New Riemannian techniques for directional and tensorial image data

This paper develops new geometrical filtering and edge detection algorithms for processing non-Euclidean image data. We view image data as residing on a Riemannian manifold, and we work with a representation based on the exponential map for this manifold together with the Riemannian weighted mean of image data. We show how the weighted mean can be efficiently computed using Newton's method, which converges faster than the gradient descent method described elsewhere in the literature. Based on geodesic distances and the exponential map, we extend the classical median filter and the Perona-Malik anisotropic diffusion technique to smooth non-Euclidean image data. We then propose an anisotropic Gaussian kernel for image filtering, and we also show how both the median filter and the anisotropic Gaussian filter can be combined to develop a new edge preserving filter, which is effective at removing both Gaussian noise and impulse noise. By using the intrinsic metric of the feature manifold, we also generalise Di Zenzo's structure tensor to non-Euclidean images for edge detection. We demonstrate the applications of our Riemannian filtering and edge detection algorithms both on directional and tensor-valued images.     

Multi-spectral fusion for surveillance systems

Surveillance systems such as object tracking and abandoned object detection systems typically rely on a single modality of colour video for their input. These systems work well in controlled conditions but often fail when low lighting, shadowing, smoke, dust or unstable backgrounds are present, or when the objects of interest are a similar colour to the background. Thermal images are not affected by lighting changes or shadowing, and are not overtly affected by smoke, dust or unstable backgrounds. However, thermal images lack colour information which makes distinguishing between different people or objects of interest within the same scene difficult.By using modalities from both the visible and thermal infrared spectra, we are able to obtain more information from a scene and overcome the problems associated with using either modality individually. We evaluate four approaches for fusing visual and thermal images for use in a person tracking system (two early fusion methods, one mid fusion and one late fusion method), in order to determine the most appropriate method for fusing multiple modalities. We also evaluate two of these approaches for use in abandoned object detection, and propose an abandoned object detection routine that utilises multiple modalities. To aid in the tracking and fusion of the modalities we propose a modified condensation filter that can dynamically change the particle count and features used according to the needs of the system.We compare tracking and abandoned object detection performance for the proposed fusion schemes and the visual and thermal domains on their own. Testing is conducted using the OTCBVS database to evaluate object tracking, and data captured in-house to evaluate the abandoned object detection. Our results show that significant improvement can be achieved, and that a middle fusion scheme is most effective.     

网络入侵检测系统的最优特征选择方法

用于网络入侵检测系统(IDS)的特征(变量)数量太多或太少都会降低IDS识别入侵者的正确率。为解决这一矛盾,提出一种选择最优特征的方法。计算每个特征或组合成的新特征对IDS的“贡献”值,选择少量“贡献”值较大的特征(最优特征)作为IDS识别入侵者的特征,既减少特征数量又基本保留了原始特征组所提供的信息。实验证明该方法实用且识别入侵者的正确率较高。     

基于Snort的IPv6入侵检测技术

针对开源入侵检测系统Snort没有提供对IPv6协议的AH和ESP扩展首部支持的问题,提出利用Snort检测ESP加密报文的解决方案。构造ESP检测规则,在Snort协议分析模块加入DecodeESP()函数并添加密钥管理模块,实现Snort对IPv6报文中ESP扩展报头的解析,管理其产生的密钥。给出一种面向ESP的入侵检测系统模型,以验证IPv6加密通信入侵检测的可行性,并给出实验验证过程。     

一种新的型可变r的动态匹配算法r可变动态匹配算法

鉴于动态r匹配算法对为了提高入侵检测系统的性能的重要性,通过研究内分泌系统对生物体内环境的调节机制,提出一个内分泌激素浓度的动态平衡模型及一种基于激素浓度调节的动态r匹配算法。仿真实验结果表明,该匹配算法能根据网络状态的变化,自适应地调整匹配参数,具有较好的匹配效果。     

Edge detection insensitive to changes of illumination in the image

In this paper we present new edge detection algorithms which are motivated by recent developments on edge-adapted reconstruction techniques [F. Aràndiga, A. Cohen, R. Donat, N. Dyn, B. Matei, Approximation of piecewise smooth functions and images by edge-adapted (ENO-EA) nonlinear multiresolution techniques, Appl. Comput. Harmon. Anal. 24 (2) (2008) 225–250]. They are based on comparing local quantities rather than on filtering and thresholding. This comparison process is invariant under certain transformations that model light changes in the image, hence we obtain edge detection algorithms which are insensitive to changes in illumination.     

基于安全状态信息库的入侵检测系统研究

主要介绍了入侵检测系统的相关概念、分类和入侵检测算法,在对各种技术进行分析、比较的基础上提出了基于安全状态信息库的入侵检测系统。该系统通过建立系统安全状态数据库,在考虑入侵行为的同时也考虑到了受保护系统的防范能力;通过建立可疑主机数据库,对不同威胁级别的主机可以采用不同的检测和响应手段。     

变频机组小波分析早期故障检测系统的设计

变频机组是地空导弹武器电源系统中不可缺少的重要设备之一.为及时发现机组早期故障以保证正常战备,设计研制了基于小波分析的早期故障检测系统.证明了变频机组发生定子匝间短路时,定子电流中的3次和5次谐波会明显增大的现象,同时利用改进谐波小波提取了这两种谐波分量,并将其作为变频机组发生定子匝间短路时的故障特征分量,分别从软硬件两个方面阐述了系统的总体设计方案.某导弹旅对系统进行的实地试验结果表明,系统能够成功检测到故障特征分量,从而验证了系统的可行性.     

基于噪声倒谱阈值频谱估计的语音活动检测

针对低方差频谱估计的语音活动检测(VAD)中Welch频谱估计方法计算量大的问题,提出利用倒谱阈值方法估计VAD中的噪声功率谱.该方法在静音时期为噪声的倒谱设置阈值,利用快速傅里叶变换计算频谱,再更新VAD中的判决阈值.算法复杂度分析与仿真结果表明,该方法的检测性能与Welch方法相当,计算量降低约18%,同时降低整个...     

Besov空间的阈值收缩和边缘补偿的图像去噪

提出一种新的图像去噪方法,它是Besov空间的变分模型,在负实数次Sobolev空间上定义了数据项,用Besov半范数定义了正则项。并详细推导了变分模型在Besov空间的阈值求解公式,先做一个Contourlet变换域的小阈值收缩,然后再利用该模型去噪。去除噪声的同时也损失了部分边缘信息,把边缘分为四种情况,针对不同情况确定相应的边缘补偿方法。实验表明该模型具有良好的去噪效果。