语句拼音-汉字转换的智能处理机制分析

语句拼音- 汉字转换是中文信息处理研究的一个重要方面,是键盘汉字输入和语音输入的核心技术,其主要特征是对动态输入的拼音串进行词法分析,给出所有可能的汉语句子,然后对这些汉语句子根据上下文环境进行句法分析和语义分析,动态调整句子中的字词,输出最佳结果。近年来,语句拼音- 汉字转换系统大量应用了人工智能技术和机器翻译的理论,以期提高系统转换的准确率和增强系统的智能处理功能。本文分析了语句拼音- 汉字转换系统所采用的核心技术,即知识支持、自动分词和动态调整等,讨论了语句拼音- 汉字转换的处理方法和过程,知识库的组成结构,用于拼音串自动分词的算法和实现,音字转换中动态调整的概率模型等,本文还分析了现有语句拼音- 汉字转换系统在拼音串自动分词和音字转换的动态调整中发生错误的原因,并提出了改进方法。     

STRUCTURED REPRESENTATION AND AUTOMATIC INDEXING OF MOVIE INFORMATION CONTENT

The development of video applications for digital multimedia has highlighted the need for indexing tools, enabling the access to meaningful segments of video. The high cost of manual indexing creates a demand for the development of automatic algorithms, able to extract such indices with little intervention. In this paper we present new editing model–based algorithms that automatically extract low–level features in a movie: camera shots and camera motion. Rules of film making are used to derive higher-level elements, such as shot-reverse shot sequences. The algorithms have been tested on 20 h of movies and comparison with techniques in the literature is provided.     

基于Word文档的数据交换策略及其实现

论文分析了当前管理信息系统数据采集与传统的数据上报方式之间存在的问题，提出了一种基于Word文档实现数据采集和上报的解决方案，对基于Word文档的数据交换策略进行了研究，实现了基于Word文档的数据自动采集和数据库系统不确定报表的自动生成，结合实际MIS系统开发，验证了该解决方案的实用性和有效性。     

WORD使用中几种常见故障的解决方法

介绍Word使用中几种常见故障的解决方法.     

Microsoft Visio 2003在绘制Word文档电路图中的应用

本文介绍了用绘图软件Microsoft Visio 2003绘制Word文档用电路图形的方法。应用这种绘图方法所绘制的电路图形在粘贴到Word文档中非常标准美观，且易学易用。因此，它是广大从事电子专业教学人员的绘图工具。     

Word prediction using a clustered optimal binary search tree

Word prediction methodologies depend heavily on the statistical approach that uses the unigram, bigram, and the trigram of words. However, the construction of the N-gram model requires a very large size of memory, which is beyond the capability of many existing computers. Beside this, the approximation reduces the accuracy of word prediction. In this paper, we suggest to use a cluster of computers to build an Optimal Binary Search Tree (OBST) that will be used for the statistical approach in word prediction. The OBST will contain extra links so that the bigram and the trigram of the language will be presented. In addition, we suggest the incorporation of other enhancements to achieve optimal performance of word prediction. Our experimental results showed that the suggested approach improves the keystroke saving.     

工艺文件计算机管理的一些技巧

介绍了利用Word及AutoCAD等通用软件管理各种工艺文件的方法和心得。     

Statistical analysis of manual segmentations of structures in medical images

The problem of extracting anatomical structures from medical images is both very important and difficult. In this paper we are motivated by a new paradigm in medical image segmentation, termed Citizen Science, which involves a volunteer effort from multiple, possibly non-expert, human participants. These contributors observe 2D images and generate their estimates of anatomical boundaries in the form of planar closed curves. The challenge, of course, is to combine these different estimates in a coherent fashion and to develop an overall estimate of the underlying structure. Treating these curves as random samples, we use statistical shape theory to generate joint inferences and analyze this data generated by the citizen scientists. The specific goals in this analysis are: (1) to find a robust estimate of the representative curve that provides an overall segmentation, (2) to quantify the level of agreement between segmentations, both globally (full contours) and locally (parts of contours), and (3) to automatically detect outliers and help reduce their influence in the estimation. We demonstrate these ideas using a number of artificial examples and real applications in medical imaging, and summarize their potential use in future scenarios.     

A label field fusion model with a variation of information estimator for image segmentation

This paper proposes a new and reliable segmentation approach based on a fusion framework for combining multiple region-based segmentation maps (with any number of regions) to provide a final improved (i.e., accurate and consistent) segmentation result. The core of this new combination model is based on a consensus (cost) function derived from the recent information Theory based variation of information criterion, proposed by Meila, and allowing to quantify the amount of information that is lost or gained in changing from one clustering to another. In this case, the resulting consensus energy-based segmentation fusion model can be efficiently optimized by exploiting an iterative steepest local energy descent strategy combined with a connectivity constraint. This new framework of segmentation combination, relying on the fusion of inaccurate, quickly and roughly calculated, spatial clustering results, emerges as an appealing alternative to the use of complex segmentation models existing nowadays. Experiments on the Berkeley Segmentation Dataset show that the proposed fusion framework compares favorably to previous techniques in terms of reliability scores.     

Segmentation of neuronal nuclei based on clump splitting and a two-step binarization of images

In this paper we present an algorithm to segment the nuclei of neuronal cells in confocal microscopy images, a key technical problem in many experimental studies in the field of neuroscience. We describe the whole procedure, from the original images to the segmented individual nuclei, paying particular attention to the binarization of the images, which is not straightforward due to the technical difficulties related to the visualization of nuclei as individual objects and incomplete and irregular staining. We have focused on the division of clusters of nuclei that appear frequently in these images. Thus we have developed a clump-splitting algorithm to separate touching or overlapping nuclei allowing us to accurate account for both the number and size of the nuclei. The results presented in the paper show that the proposed algorithm performs well on different sets of images from different layers of the cerebral cortex.