基于Gabor滤波的指纹图像识别研究与实现

采用基于Gabor滤波指纹识别算法进行指纹图像中心点计算、扇形化、归一化、Gabor滤波、特征提取以及比对,并利用Matlab仿真实现该算法.在仿真实验中建立一定数量的指纹库,对该算法进行比对分析.     

视频目标跟踪中的提升分类算法

针对目标跟踪过程中各类图像特征分离背景和目标能力的动态变化,提出一种基于协同训练框架的在线提升分类特征选择算法.该算法采用两组特征描述目标与背景区域各像素,并分别训练一在线提升分类器对特征组进行选择,然后综合分类结果,得到最优似然图像,基于该似然图像,采用粒子滤波对目标进行琅踪并通过图像处理方法获得最佳前景分割图.该方法的主要优点是仅需对首帧图像进行训练,并在跟踪过程中通过协同训练在线更新提升分类器.同时,实验表明该算法运算速度快,并能适应环境光照变化、遮挡等恶劣条件.     

高提升滤波在低对比度目标相关探测中的应用

光电混合联合变换器可实现对目标的实时探测、识别及自动定位。但低对比度目标的相关峰强度很弱,甚至得不到相关峰．影响了目标的识别率。利用高提升滤波处理技术和图像分割对功率谱进行增强处理,可以有效提高功率谱对比度,最大限度保留光谱图像的细节信息,提高光电联合相关系统的目标识别率。     

信号的滤波技术发展历程

详细介绍了滤波技术的历史、发展和应用状况,对几种新型滤波器技术进行了讨论,提供了它们的研究课题。对国内外滤波器现状进行了分析,展望了滤波器技术的未来。     

网络信息两层过滤系统的设计

随着Internet的普及和应用,网络应用在人们生活中占据越来越大的比重,对网络中的信息进行实时过滤,具有十分重要的意义。本文对贪婪随机自适应算法(GRASP)进行扩展,得到基于权值的贪婪随机自适应算法(WGRASP),利用WGRASP、多模式匹配分类、支持向量机等算法构造了一个网络信息两层过滤系统。该系统能自动获取一个优良的非法关键词集合,且对网络信息的过滤具有较高的过滤速度和文本识别查准率、查全率。     

Using local transition probability models in Markov random fields for forest change detection

Change detection based on the comparison of independently classified images (i.e. post-classification comparison) is well-known to be negatively affected by classification errors of individual maps. Incorporating spatial-temporal contextual information in the classification helps to reduce the classification errors, thus improving change detection results. In this paper, spatial-temporal Markov Random Fields (MRF) models were used to integrate spatial-temporal information with spectral information for multi-temporal classification in an attempt to mitigate the impacts of classification errors on change detection. One important component in spatial-temporal MRF models is the specification of transition probabilities. Traditionally, a global transition probability model is used that assumes spatial stationarity of transition probabilities across an image scene, which may be invalid if areas have varying transition probabilities. By relaxing the stationarity assumption, we developed two local transition probability models to make the transition model locally adaptive to spatially varying transition probabilities. The first model called locally adjusted global transition model adapts to the local variation by multiplying a pixel-wise probability of change with the global transition model. The second model called pixel-wise transition model was developed as a fully local model based on the estimation of the pixel-wise joint probabilities. When applied to the forest change detection in Paraguay, the two local models showed significant improvements in the accuracy of identifying the change from forest to non-forest compared with traditional models. This indicates that the local transition probability models can present temporal information more accurately in change detection algorithms based on spatial-temporal classification of multi-temporal images. The comparison between the two local transition models showed that the fully local model better captured the spatial heterogeneity of the transition probabilities and achieved more stable and consistent results over different regions of a large image scene.     

Robust Kalman filtering for uncertain discrete-time systems with probabilistic parameters bounded within a polytope

We consider the problem of finite horizon discrete-time Kalman filtering for systems with parametric uncertainties. Specifically, we consider unknown but deterministic uncertainties where the uncertain parameters are assumed to lie in a convex polyhedron with uniform probability density. The condition and a procedure for the construction of a suboptimal filter that minimizes an expected error covariance over-bound are derived.     

An efficient algorithm for positive realizations

We observe that successive applications of known results from the theory of positive systems lead to an efficient general algorithm for positive realizations of transfer functions. We give two examples to illustrate the algorithm, one of which complements an earlier result of [L. Benvenuti, L. Farina, An example of how positivity may force realizations of ‘large’ dimensions, Systems Control Lett. 36 (1999) 261–266]. Finally, we improve a lower-bound of [B. Nagy, M. Matolcsi, A lower-bound on the dimension of positive realizations, IEEE Trans. Circuits Syst. I 50 (2003) 782–784] to indicate that the algorithm is indeed efficient in general.     

A collaborative filtering framework based on both local user similarity and global user similarity

Collaborative filtering as a classical method of information retrieval has been widely used in helping people to deal with information overload. In this paper, we introduce the concept of local user similarity and global user similarity, based on surprisal-based vector similarity and the application of the concept of maximin distance in graph theory. Surprisal-based vector similarity expresses the relationship between any two users based on the quantities of information (called surprisal) contained in their ratings. Global user similarity defines two users being similar if they can be connected through their locally similar neighbors. Based on both of Local User Similarity and Global User Similarity, we develop a collaborative filtering framework called LS&GS. An empirical study using the MovieLens dataset shows that our proposed framework outperforms other state-of-the-art collaborative filtering algorithms.     

A multi-agent legal recommender system

Infonorma is a multi-agent system that provides its users with recommendations of legal normative instruments they might be interested in. The Filter agent of Infonorma classifies normative instruments represented as Semantic Web documents into legal branches and performs content-based similarity analysis. This agent, as well as the entire Infonorma system, was modeled under the guidelines of MAAEM, a software development methodology for multi-agent application engineering. This article describes the Infonorma requirements specification, the architectural design solution for those requirements, the detailed design of the Filter agent and the implementation model of Infonorma, according to the guidelines of the MAAEM methodology.