Ground target detection, classification and sensor fusion in distributed fiber seismic sensor network - art. no. 683015

This paper describes the ground target detection, classification and sensor fusion problems in distributed fiber seismic sensor network. Compared with conventional piezoelectric seismic sensor used in UGS, fiber optic sensor has advantages of high sensitivity and resistance to electromagnetic disturbance. We have developed a fiber seismic sensor network for target detection and classification. However, ground target recognition based on seismic sensor is a very challenging problem because of the non-stationary characteristic of seismic signal and complicated real life application environment. To solve these difficulties, we study robust feature extraction and classification algorithms adapted to fiber sensor network. An united multi-feature (UMF) method is used. An adaptive threshold detection algorithm is proposed to minimize the false alarm rate. Three kinds of targets comprise personnel, wheeled vehicle and tracked vehicle are concerned in the system. The classification simulation result shows that the SVM classifier outperforms the GMM and BPNN. The sensor fusion method based on D-S evidence theory is discussed to fully utilize information of fiber sensor array and improve overall performance of the system. A field experiment is organized to test the performance of fiber sensor network and gather real signal of targets for classification testing.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.

基于特征提取的服装外形设计草图推荐技术

草图是服装外形设计中一类高效的信息载体,为了提高服装外形设计的效率,减少大量的重复操作,本文设计并实现了一种基于草图特征提取的二级推荐技术,在设计过程中整合草图轮廓的几何特征和草图勾画过程中的过程特征,同时结合相关反馈技术和用户交互历史数据,并结合笔式界面技术,开发了笔式服装外形设计原型,改善了人机交互方式,能够在用户设计过程中满足草图推荐的目的。

Deterministic Column-Based Matrix Decomposition

In this paper, we propose a deterministic column-based matrix decomposition method. Conventional column-based matrix decomposition (CX) computes the columns by randomly sampling columns of the data matrix. Instead, the newly proposed method (termed as CX_D) selects columns in a deterministic manner, which well approximates singular value decomposition. The experimental results well demonstrate the power and the advantages of the proposed method upon three real-world data sets.

阿片类药物成瘾者瞳孔光反射的检测和特征提取

建立了一种基于图像处理的快速瞳孔直径检测算法,运用此算法提取了反映阿片类药物成瘾人员与正常人对瞳孔光反射变化差异的3个特征值:绝对收缩幅度(absolute amplitude of contraction,AAC)、相对收缩幅度(relative amplitude of contraction,RAC)和收缩斜率(SCV,slope of contraction velocity);分别研究了成瘾、性别、近视、年龄、睡眠剥夺等因素对于这3个特征值的影响。不同性别、近视人员、睡眠剥夺人员与正常人之间的3个特征值均无显著差异,成瘾人员与之对比均显著减小。老年人相对于正常青年人,3个特征值都明显减小;与成瘾人员相比,仅在RAC值上有显著差异。结果表明,阿片类药物成瘾人员除了与正常人外,也与其他具有潜在影响瞳孔变化因素的非阿片成瘾人员在瞳孔对光反射的特征值上具有显著差异。该研究的实验数据为进一步建立基于检测瞳孔对光反射其直径发生变化的方法来快速、非接触地鉴别出阿片类药物成瘾人员提供了可靠的依据。

A method of Biomimetic Pattern Recognition for face recognition

A new method of face recognition, based on Biomimetic Pattern Recognition and Multi-Weights Neuron Network, had been proposed. A model for face recognition that is based on Biomimetic Pattern Recognition had been discussed, and a new method of facial feature extraction also had been introduced. The results of experiments with BPR and K-Nearest Neighbor Rules showed that the method based on BPR can eliminate the error recognition of the samples of the types that not be trained, the correct rate is also enhanced.

联机藏文手写识别特征提取方法研究

近年来，藏文信息处理倍受国家重视，发展迅速，但是针对联机藏文手写识别的研究却处于起步阶段。半个世纪以来，关于英文、中文、日文等的联机手写识别的研究已经发展的相当成熟，大量应用于各种联机终端中，给大众带来了巨大的便利。根据联机藏文手写字符的特点，在大量的已有理论中选取适合的方案，对选中的成熟理论进行合理的改进，是一条快捷的发展联机藏文手写识别的路线。本文研究了联机藏文手写识别中的预处理和特征提取部分。预处理是保证特征提取设计方案有效性的前提，包括线性归一化、非线性归一化、等距离重采样和笔画平滑等操作。对这些操作，本文参考其他文种联机手写识别中成熟的方法，针对联机藏文手写字符的特点进行了筛选和改进。本文主要使用网格方向特征来对特征提取阶段进行设计，它是一种目前大量使用的手写体特征。网格方向特征提取方案主要包括方向分解、投影子分量图像、网格划分、确定权值分配器等操作。本文针对藏文手写字符的特点，在大量实验的基础上，确定了四方向拆分、均匀网格划分和使用Gaussian滤波器作为权值分配器的网格方向特征提取方案。在对藏文手写识别预处理和特征提取研究的基础上，本文提出了基于网格方向特征的联机藏文手写识别特征提取方案。

An integrated aurora image retrieval system: AuroraEye

With the digital all-sky imager (ASI) emergence in aurora research, millions of images are captured annually. However, only a fraction of which can be actually used. To address the problem incurred by low efficient manual processing, an integrated image analysis and retrieval system is developed. For precisely representing aurora image, macroscopic and microscopic features are combined to describe aurora texture. To reduce the feature dimensionality of the huge dataset, a modified local binary pattern (LBP) called ALBP is proposed to depict the microscopic texture, and scale-invariant Gabor and orientation-invariant Gabor are employed to extract the macroscopic texture. A physical property of aurora is inducted as region features to bridge the gap between the low-level visual features and high-level semantic description. The experiments results demonstrate that the ALBP method achieves high classification rate and low computational complexity. The retrieval simulation results show that the developed retrieval system is efficient for huge dataset. (c) 2010 Elsevier Inc. All rights reserved.

基于多重分形的水泥回转窑工况识别研究

鉴于多重分形理论在定量描述复杂系统非线性运行规律方面具有的独特优势,将多重分形理论引入到工况特征识别研究中来,确认了水泥回转窑窑电流信号的多重分形特性。在此基础上,研究了窑电流多重分形谱参数随工况变化的情况,发现多重分形谱参数的变化趋势与回转窑内工况状态的变化趋势之间具有较强的关联性,进而提出了基于多重分形谱参数进行水泥回转窑异常工况特征提取的新方法,为水泥生产过程中工况状态的在线监控和预报提供了有力支持。

基于结构化Fisherface的人脸识别新方法

特征提取是人脸识别中一个关键步骤。传统的Fisherface人脸识别方法中用样本的类均值和总体均值定义相应的散布矩阵，丢失了样本个体之间的结构信息，本文提出了一种基于原始样本个体结构信息的结构化Fisherface人脸识别方法，最后得到的特征数据中保留了原始样本更多的分布信息。在ORL人脸数据库的实验结果验证了该方法的有效性。

改进的统计不相关最优鉴别矢量集

该文对统计不相关最优鉴别矢量集算法进行研究,在分析统计不相关最优鉴别矢量集算法的基础上提出了一种改进的方法。该方法在类内散布矩阵的特征空间中求解统计不相关最优鉴别矢量集。为了加快特征抽取速度,利用基于图像鉴别分析的维数压缩方法,对图像数据进行了压缩。在ORL和Yale人脸数据库的数值实验,验证本文所提出的方法的有效性。

广义统计不相关最优鉴别矢量集的一个理论结果

本文对统计不相关最优鉴别矢量集的理论问题进行研究 ,提出了广义统计不相关最优鉴别准则 ,并给出了广义统计不相关最佳鉴别矢量集的一个理论结果 ,研究表明 ,广义统计不相关最佳鉴别矢量集的计算公式与基于Fisher最优鉴别准则的统计不相关最佳鉴别矢量集的计算公式完全一样 ,但是以前这一点没有被认识到 .本文的研究丰富了统计不相关最优鉴别分析的特征抽取理论 .

统计不相关最佳鉴别矢量集的本质研究

对统计不相关最佳鉴别矢量集的本质进行研究 ,在基于总体散布矩阵特征分解的基础上 ,构造了一种白化变换 ,使得变换后的样本空间中的总体散布矩阵为单位矩阵 ,这样使得传统的最佳鉴别矢量集算法得到的均是具有统计不相关的最佳鉴别矢量集 ,从而揭示了统计不相关最佳鉴别变换的本质———白化变换加普通的线性鉴别变换。该方法的最大优点在于所获得的最优鉴别矢量同时具有正交性和统计不相关性。该方法对代数特征抽取具有普遍适用性。用ORL人脸数据库的数值实验 ,验证了该方法的有效性

一种新的图像特征抽取方法研究

对最佳鉴别矢量的求解方法进行了研究，根据矩阵的分块理论和优化理论，在一定的条件下，从理论上得到类间散布矩阵和总体散布矩阵的一种简洁表示方法，提出了求解最佳鉴别矢量的一种新算法，该算法的优点是计算量明显减少。ORL人脸数据库的数值实验，验证了上述论断的正确性。实验结果表明，虽然识别率与分块维数之间存在非线性关系，但可以通过选择适当的分块维数来获得较高的识别率。类间散布矩阵和总体散布矩阵的一种简洁表示方法适合于一切使用Fisher鉴别准则的模式识别问题。