一种新的基于分形特征的人造目标快速检测算法

从分形布朗模型出发,通过对自然背景和人造目标图像不同的灰度相关性分析,提出 一种新的分形特征提取算法,根据新算法得到的分形特征分布规律,进而形成一种简单有效的自然背景中人造目标的检测算法。实验结果说明了该算法的性能。     

自然背景中人造目标的自适应检测

本文介绍了一种在可见光和红外图像中，自动检测自然背景中人造目标的方法。该方法以分形结构做为自然背景的数学模型，利用自然背景和人造目标在分形特征上的固有相对差异检测人造目标，本文还介绍了自适应分形特征增强和组合图像检测方法，并以对实际图像的处理的结果，说明本文介绍的人造目标检测方法的效果。     

基于分形模型的人造目标检测技术

介绍一种在红外和可见光图像中，自动检测自然背景中人造目标的方法．该方法以分形结构做为自然背景的数学模型，利用自然背景和人造目标在分形模型上的固有差异检测人造目标．本文还介绍了自适应分形特征的增强及图像检测方法；并以实际图像的处理结果，说明人造目标检测方法的效果．     

基于分形的红外图象目标自动检测

本文介绍了一种基于分形的复杂背景下红外图象目标自动检测方法,该方法以自然背景和人造目标在分形特征上的固有的差异为依据,利用了人造目标分形特征拟合误差大的特点,实现了复杂背景中红外图象目标的自动检测。实际检测的实验结果表明：该目标自动检测方法对环境的适应性强,是一种颇具发展前景的目标自动检测方法。     

基于分形的实现小目标检测的一种具体方法

介绍了基于分形的复杂背景下实现小目标检测的一种具体方法，该方法以自然背景和人造目标在分形特征上的固有差异为依据，利用了人造目标分形特征数大的特点，给出了进行小目标检测的具体实现过程。实际检测的实验结果表明：该小目标检测方法对环境的适应性强，检测结果精确，颇具发展前途。     

Efficient computation of the Hutchinson metric between digitized images

The Hutchinson metric is a natural measure of the discrepancy between two images for use in fractal image processing. An efficient solution to the problem of computing the Hutchinson metric between two arbitrary digitized images is considered. The technique proposed here, based on the shape of the objects as projected on the digitized screen, can be used as an effective way to establish the error between the original and the, possibly compressed, decoded image. To test the performance of our method, we apply it to compare pairs of fractal objects, as well as to compare real-world images with the corresponding reconstructed ones.     

基于分形曲面尺度斜率特征的弱小目标检测

鉴于弱小目标检测所固有的难点及常用的检测方法不能准确、稳定地检测出目标,提出了运用分形曲面尺度斜率特征检测弱小目标的方法。通过实际数据分析可以得出:相比常用的分形维数和分形拟合误差等检测特征,分形曲面尺度斜率特征在表征人造目标与自然背景的差异上更加明显,在抗图像噪声干扰上也更为优异,有着更强的鲁棒性。该方法普遍适用于检测自然环境中的弱小目标,尤其在对空弱小目标方面,检测概率更高。无论背景、飞行姿态、目标类型发生怎样的变化,经本文算法运算后只需一步简易的分割就可以检测出微弱暗小目标。     

流媒体中的实时语义检测

流媒体中基于语音的实时语义检测是信息安全的重要方面,在分析研究基于音频的内容检索技术基础上,提出了一种适合于流媒体中基于语音的实时语义检测方法,该方法通过预判决和二次匹配两个过程,利用特征值前向序列对视频的语音特征进行加权匹配.实验结果表明,利用该匹配算法能满足语音实时语义检测的要求,可以更好地获得流媒体检索所需要的实时性.     

IMAGE OBJECT DETECTION BASED ON FRACTIONAL BROWNIAN MOTION

Fractional Brownian motion, continuous everywhere and differentiable nowhere, offers a convenient modeling for irregular nonstationary stochastic processes with long-term dependencies and power law behavior of spectrum over wide ranges of frequencies. It shows high correlation at coarse scale and varies slightly at fine scale, which is suitable for and successful in describing and modeling natural scenes. On the other hand, man-made objects can be constructively well described by using a set of regular simple shape primitives such as line, cylinder, etc. and are free of fractal. Based on the difference, a method to discriminate man-made objects from natural scenes is provided. Experiments are used to demonstrate the good efficiency of developed technique.     

基于分形特征融合的目标边缘检测算法

针对复杂背景下目标边缘检测问题,研究了分形在目标检测中的应用,提出了基于分形特征融合的目标边缘检测算法。首先求取图像的分形维数和几何度量空间变换率,然后运用Dempster-Shafer证据理论对分形维数和几何度量空间变换率进行融合处理,对图像像素分类,从而得到目标边缘,最后细化边缘得到目标检测结果。实验表明,该算法能够克服复杂背景的干扰,有效地提取目标边缘。     

IMAGE OBJECT DETECTION BASED ON FRACTIONAL BROWNIAN MOTION

Fractional Brownian motion, continuous everywhere and differentiable nowhere, offers a convenient modeling for irregular nonstationary stochastic processes with long-term dependencies and power law behavior of spectrum over wide ranges of frequencies. It shows high correlation at coase scale and varies slightly at fine scale, which is suitable for and successful in describing and modeling natural scenes. On the other hand, man-made objects can be constructively well described by using a set of regular simple shape primitives such as line, cylinder, etc. and are free of fractal. Based on the difference, we provide a method to discriminate man-made objects from natural scenes. Experiments are used to demonstrated the good efficiency of the developed technique.     

基于分形技术的目标检测算法研究

分形特征参数对于自然背景和人造目标存在着本质差别,充分利用这些差别,能为目标检测提供一种新思路,就分形技术在数字图像处理领域中目标检测方面的应用进行了讨论。分形的应用总是以各种分形参数为中介,有时完全依赖于分形参数,有时将分形参数和其他参数相结合,有时将分形方法和其他方法相结合,在处理的不同阶段分别采用分形方法和其他方法。对几种分形特征参数的估算方法及其在目标检测中的应用进行了研究。     

谐波信号频率的混沌检测方法

该文针对实际中大量存在的未知谐波信号频率,提出用非线性混沌动力学系统检测的方法;先从非线性方程理论出发论证系统存在周期解;然后建立仿真模型,用定向过零技术计算谐波频率;经过对计算结果绝对误差与相对误差的分析,得出该项技术是可行的。同时提出在检测频率后,进一步检测谐波幅值的可能性。     

地震信号空间序列分形特征的研究

当把地震勘探信号各道间对应于同一时刻的数据序列看作是反映一定范围内地层及其地质参数的复杂函数时,便构成了地震勘探信号的空间序列。本文建立了地震信号空间序列的分形模型,将根据一维时间序列重建动力学系统的理论和时间序列关联维计算的G.P算法拓展到对地震信号空间序列的研究之中,提出了地震信号空间序列关联维的分析方法,对地震信号空间序列的分形特征进行了研究,结果表明,地震信号空间序列在一定的尺度范围内为分形结构,并存在较宽的分形无标度区。这一结论为应用分形理论到地震信号空间序列的研究提供了思路,若能将对地震勘探信号时深序列的研究和空间序列的研究相结合,有可能取得分形理论在地震勘探领域应用研究的新突破。     

基于兴趣区检测的地面目标识别方法研究

提出了一种基于兴趣区检测的地面目标识别方法。根据目标模型的有关几何形状和热辐射知识,采用自上而下的计算模型,计算所提取的基元特征的显著性,并根据基元特征的显著性的大小确定出注视点及相应的兴趣区,然后对兴趣区中的图像进行分割,并利用模型知识对分割图像进行识别。该方法对已有的红外目标图像系列取得了很好的识别效果,提高了识别精度和弱小目标检测识别的能力。     

Reduced reference image quality assessment using regularity of phase congruency

In this paper, a reduced-reference image quality assessment metric is proposed, which measures the difference of the regularity of the phase congruency (PC) between the reference image and the distorted image. The proposed model adopts a three-stage approach. The PC of the image is first extracted, then the fractal dimensions are computed on PC as the image features that characterize the image structures from the view of the spatial distribution. Finally the image features are pooled as the quality score using ℓ₁ distance. The proposed approach is evaluated on seven public benchmark databases. Experimental results have demonstrated the excellent performance of the proposed approach.     

基于分形特征的目标检测算法概述及仿真

对目前比较常用的基于分形特征的目标检测算法进行了概述，并给出了相应的仿真结果。从仿真结果我们看出基于多尺度分形参数的目标检测算法是一种比较精确的算法，但是计算量太大。从计算量和精确度双方面考虑快速的基于分维数和分形拟合误差的目标检测算法是一种不错的算法。     

基于热传导理论的背景抑制算法应用研究

针对复杂背景中红外运动目标检测速度慢,检测概率低的问题,对红外图像的背景和目标特性进行了分析,提出了一种基于热传导方程和自适应阈值的红外目标实时检测算法。首先,依据红外图像背景具有各向同性的分形特性,确定采用各向同性的算子对背景进行估计;然后,根据热传导方程和反热传导方程在图像处理中的应用,推导出了用于红外目标检测的背景抑制算子,在此基础上设计了自适应阈值用于红外目标的检测;最后给出了该算法的实验结果,并与基于特征选择性滤波的检测算法做了比较。实验结果表明该算法具有良好的背景抑制和目标增强性能,大幅提升了图像的信噪比,提高了红外运动目标的检测概率,且结构简单,有利于硬件实时实现。     

声波测井信号的分形特征研究

许多研究认为，测井信号在纵向上的分布具有分数高斯噪音（ｆＧｎ）特征。如果对不同的地层套用相同的模型就会导致输出结果与实际情况不符。本文根据实际资料，利用Ｒ／Ｓ分析、谱密度分析等手段对声波时差测井信号分段进行了分析。结果发现，声波时差测井信号在纵向上的分布可以同时服从分数高斯噪音（ｆＧｎ）和分数布朗运动（ｆＢｍ）。由于分形模型的判别是一切分形地质统计研究的基础，因此，本文的研究对声波测井信号的分析具有较高的实际意义。     

A practical metric for soft error vulnerability analysis of combinational circuits

Besides the advantages brought by technology scaling, soft errors have emerged as an important reliability challenge for nanoscale combinational circuits. Hence, it is important for vulnerability analysis of digital circuits due to soft errors to take advantage of practical metrics to achieve cost-effective and reliable designs. In this paper, a new metric called Triple Constraint Satisfaction probability (TCS) is proposed to evaluate the soft error vulnerability of combinational circuits. TCS is based on a concept called Probabilistic Vulnerability Window (PVW) which is an inference of the necessary conditions for soft-error occurrence in the circuit. We propose a computation model to calculate the PVW’s for all circuit gate outputs. In order to show the efficiency of the proposed metric, TCS is used in the vulnerability ranking of the circuit gates as the basic step of the vulnerability reduction techniques. The experimental results show that TCS provides a distribution of soft error vulnerability similar to that obtained with fault injections performed with HSPICE or with an event driven simulator while it is more than three orders of magnitude faster. Also, the results show that using the proposed metric in the well-known filter insertion technique achieves up to 19.4%, 34.1%, and 55% in soft error vulnerability reduction of benchmark circuits with the cost of increasing the area overhead by 5%, 10%, and 20%, respectively.