基于Eigenspace的面瘫判定系统

本文将特征空间分类方法应用于面瘫图像的判定。结合应用的特点，引入多特征空间分类，比较图像与它们到两个特征空间的投影之间的距离作出判定，并在系统中加入灰度图变换，脸部子区域分割，确定对称轴和输入向量计算等前处理步骤。实验证明，本文的改进提高了系统判定的正确性。     

模式特征滤波

借鉴了传统的信号频谱滤波原理,根据最小均方误差原则,在特征空间法模式识别中提出了“特征空间维纳滤波”算法,它充分利用先验知识,为模式识别系统构造一个线性滤波器;理论和实验结果表明,维纳滤波使总偏差达到最小,实验结果还表明它对识别性能有一定改善。     

A Bayesian approach to object detection using probabilistic appearance-based models

In this paper, we introduce a Bayesian approach, inspired by probabilistic principal component analysis (PPCA) (Tipping and Bishop in J Royal Stat Soc Ser B 61(3):611–622, 1999), to detect objects in complex scenes using appearance-based models. The originality of the proposed framework is to explicitly take into account general forms of the underlying distributions, both for the in-eigenspace distribution and for the observation model. The approach combines linear data reduction techniques (to preserve computational efficiency), non-linear constraints on the in-eigenspace distribution (to model complex variabilities) and non-linear (robust) observation models (to cope with clutter, outliers and occlusions). The resulting statistical representation generalises most existing PCA-based models (Tipping and Bishop in J Royal Stat Soc Ser B 61(3):611–622, 1999; Black and Jepson in Int J Comput Vis 26(1):63–84, 1998; Moghaddam and Pentland in IEEE Trans Pattern Anal Machine Intell 19(7):696–710, 1997) and leads to the definition of a new family of non-linear probabilistic detectors. The performance of the approach is assessed using receiver operating characteristic (ROC) analysis on several representative databases, showing a major improvement in detection performances with respect to the standard methods that have been the references up to now.This revised version was published online in November 2004 with corrections to the section numbers.     

一种新的高分辨稳定阵列信号估计算法

为提高信号的分辨率,提出了一种新的基于均匀线阵的高分辨稳定的阵列信号方向(DOA)估计方法。主要利用阵列接受数据的自相关矩阵进行特征分解,得到最大特征值所对应的最大特征向量。按一定的方式对最大特征向量数据进行重排,构成新的矩阵,通过SVD分解获取信号的噪声子空间,然后利用特征空间的正交性进行DOA估计。新方法由于新构矩阵的特殊性能实现相干和非相干信号的同时分辨,能克服常用阵列信号估计MUSIC法信噪比门限较高以及常用解相干平滑算法(FBSS)无法完全利用阵列接受数据自相关矩阵的固有缺陷。大量的计算机仿真实现表明,方法是一种高分辨、高稳定性的DOA估计算法。     

A subarray-synthesis based 2D DOA estimation method

In some satellite communications, we need to perform Direction Of Arrival （DOA） angle estimation under the restriction that the number of receivers is less than that of the array elements in an array antenna. To solve the conundrum, a method named subarray-synthesis-based Two-Dimensional DOA （2D DOA） angle estimation is proposed. In the method, firstly, the array antenna is divided into a series of subarray antennas based on the total number of receivers; secondly, the subarray antennas＇ output covariance matrices are estimated; thirdly, an equivalent covariance matrix is synthesized based on the subarray output covariance matrices; then 2D DOA estimation is performed. Monte Carlo simulations showed that the estimation method is effective.     

基于子空间的3D目标识别和姿态估计方法

提出一种基于子空间的3D目标识别方法。该方法对3D目标进行事先的训练学习,采集目标可能出现时的图像,提取场景中目标的主要特征成分,建立所有目标样本图像和每个目标样本图像对应的两类特征子空间,分别用来确定目标类型和姿态。当输入一幅未知的待识别目标样像,识别系统将其分别向两类特征空间投影,根据它在两类特征子空间中的投影位置并参照目标特征的分布规律识别目标类型和姿态。实验证明,该方法具备对目标多种姿态图像畸变的鲁棒性,对光照变化也有很好的抑制作用,取得良好的目标识别效果。     

基于特征空间的相干干扰抑制技术

该文把多线性约束和特征空间技术相结合,提出了基于特征空间的多约束最小方差波束形成器(EM-CMVB),该波束形成器可以有效抑制相干干扰。EMCMVB是利用估计得到的相干干扰方向和阵列信号相关矩阵特征分解来得到修改的信号子空间,再把多约束最小方差波束形成器(MCMVB)的权矢量向修改的信号子空间投影来得出新的权矢量。经分析表明,EMCMVB的性能优于MCMVB的性能。最后,给出了计算机仿真结果,证实了EMCMVB的优越性。     

Performance evaluation of incremental training method for face recognition using PCA

Relevance of ‘face recognition’ (FR) in the modern world requirements is presented as a case of human machine interaction. Physical conditions that influence the face recognition process regarding the facial features, illumination changes and viewing angles etc. are discussed. Face recognition process predominantly depends on machine perception i.e. information through an array of pixels with respect to the facial image. Details of eigenface approach through the involvement of contemporary algebraic and statistical analysis are revisited. Methodology involved in the Principal Component Analysis and advantages of exposing the data to incremental training (using PCA) are discussed. A model for the implementation of IPCA over the face databases is proposed to estimate its performance for the face recognition process. Performance of the present model is studied in the domain of Euclidean distance, decay parameter, recognition rate, eigenvalues and overall computational time. Present IPCA model administered over standard ORL, FERET databases along with that over the JNTU face database with large number of face images revealed relative performance. The merit of present IPCA is inferred through enhanced recognition rate and reduced complexity (in the algorithm), intelligent eigenvectors and lesser computational time. The results are presented in the wake of the body of data available with other methods.

A robust eigenspace method for obtaining feature values in high-speed massively parallel vision systems

Image-processing systems, each consisting of massively parallel photodetectors and digital processing elements on a monolithic circuit, are currently being developed by several researchers. Some earlyvision-like processing algorithms are installed in the vision systems. However, they are not sufficient for applications because their output is in the form of pattern information, so that, in order to respond to input, some feature values are required to be extracted from the pattern. In the present paper, we propose a robust method for extracting feature values associated with images in a massively parallel vision system.     

Finite-dimensional perturbations of linear operators and some applications to boundary integral equations

Finite-dimensional perturbing operators are constructed using some incomplete information about eigen-solutions of an original and/or adjoint generalized Fredholm operator equation (with zero index). Adding such a perturbing operator to the original one reduces the eigen-space dimension and can, particularly, lead to an unconditionally and uniquely solvable perturbed equation. For the second kind Fredholm operators, the perturbing operators are analyzed such that the spectrum points for an original and the perturbed operators coincide except a spectrum point considered, which can be removed for the perturbed operator. A relation between resolvents of original and perturbed operators is obtained. Effective procedures are described for calculation of the undetermined constants in the right-hand side of an operator equation for the case when these constants must be chosen to satisfy the solvability conditions not written explicitly. Implementation of the methods is illustrated on a boundary integral equation of elasticity.