第三章客户终端中文输入法处理

本课件对于理解.NET平台特征和使用C#语言语法面向对象设计在C#中的实现和表达、 基本本的WinForms桌面客户端程序设计有较好地促进作用-The courseware for understanding. NET platform using C# language features and syntax of object-oriented design in C# in the realization and expression of the basic texts WinForms desktop client program designed to better promote the role of the

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1． 本文档只包括PowerDesigner部分内容（RQM,Report,CDM,PDM），内容不够全面。 2． 内容尽量简略，一些相同或类似操作过程尽量不再重复。 3． 部分术语参考了飞思科技产品研发中心监制电子工业出版社的《PowerDesigner数据库系统分析设计与应用》。 4． 暂时没有包含OOM,XML,BPM,ILM等模型内容， -1. PowerDesigner this document covers only part of the contents (RQM, Report, CDM, PDM), the content is not comprehensive enough. 2. Briefly the contents of as much as possible, some of the same or similar operation will not repeat as much as possible. 3. Fly into some of the terminology Synopsys R & D Center Products Electronics Industry Press producer of " PowerDesigner database design and application of systems analysis." 4. Does not contain the time being OOM, XML, BPM, ILM model, such as content,

这个小程序师以顺序栈来模拟停车场，以链队列来模拟便道，实现的c语言经典算法。-This small program division in order to stack the order to simulate the car park to chain queue to simulate the road, the realization of the classical algorithm c language.

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详细介绍了计算机视觉系统的开发与应用

本文完成了对唇动身份识别技术几个基本问题的理论研究,并对整个系统加以实现.作为本文研究的实验基础,我们建立了唇动方式身份识别数据库(HITLUDB), 该库目前包含30个说话人每人20个汉语词的音视频语料.数据库的扩充与完善工作仍在不断的进行之中.在嘴唇检测方面, 我们对自适应色度过滤模型进行改进,提高了算法的鲁棒性,完成了对嘴唇的精确定位.结合DCT变换与K-L变换的各自特点, 我们提出了特征提取算法,使用较少维数的特征完成了对嘴唇区域主要信息的刻画.由于唇动信息同时包含了生理特征与行为特征, 我们使用静念动念混合建模的方式,完成了对说话人唇动个性特点的精确描述.在HMM训练时,我们提出了特征的归一化处理方法,提高了HMM在实际应用中的性能. 最后,我们分别对身份辨认系统与身份确认系统的基本理论进行了叙述,并完成了系统的实践工作. 关　 键　 词：身份识别 唇动 特征提取 隐马尔可夫模型 K-L变换 -paper completed the lip movements identification technology several basic issues of theoretical study, system as a whole be achieved. As this paper, the experimental basis, We have established a dynamic manner lip identification database (HITLUDB) The library currently contains 30 words each of 20 Chinese words, sound and video corpus. and the expansion of the database is still perfect keep going on. Detection of the lips, we adaptive color filter model improvements, improve the robustness of the algorithm, completed a pair of lips the precise positioning. DCT combined with

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