基于图像质量分区的指纹特征提取

低质量指纹图像的特征提取和变形指纹的匹配是当前指纹识别研究中的两个主要问题。很多算法在特征提取时不区分高、低质量区域,结果在高质量区域耗费了过多的运算时间和计算资源。本文提出了一种基于图像质量分区的指纹特征提取方法,先用一种简单的图像区域质量计算方法评价各区域的图像质量,然后对高质量区域直接从灰度图像跟踪纹线、提取节点,对低质量区域执行传统的方向计算、增强、二值化和细化后提取特征。实验结果表明,该方法不仅提高了特征提取的速度,在准确性上也有所提高。     

Automated analysis of images in documents for intelligent document search

Authors use images to present a wide variety of important information in documents. For example, two-dimensional (2-D) plots display important data in scientific publications. Often, end-users seek to extract this data and convert it into a machine-processible form so that the data can be analyzed automatically or compared with other existing data. Existing document data extraction tools are semi-automatic and require users to provide metadata and interactively extract the data. In this paper, we describe a system that extracts data from documents fully automatically, completely eliminating the need for human intervention. The system uses a supervised learning-based algorithm to classify figures in digital documents into five classes: photographs, 2-D plots, 3-D plots, diagrams, and others. Then, an integrated algorithm is used to extract numerical data from data points and lines in the 2-D plot images along with the axes and their labels, the data symbols in the figure’s legend and their associated labels. We demonstrate that the proposed system and its component algorithms are effective via an empirical evaluation. Our data extraction system has the potential to be a vital component in high volume digital libraries.     

Human tracking and silhouette extraction for human–robot interaction systems

In this study, we propose a new integrated computer vision system designed to track multiple human beings and extract their silhouette with a pan-tilt stereo camera, so that it can assist in gesture and gait recognition in the field of Human–Robot Interaction (HRI). The proposed system consists of three modules: detection, tracking and silhouette extraction. These modules are robust to camera movements, and they work interactively in near real-time. Detection was performed by camera ego-motion compensation and disparity segmentation. For tracking, we present an efficient mean shift-based tracking method in which the tracking objects are characterized as disparity weighted color histograms. The silhouette was obtained by two-step segmentation. A trimap was estimated in advance and then effectively incorporated into the graph-cut framework for fine segmentation. The proposed system was evaluated with respect to ground truth data, and it was shown to detect and track multiple people very well and also produce high-quality silhouettes.

Finding the number of clusters in ordered dissimilarities

As humans, we have innate faculties that allow us to efficiently segment groups of objects. Computers, to some degree, can be programmed with similar categorical capabilities, which stem from exploratory data analysis. Out of the various subsets of data reasoning, clustering provides insight into the structure and relationships of input samples situated in a number of distributions. To determine these relationships, many clustering methods rely on one or more human inputs; the most important being the number of distributions, c, to seek. This work investigates a technique for estimating the number of clusters from a general type of data called relational data. Several numerical examples are presented to illustrate the effectiveness of the proposed method.     

Extraction of genomic DNA and detection of single nucleotide polymorphism genotyping utilizing an integrated magnetic bead-based microfluidic platform

This study presents a new magnetic bead-based microfluidic platform, which integrates three major modules for rapid leukocytes purification, genomic DNA (gDNA) extraction and fast analysis of genetic gene. By utilizing microfluidic technologies and magnetic beads conjugated with CD_15/45 antibodies, leukocytes in a human whole blood sample can be first purified and concentrated, followed by extraction of gDNA utilizing surface-charge switchable, DNA-specific, magnetic beads in the lysis solution. Then, specific genes associated with genetic diseases can be amplified by an on-chip polymerase chain reaction (PCR) process automatically. The whole pretreatment process including the leukocytes purification and gDNA extraction can be performed in an automatic fashion with the incorporation of the built bio-separators consisting of microcoils array within less than 20 min. The detection of single nucleotide polymorphism (SNP) genotyping of methylenetetra-hydrofolate reductase (MTHFR) C677T region associated with an increased risk of genetic diseases was further performed to demonstrate the capability of the proposed system. The extracted gDNA can be transported into a micro PCR chamber for on-chip fast nucleic acid amplification of detection genes with minimum human intervention. Hence, the developed system may provide a powerful automated platform for pretreatment of human leukocytes, gDNA extraction and fast analysis of genetic gene.     

基于签名鉴别的网络身份认证模型

计算机网络安全问题已经越来越受到人们的重视,而整个网络安全体系又是以有效的身份认证为基础。分析了传统的身份认证技术的缺点,对签名鉴别进行了概述,详细阐述了基于签名鉴别的身份认证技术的基本原理和基本流程,并根据签名鉴别的基本原理,把签名鉴别技术应用到网络身份认证领域,并详细介绍了基于签名鉴别的网络身份认证系统的体系结构和认证原理。     

基于照片的人体尺寸信息自动提取技术

针对3D扫描仪成本高,传统手工软尺测量效率低的状况,在保证精度的前提下,尝试应用图像处理理论从满足一定条件的照片提取人体尺寸信息,为建立人体尺寸信息库提供一种信息采集的自动化方法.通过对实验结果的分析,本文验证了此方法的可行性,并提出未来的研究方向.     

粗糙集理论的双重学习方法研究

提出了一种新的粗糙集双重学习方法,该方法能用遗传算法实现外层学习,用规则提取方法进行内层学习．其基本思想是：首先引入遗传算法,将属性编码,并针对不同的属性组合进行规则提取;然后用测试样本对规则集进行检验,并基于所得到的识别率建立适应度函数;最后在合适的遗传算子下获取最佳的属性组合及相应的知识规则．与其他方法相比,本文所提粗糙集双重学习方法集属性约简和规则提取于一体,整个过程具有很强的自适应能力．最后,用算例对本文方法进行了验证．     

川南石刻图像特征提取方法研究

本文以四川南部地区南宋墓葬石刻艺术为切入点,提出石刻图像特征提取的具体方法,并通过仿真试验证明了该方法的有效性,最后指出了进一步的研究方向.     

Web检索查询意图分类技术综述

查询分类是近年来信息检索领域的研究热点,并且在很多领域得到了广泛地关注。主要讨论根据查询的意图进行分类的研究工作,从查询分类的诞生背景、关键技术、所使用的分类方法和评价方法方面进行综述评论,提出了查询意图分类面临的问题和挑战。认为缺乏权威的评测标准、在大规模数据集上的未经全面测试的性能、如何准确地获取查询的特征以及如何证明分类体系的完备性和独立性是目前查询意图分类研究的关键问题。