蒙古文编码向拉丁转写转换和分音节算法实现

在蒙古文单词拼写中有很多型同音异词,从字面上难以辨别和区分型同字符的差异,这对蒙古文信息处理方面都带来了一定的困难。但在蒙古文的文字信息处理过程中,解决型同音异词,确定其编码是一项重要研究内容。该文重点讨论如何实现蒙古文的拉丁转写和切分音节,来确定那些型同音异词中的型同字符的问题。     

彝文自动分词技术研究

该文介绍了彝文自动分词的技术.首先阐述了研究彝文自动分词的必要性和重要意义,然后介绍了彝文分词规范的原则及词表,讨论了彝文分词的算法,最后根据彝文的特性,设计了基于Java语言的彝文自动分词软件并得出了良好的分词结果.     

改进的规范割方法提取肉品图像中肌肉和脂肪

肉品图像中脂肪与肌肉的精确提取是无损检测的关键技术之一,针对这一问题,在规范割方法（Normalized cut,Ncut）的基础上,为了减少计算复杂度,提出了一种改进算法。首先,利用基于矩的阈值选择方法将肉品区域从背景中分割出来;其次,量化色彩等级,为肉品区域创建彩色直方图;最后,计算彩色直方图的相似度矩阵,并使用规范割做为谱聚类测度对直方图进行划分,按照直方图划分结果提取肌肉和脂肪。实验表明,和基于像素的谱聚类算法相比,改进算法不但降低了计算复杂度,且能有效提取肌肉与脂肪。     

面向网页结构特征的Hopfield 算法

针对目前互联网信息资源广泛、网页结构复杂、噪音信息较多的现状,主题爬虫获取有效信息过程中精确度低、耗费时间长等问题。结合经典的Hopfield算法,提出了针对网页结构特征进行分块的主题爬行改进算法,实验证明该改进算法在一定程度上能有效地解决目前信息获取过程中所面临的问题。     

基于自适应阈值的舌像分割方法

中医舌诊是目前医学领域的重要前沿课题之一,而舌体轮廓的正确分割是实现中医舌诊信息化的重要前提,目前主流的阈值分割方法对对比度较小的舌像仍不能实现很好的分割。为此提出了一种舌像的自适应阈值分割算法,该算法是以VC＋＋为开发平台,首先把图像分成多个子块,然后运用迭代的方法计算每个子块的最佳阈值,根据每个局部最佳阈值构成的阈值矩阵进行分割。实验结果表明,该算法对背景和目标分界不明显的舌像有很好的分割效果,对中医舌诊的继续发展有很强的现实意义。     

加权三维Otsu方法在图像分割中的应用*

从信息散度角度出发,提出一种新的三维Otsu加权方法,对分布不均匀的图像做细致分割。由于三维Otsu中三个维度在分割中所起的作用不同,可以通过调整不同维度的权因子,使不同的图像都能得到很好的分割效果。最后利用加权的特点,将三维Otsu分解为三个一维Otsu,在加快运算速度,降低时间复杂度的同时得到了比较好的分割效果,使得加权方法更具有实际效用。     

一种基于LUV均匀颜色空间的彩色分割方法

本文提出一种通过转换颜色空间和颜色聚类的方法来实现对彩色图像的分割。由于RGB颜色空间是非均匀空间,算法首先把RGB颜色空间转换到LUV均匀颜色空间,然后对颜色进行相似合并,最终由几种颜色表示待分割的图像,从而实现对彩色图像的色彩分割。     

Mixture model for face-color modeling and segmentation

In this paper, we propose a general methodology for face-color modeling and segmentation. One of the major difficulties in face detection and retrieval is partial face extraction due to highlights, shadows and lighting variations. We show that a mixture-of-Gaussians modeling of the color space, provides a robust representation that can accommodate large color variations, as well as highlights and shadows. Our method enables to segment within-face regions, and associate semantic meaning to them, and provides statistical analysis and evaluation of the dominant variability within a given archive.     

敏感视频检测

This paper addresses the problem of detecting objectionable videos, which has never been carefully studied before. Our method can be efficiently used to filter objectionable videos on Internet. One tensor based key-frame selection algorithm, one cube based color model and one objectionable video estimation algorithm are presented. The key frame selection is based on motion analysis using the three-dimensional structure tensor. Then the cube based color model is employed to detect skin color in each key frame. Finally, the video estimation algorithm is applied to estimate objectionable degree in videos. Experimental results on a variety of real-world videos downloaded from Internet show that this method is promising.     

Content-based audio classification and segmentation by using support vector machines

Content-based audio classification and segmentation is a basis for further audio/video analysis. In this paper, we present our work on audio segmentation and classification which employs support vector machines (SVMs). Five audio classes are considered in this paper: silence, music, background sound, pure speech, and non- pure speech which includes speech over music and speech over noise. A sound stream is segmented by classifying each sub-segment into one of these five classes. We have evaluated the performance of SVM on different audio type-pairs classification with testing unit of different- length and compared the performance of SVM, K-Nearest Neighbor (KNN), and Gaussian Mixture Model (GMM). We also evaluated the effectiveness of some new proposed features. Experiments on a database composed of about 4- hour audio data show that the proposed classifier is very efficient on audio classification and segmentation. It also shows the accuracy of the SVM-based method is much better than the method based on KNN and GMM.