上可取得几乎相同的理想平滑效果,     

视频字符叠加技术的研究及应用

介绍了字符建模方法和视频同步信号提取方法,并在分析视频字符叠加技术原理的基础上提出视频与字符叠加的三种电路设计方案,给出采用uPD6453芯片设计的电路原理框图。     

Joint on-the-fly network coding/video quality adaptation for real-time delivery

This paper introduces a redundancy adaptation algorithm based on an on-the-fly erasure network coding scheme named Tetrys in the context of real-time video transmission. The algorithm exploits the relationship between the redundancy ratio used by Tetrys and the gain or loss in encoding bit rate from changing a video quality parameter called the Quantization Parameter (QP). Our evaluations show that with equal or less bandwidth occupation, the video protected by Tetrys with redundancy adaptation algorithm obtains a PSNR gain up to or more than 4 dB compared to the video without Tetrys protection. We demonstrate that the Tetrys redundancy adaptation algorithm performs well with the variations of both loss pattern and delay induced by the networks. We also show that Tetrys with the redundancy adaptation algorithm outperforms traditional block-based FEC codes with and without redundancy adaptation.     

Effective edge node configuration for video transport over Optical Burst Switched networks

This paper considers digital video transport over Optical Burst Switched networks where burst losses cause data loss from one or more adjacent video frames. Analytical approximations for the frame losses and video playback interruptions are derived and validated using simulations. Both parameters require a very limited and static amount of data about the video on the user side and some quality of service metrics about the network to quantify the quality of the received video. The results take into account the strong dependency in the video traffic structure due to the coding mechanisms. The critical effect of video coding parameters is also revealed. The paper also presents a Traffic Engineering procedure to select the best parameters for the edge node and the video codec to meet a given video quality level on the user side.     

基于矩和Gabor变换的手写体汉字识别方法

特征提取是手写体汉字识别的一个研究难点。本文提出了一种基于特征融合的新提取方法,即将Gabor变换和正交矩变换结合起来,用正交Zernike矩提取全局特征,用Gabor变换提取局部特征,然后使用主成分分析的方法进行特征压缩。由此得到的特征向量能从整体上和局部上反映汉字的特征。实验证明该方法是行之有效的。     

基于弹性网格特征和神经网络的手写文字识别

提出一种有效的文字特征提取方法,在传统弹性网格基础上,采取与对角弹性网格相结合的方法进行特征提取,然后通过改进的BP神经网络进行文字识别.该方法集合了弹性网格特征和神经网络的优势,可有效提高手写文字的识别率、识别速度以及识别系统的泛化能力.实践证明,该方法用于文字识别准确性较高.     

分数傅里叶变换的定标性

提供了分数傅时叶变换定标性的数学表达和物理解释。计算机模拟结果验证了可靠与可行。     

基于文字信息的碎纸片拼接复原算法

基于不同碎纸片的文字特征进行分析,在合理假设下开发了多种算法,这些算法综合了多向递推、行列间距检测、字符模式识别与必要时的人工干预等多种手段,成功解决了碎纸片的拼接复原问题。同时,多种算法相互结合也有效地降低了错误拼接的概率,提高了复原文件方案的稳定性。拼接完成后的试验结果表明该方案的实际拼接效果非常理想。     

一种IC卡喷码实时检测系统

介绍了一种IC卡喷码实时检测系统。分析了系统原理、卡片的检测处理、粘连字符分割算法及识别方法。提出用自适应浮动模板法识别IC卡的码号，二次比对法对识别结果进一步处理。系统实现了IC卡的实时检测。实验表明检测速度为3张／秒，识别精度为99．9％。     

基于权系数标识符矩阵的车牌字符识别方法

为了提高对车牌字符的准确识别能力,提出了一种基于权系数标识符矩阵的模板匹配车牌字符识别方法。具体方法是在进行字符识别前为每一个车牌字符制定一个标准化的模板,再将每一个模板字符的像素依据像素区域、像素边缘区域和非像素及非像素边缘区域等标记成不同的区域,并依此为基准生成一个模板矩阵。根据车牌字符闭合区域个数及字符二值图像中间行、中间列黑白跳变次数,可将字符分为10类。进行字符识别时,首先判定待识别字符属于哪一类,然后与所在类的每一个字符的标准模板进行匹配,统计待识别字符落在每一个标准模板矩阵的不同区域的像素数,并根据不同区域的不同权值计算相似度值,相似度值最大的即为识别结果。该方法采用两级分类法对车牌字符图像进行分类,再采用基于权系数标识符矩阵的模板匹配法对车牌字符进行识别。实验结果表明,该方法提高了识别结果的准确度,对于存在字符断裂以及形状相似而容易混淆的字符有较好的识别效果。     

视频信号的实时小波压缩芯片·实用电路·ADV601及其应用

文章介绍了一种基于小波理论的实时视频压缩芯片ＡＤＶ６０１，它是世界上第一块实时视频小波压缩芯片。有关ＡＤＶ６０１的使用在文中也有介绍。