Word recognition: From letters to meaning.

Reviews the book, Basic Processes in Reading Visual Word Recognition by Derek Besner and Glyn W. Humphreys (see record 1990-99017-000). While there has been an increase in the amount of work on many different aspects of reading, as Besner and Humphreys point out in the overview to their book, the vast majority of the research on the topic of reading in the past twenty years has been concerned with the processes involved in word recognition. For this reason, Besner and Humphreys have attempted to bring together studies on topics which are both relevant to current debates in the field of word recognition, and which are likely to be important for future developments in the field. They have compiled an edited volume consisting of their overview and eight additional chapters. The editors have attempted to span the continuum of processes involved in word recognition and thus have included chapters which cover topics ranging from the visual analysis of words, to those on the influence of semantic factors on word recognition. The authors of these chapters comprise an impressive list of researchers in the field of word recognition, with the majority of chapters being authored by leading researchers on the topic. Given the stature of the authors and the range of topics covered, in theory this volume should provide a very thorough overview of current theory and research on reading. There is no question that each of the chapters is interesting and important in its own right. However, in practice the volume as a whole fell somewhat short of my expectations. The different tacts taken by different authors has resulted in a very uneven coverage of the current debates in the field. Notwithstanding these criticisms, I am sure that the majority of researchers in this field will consider this volume to be an important contribution. The book would provide a very useful addition to graduate courses in cognitive sciences and as a supplemental text for an undergraduate course on the psychology of reading. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Learning-based dynamic coupling of discrete and continuous trackers

We present a data-driven dynamic coupling between discrete and continuous methods for tracking objects of high dofs, which overcomes the limitations of previous techniques. In our approach, two trackers work in parallel, and the coupling between them is based on the tracking error. We use a model-based continuous method to achieve accurate results and, in cases of failure, we re-initialize the model using our discrete tracker. This method maintains the accuracy of a more tightly coupled system, while increasing its efficiency. At any given frame, our discrete tracker uses the current and several previous frames to search into a database for the best matching solution. For improved robustness, object configuration sequences, rather than single configurations, are stored in the database. We apply our framework to the problem of 3D hand tracking from image sequences and the discrimination between fingerspelling and continuous signs in American Sign Language.     

大庆油田杏1-3区三次加密试验区剩余油分布研究

通过对试验区近年完钻的检查井各单砂层水淹状况及其影响因素的分析。利用神经网络模式识别方法建立起单层剩余油识别模型，在此基础上对三次加密试验区各小层剩余油分布进行研究，总结出各类油层剩余油分布特征及成因类型，并分析了试验区剩余油潜力分布，从而为三次加密布井及射孔方案编制提供了可靠的依据。     

Two-tier architecture for unconstrained handwritten character recognition

In this paper, we propose an approach that combines the unsupervised and supervised learning techniques for unconstrained handwritten numeral recognition. This approach uses the Kohonen self-organizing neural network for data classification in the first stage and the learning vector quantization (LVQ) model in the second stage to improve classification accuracy. The combined architecture performs better than the Kohonen self-organizing map alone. In the proposed approach, the collection of centroids at different phases of training plays a vital role in the performance of the recognition system. Four experiments have been conducted and experimental results show that the collection of centroids in the middle of the training gives high performance in terms of speed and accuracy. The systems developed also resolve the confusion between handwritten numerals.     

检测骨架图形特征点的新方法

端点、折点、歧点和交点是骨架图形的重要特点是，提出用快速查表法检测侯选特征点，再进行侯选特征点聚集，用局部结构分析，去除假特征点，判别真特征点的类型，并对折点给以大致的定量描述。实验结果表明，该方法能快速有效地找出骨架图形的特征点，在目标识别、字符识别等应用中起着关键作用。     

全双极WTA模型的改进及其实现

提出了一种相似度的新定义，使双极ＷＡＴ模型的实现更加简洁明了。增加一个阈值使系统能判别非存储模式或因其信息量过低而无法鉴定的输入模式，阈值的大小与要求的容错能力有关。双极相似度及阈值通过光学方法实现。阈值的光学实现使系统仍具有照明不变性。通过电路实现取及ＷＴＡ网络的迭代运算。     

非AWGN环境下调制类型自动识别

在非AWGN环境下，针对CW，AM，FM，OOK，PSK，QPSK，16QAM，FSK，MSK，SSB十种混合调制信号，对调制类型特征提取方法和自动识别算法进行了分析、综合、设计和计算机仿真。提出了梯层电平分析方法、频率瞬时信号分析、剔除载波的归一化能量分析、平方后信号的频谱分析、四次方后的信号频谱分析等方法。当SNR=10dB时，计算机仿真结果是正确识别率不低于95％，自动识别的平均处理时间为不超过10s。     

基于切削振动相关性识别刀具磨损状态的模型与实验

本文提出基于切削振动相关性识别刀具磨损状态的研究思想,建立了车削中刀杆双向横振动系统及其单输入双输出系统的数学模型。理论和实验均表明,刀杆双向横振动之加速度响应在固有基频处的相干函数与刀具磨损状态之间有很好的规律性,它反映了两者之间的本质机理,并提供了识别刀具磨损状态的新途径。     

基于宽带毫米波工作体制的雷达目标识别方法探讨

对基于宽带毫米波工作体制的雷达目标特征获取和识别方法进行了综合分析和探讨.表明:在毫米波照射下,雷达目标可模型化为由多个散射中心组成的扩展目标;只要发射信号有足够的带宽,就可以从回波信号中获得各散射中心的空间分布、类型等特征;仿真结果说明,这种方法对高频区复杂目标的识别是有效的.