Object recognition and pose estimation using appearance manifolds

Conventionally, image object recognition and pose estimation are two independent components in machine vision. This paper presented a simple but effective method KNNSNG, which tightly couples these two com ponents within a single algorithm framework. The basic idea of this method came from the bionic pattern recog nition and the manifold ways of perception. Firstly, the shortest neighborhood graphs （SNG） are established for each registered object. SNG can be regarded as a covering and triangulation for a hypersurface on which the training data are distributed. Then for recognition task, the deter mined test image lies on which SNG by employing the parameter ＂k＂, which can be calculated adaptively. Finally, the local linear approximation method is adopted to build a local map between highdimensional image space and lowdimensional manifold for pose estimation. The projective coordinates on manifold can depict the pose of object. Experiment results manifested the effectiveness of the method.     

伪黎曼流形中具有平行第二基本形式的子流形

讨论了伪黎曼流形中具有平行第二基本形式的类空子流形,得到了它的第二基本形式模长平方有界的一个拼挤条件,推广了已有结论.     

关于左k-单演函数中一个积分算子的注记

本文证明了左k-单演函数中一个积分算子的两个性质。     

一类时滞SIV无线传感器网络病毒传播模型

为了深入研究无线传感器网络中病毒的传播规律,以无线传感器网络中病毒潜伏期的时滞为分岔参数,研究了一类时滞SIV无线传感网络病毒传播模型的局部渐近稳定性和Hopf分岔。首先通过讨论特征根分布,得到模型局部渐近稳定性和产生Hopf分岔的充分条件,进而利用中心流形定理和规范型理论确定了Hopf分岔的方向和分岔周期解的稳定性,最后通过仿真示例验证了理论结果的正确性。     

基于小波特征的维吾尔语方言识别

为进一步提高维吾尔语方言识别准确率,从特征域出发提出基于小波包变换的方言语音声学参数提取算法,利用小波包变换对语音信号进行精细分解,使用Bark滤波器提取低频子带和高频子带系数与人耳基底膜的不同部分对应,用局部线性嵌入的流形学习算法进行降维,提升信号的分类性能。分别以i-vector和x-vector两个主流系统进行方言识别实验,结果表明,小波特征声学参数的方言识别准确率比MFCC特征和FBank特征识别准确率均有提高。     

复Grassmann流形中全实曲面的构造

给出复Grassmann流形G（2,n+2）的全实曲面的一种构造方法,也就是把G（2,n+2）看作$\mathbb{H}$Pⁿ⁺¹中极小子流形Qⁿ⁺¹的商,并证明G（2,n+2）中的曲面可以水平提升到Qⁿ⁺¹中当且仅当它是全实的。     

Image feature optimization based on nonlinear dimensionality reduction

Image feature optimization is an important means to deal with high-dimensional image data in image semantic un- derstanding and its applications. We formulate image feature optimization as the establishment of a mapping between high- and low-dimensional space via a five-tuple model. Nonlinear dimensionality reduction based on manifold learning provides a feasible way for solving such a problem. We propose a novel globular neighborhood based locally linear embedding （GNLLE） algorithm using neighborhood update and an incremental neighbor search scheme, which not only can handle sparse datasets but also has strong anti-noise capability and good topological stability. Given that the distance measure adopted in nonlinear dimensionality reduction is usually based on pairwise similarity calculation, we also present a globular neighborhood and path clustering based locally linear embedding （GNPCLLE） algorithm based on path-based clustering. Due to its full consideration of correlations between image data, GNPCLLE can eliminate the distortion of the overall topological structure within the dataset on the manifold. Experimental results on two image sets show the effectiveness and efficiency of the proposed algorithms.     

线性流形上D反对称矩阵反问题的最佳逼近

【摘要】本文研究了线性流形S={A∈D^-2A．sRnxn｜｜｜AxB｜｜=min,X,B∈Rnxm}上矩阵方程f（A）=｜｜AY—z｜｜=min的D反对称解,利用矩阵的奇异值分解,给出了这类线性流形上矩阵方程存在D反对称解的充要条件及其通解表达式．另外,导出了在线性流形上矩阵方程的解集合中与给定矩阵的最佳逼近解的表达式．     

基于半监督拉普拉斯适应实验设计的图像分类

基于流形学习理论,通过改变权值的设置将类别信息加到图构造过程中,提出一种新的主动学习算法.在3个真实图像数据集上的实验结果表明,新算法能够取得较好的图像分类效果.     

局部对称Bochner—Kaehler流形的实超曲面

本文把M.Kon关于复射影空间CP~n上紧致极小实超曲面的截面曲曲率的Pinching定理推广到局部对称Bochner—Kaehler流形上去。