二维Fisher线性判别中子模式性能的一种评价方法*

针对二维Fisher线性判别（2DFLD）方法中传统子模式选取方法计算量大、十分耗时的问题,结合影响2DFLD方法识别结果的两个主要因素——样本在投影空间的离散程度和子模式之间的相似度,提出了一种子模式性能的评价方法。首先设计子模式的构成方式;接着依据该评价方法计算各个子模式的性能指标;最后选出较优的子模式。在人耳图库、ORL人脸图库及虹膜图库上的实验结果表明,该评价方法能有效地选取较优子模式,并能够将计算时间缩短为常规子模式选择方法的近1/4,是一种有效的子模式性能评价方法。     

一种基于NAMPD的快速图像分割

非对称逆布局模式表示模型(Non-symmetry and Anti-packing pattern representation Model,NAM)借助布局问题的思想,使用一个子模式集合来表示原模式。基于NAM模型,提出了一种灰度图像表示方法,非对称逆布局平面分解模式表示模型(NAM-structrued Plane Decomposition,NAMPD)。在NAMPD中,每一个子模式都对应于图像中的一个矩形区域,该区域的亮度函数由一个斜面模型逼近。图像分割是图像分析中的一种关键方法。传统的图像分割算法大多是基于点阵表示的,运算效率不高。基于NAMPD,提出了一种快速图像分割算法。因为NAMPD将图像块而不是像素作为最小操作单位,所以基于NAMPD的图像处理操作效率更高。实验结果表明,基于NAMPD的图像分割算法的速度较之经典算法更快。     

A structure-preserved local matching approach for face recognition

In this paper, a novel local matching method called structure-preserved projections (SPP) is proposed for face recognition. Unlike most existing local matching methods which neglect the interactions of different sub-pattern sets during feature extraction, i.e., they assume different sub-pattern sets are independent; SPP takes the holistic context of the face into account and can preserve the configural structure of each face image in subspace. Moreover, the intrinsic manifold structure of the sub-pattern sets can also be preserved in our method. With SPP, all sub-patterns partitioned from the original face images are trained to obtain a unified subspace, in which recognition can be performed. The efficiency of the proposed algorithm is demonstrated by extensive experiments on three standard face databases (Yale, Extended YaleB and PIE). Experimental results show that SPP outperforms other holistic and local matching methods.     

An adaptively weighted sub-pattern locality preserving projection for face recognition

In this paper, an adaptively weighted sub-pattern locality preserving projection (Aw-SpLPP) algorithm is proposed for face recognition. Unlike the traditional LPP algorithm which operates directly on the whole face image patterns and obtains a global face features that best detects the essential face manifold structure, the proposed Aw-SpLPP method operates on sub-patterns partitioned from an original whole face image and separately extracts corresponding local sub-features from them. Furthermore, the contribution of each sub-pattern can be adaptively computed by Aw-SpLPP in order to enhance the robustness to facial pose, expression and illumination variations. The efficiency of the proposed algorithm is demonstrated by extensive experiments on three standard face databases (Yale, YaleB and PIE). Experimental results show that Aw-SpLPP outperforms other holistic and sub-pattern based methods.     

SubXPCA and a generalized feature partitioning approach to principal component analysis

In this paper we propose a general feature partitioning framework to PCA computation and raise issues of cross-sub-pattern correlation, feature ordering dependence, selection of sub-pattern size, overlap of sub-patterns and selection of principal components. These issues are critical to the design and performance of feature partitioning approaches to PCA computation. We show several open issues and present a novel algorithm, SubXPCA which proposes a solution to the cross-sub-pattern correlation issue in the feature partitioning framework. SubXPCA is shown to be a general technique since we derive PCA and SubPCA as special cases of SubXPCA. We show SubXPCA has theoretically better time complexity as compared to PCA. Comprehensive experimentation on UCI repository data and face data sets (ORL, CMU, Yale) confirms the superiority of SubXPCA with better classification accuracy. SubXPCA not only has better time performance but is also superior in its summarization of variance as compared to SubPCA. SubXPCA is shown to be robust in its performance with respect to feature ordering and overlapped sub-patterns.     

关于软件标本重用技术的研究

文中阐述了具有类Ａｄａ风格的软件标本重用的技术，阐明了标本重用的基本概念，途径及实现方法，提出将杯本作为重用库中的可重用单元，其重用途径是将部件作为重用参数，标本作为一种模板，实现重用的关键策略是通过预处理生成标本实例，同时，提出了重用库中的单元组成标准的建议。     

基于位平面分解的NAM优化策略

在研究非对称逆布局模式表示模型(NAM)的基础上,针对多值图像表示中子模式的数量优化问题,提出一种基于位平面分解的优化策略,利用位面间的相关性,改变位面内的扫描算法,进一步减少数据冗余,实现更高的压缩率,并进行理论分析和实验验证。结果表明,该优化策略可以大幅减少子模式数量,优化后的子模式个数减少7.1%~27.1%。     

子模式典型相关分析及其在人脸识别中的应用

传统的典型相关分析 (CCA) 是有效的特征提取方法之一, 已广泛应用于包括人脸识别在内的模式识别的许多领域. 但在人脸识别为代表的高维小样本问题上该方法存在如下不足: 1) 人脸识别的小样本特性使 CCA 两组特征矢量构成的总体协方差矩阵奇异, 难以直接应用; 2) 作为一种全局线性投影方法, 不足以很好地描述非线性的人脸识别问题; 3) 缺乏对局部变化的识别鲁棒性. 本文受已提出的子模式主分量分析 (SpPCA) 的启发, 提出了子模式典型相关分析 (SpCCA). 该方法将局部与全局特征矢量之间的相关性特征作为有效的判别信息, 既达到了融合局部与全局信息的目的, 又消除了特征之间的信息冗余. 通过子模式的划分, SpCCA 避免了小样本问题, 更好地描述了非线性的人脸识别问题; 并通过投票方式融合结果, 增强了对局部变化的鲁棒性. 在 AR 与 Yale 两个人脸数据集上的实验证实了该方法比对比方法不仅有更优的识别性能, 而且更加稳定和鲁棒.     

正方形子模式非对称可重叠逆布局表示方法

基于非对称逆布局模式表示模型（NAM）,提出了一个正方形子模式非对称可重叠逆布局表示方法(NOAS)。NOAS从原模式中逆布局出若干正方形子模式,且子模式之间允许重叠。以二值图像为例,给出了NOAS表示的编码算法和解码算法。理论分析表明,NOAS表示相对于线性四元树表示在压缩率方面更具优势。实验结果表明,NOAS表示的子模式数远远小于线性四元树的节点数,且NOAS表示的压缩率明显高于线性四元树、行程编码和LZW编码表示方法。     

基于多元图结构子模式表示的模式识别方法*

针对统计模式识别和结构模式识别方法各自的局限性,提出一种基于多维数据多元图结构子模式表示（包括图形基元和特征基元表示）的模式识别方法,它体现了模式识别问题研究的一种新思维,具有鲜明的可视化特点。应用多维数据多元图表示原理实现无结构数据的结构化表示,提取出表征多元图图形的图形基元和特征基元,将对象表达为多元图结构特征子模式进行分类识别。利用UCI机器学习数据库中的Iris数据进行了分类实验,实验对比结果显示该方法具有较好的识别效果。     

Optimal Spare Capacity Preconfiguration for Faster Restoration of Mesh Networks

Several distributed real-time methods have beenproposed for restoration from single span failures indigital transport networks. These methods have thepotential to avoid user service outages due to such failures, if they operate quickly enough. Forexample, switched 64 kbps connections will not bedisconnected if the network can be restored before thetime at which calls in progress are dropped, typically 1-2 seconds after a failure. However, it willbe difficult to achieve the goal of sub-secondrestoration if cross-connects cannot operate crosspointsquickly enough, either due to large workloads during a restoration response, or because ofimplementation choices such as testing eachcross-connection while in the midst of a serious outage.The results in this paper demonstrate that it can beuseful to pre-operate selected cross-points between thespare links of a mesh-restorable network before anyfailure has occurred, putting the network into astatistically optimal state of readiness. When a failure occurs, some of the preconfigured restorationpath bundles can be used immediately. If morerestoration paths are needed, they can be obtained by areal-time restoration process. The first advantage of preconfiguration is that the number ofcross-connection operations may be greatly reduced oreliminated for a portion of the affected traffic. Thiswill reduce restoration time significantly. Secondly, after utilizing preconfigured restorationpaths, the workload of a real-time restoration processwill be lower because it will be searching for fewerpaths. This paper demonstrates that preconfiguration can supply a significant proportion of thereplacement capacity required after a span failure. Theresults are obtained through integerprogramming.     

一种快速边缘检测算法

边缘检测是对图像中像素值的不连续性的度量、检测和定位,是图像分析领域中的一个基础问题.基于斜面分解的非对称逆布局图像表示方法(IDNAM)借鉴斜面拟合和逆布局的思想,使用矩形斜面子模式(RIP子模式)实例的集合来表示图像.基于IDNAM,提出一个RIP子模式内的理想边缘模型,将RIP子模式内的边缘分成五种,并给出了每一种边缘的强度和方向的计算公式.为了消除噪声对边缘检测结果的干扰,提出了RIP子模式内边缘存在性判定条件.根据理想边缘模型和判定条件,提出了一种基于IDNAM的边缘检测算法.理论分析和实验结果均表明,基于IDNAM表示的边缘检测算法的处理速度较传统的边缘检测算法更快.     

Word spotting in historical printed documents using shape and sequence comparisons

Information spotting in scanned historical document images is a very challenging task. The joint use of the mechanical press and of human controlled inking introduced great variability in ink level within a book or even within a page. Consequently characters are often broken or merged together and thus become difficult to segment and recognize. The limitations of commercial OCR engines for information retrieval in historical document images have inspired alternative means of identification of given words in such documents. We present a word spotting method for scanned documents in order to find the word images that are similar to a query word, without assuming a correct segmentation of the words into characters. The connected components are first processed to transform a word pattern into a sequence of sub-patterns. Each sub-pattern is represented by a sequence of feature vectors. A modified Edit distance is proposed to perform a segmentation-driven string matching and to compute the Segmentation Driven Edit (SDE) distance between the words to be compared. The set of SDE operations is defined to obtain the word segmentations that are the most appropriate to evaluate their similarity. These operations are efficient to cope with broken and touching characters in words. The distortion of character shapes is handled by coupling the string matching process with local shape comparisons that are achieved by Dynamic Time Warping (DTW). The costs of the SDE operations are provided by the DTW distances. A sub-optimal version of the SDE string matching is also proposed to reduce the computation time, nevertheless it did not lead to a great decrease in performance. It is possible to enter a query by example or a textual query entered with the keyboard. Textual queries can be used to directly spot the word without the need to synthesize its image, as far as character prototype images are available. Results are presented for different documents and compared with other methods, showing the efficiency of our method.     

基于最小鉴别信息的正则化图像恢复方法

提出在正则化图像恢复方法中将图像恢复结果与先验图像的最小鉴别信息作为新的正则化约束.同传统的正则化约束不同,新的约束使得恢复的图像与给定的先验图像具有最相似的灰度分布.同时给出一种自适应确定正则化参数的方法.实验结果表明,新方法在恢复效果上要优于传统的正则化方法,但对噪声则比较敏感.因此,提出在降质图像含有较多的噪声时保留传统的正则化约束,以达到更好的恢复效果.     

基于二级修复的多方向加权均值滤波算法

针对目前算法不能有效去除高概率的椒盐噪声并保护图像边缘和细节特征的缺点,提出了一种基于二级修复的多方向加权均值滤波算法。在噪声检测阶段,首先利用一个方差参数判断当前像素点与其邻域像素点之间的灰度差异程度,再通过将方差参数和灰度极值相结合的方法检测出图像中的椒盐噪声点。在噪声修复阶段,提出一种二级修复方法来修复噪声点的灰度值。首先利用改进的自适应中值滤波器对椒盐噪声点进行第一级噪声修复;然后利用方差参数将第一级修复后的噪声点划分为两类,并采用不同的修复方法对这两类像素点进行第二级噪声修复,一类像素点采用均值滤波器进行再修复,另外一类像素点采用多方向加权均值滤波器进行再修复。数值实验结果表明,所提算法的滤波性能和边缘保护能力均优于当下很多先进的滤波器。     

Associative Gray Level Pattern Processing using Binary Decomposition and α β Memories

In this note we show how a binary memory can be used to recall gray-level patterns. We take as example the α β associative memories recently proposed in Yáñez, Associative Memories based on order Relations and Binary Operators(In Spanish), PhD Thesis, Center for computing Research, February of 2002, only useful in the binary case. Basically, the idea consists on that given a set of gray-level patterns to be first memorized: (1) Decompose them into their corresponding binary patterns, and (2) Build the corresponding binary associative memory (one memory for each binary layer) with each training pattern set (by layers). A given pattern or a distorted version of it, it is recalled in three steps: (1) Decomposition of the pattern by layers into its binary patterns, (2) Recalling of each one of its binary components, layer by layer also, and (3) Reconstruction of the pattern from the binary patterns already recalled in step 2. The proposed methodology operates at two phases: training and recalling. Conditions for perfect recall of a pattern either from the fundamental set or from a distorted version of one them are also given. Experiments where the efficiency of the proposal is tested are also given.     

基于方向信息测度的图像恢复

文章根据人眼对图像平滑区噪声敏感,而对边缘区不敏感这一特性,提出了基于图像信息测度的图像恢复方法。在图像恢复的过程中,在图像的平滑区加大平滑程度,从而减小噪声;而在边缘区,加大图像恢复程度,从而增强边缘,并且考虑了模糊程度随空间变化这一实际。实验证明本文的方法在增大图像边缘恢复程度,减小噪声放大之间能够达到一个很好的折衷,恢复出来的结果图像视觉效果良好。     

Software support for the construction of syntactic pattern recognisers by a research engineer

Syntactic pattern recognition is introduced and it is suggested that a research engineer will probably be less familiar with the language theory underlying syntactic pattern recognition than with the statistical ideas connected with decision theoretic methods. For this reason application of syntactic pattern recognition will only develop if software support is provided. The steps of grammatical inference, recogniser construction and recogniser optimisation are outlined. They are included in a scheme by which, working interactively and iteratively, a syntactic pattern recogniser can be produced. Some comments on an implementation are given.     

铜闪速熔炼过程操作模式的智能优化

提出了基于模式分解的铜闪速熔炼操作模式智能优化方法,描述需要在线决策的操作参数.从历史样本集中筛选优化的样本组成模式,采用基于聚类的方法进行模武分解,通过混沌遗传算法对每个模式子集寻求操作模式的优化.将该方法应用于铜闪速熔炼过程,提高了铜闪速的操作水平.