旋转不变二值化联合变换相关识别方法

介绍一种基于二值化联合变换相关的综合鉴别函数算法，用等相关峰及Gram-schmidt展开获得的综合鉴别函数对迭代算法进一步优化，获得了适用于二值化联合变换相关的综合鉴别函数。采用这种算法制作的综合鉴别滤波器既有相关峰值高和相关峰尖锐等特点,又能识别旋转图像。计算机模拟结果表明：这种相关器在信噪比、光能效率、相关峰尖锐度和鉴别能力上都有一定程度地提高，实现了较高鉴别率旋转不变光学模式识别。     

用于畸变目标相关识别的匹配滤波器设计

针对尺度缩放和角度旋转变化目标相关识别率低的问题,采用优化折中综合鉴别函数方法设计了具有较大畸变容限的匹配滤波器。在匹配滤波器设计时,通过调整相关输出噪声、训练样本平均功率谱密度和训练样本相似度对训练样本平均值的贡献,得到具有带通性质的匹配滤波器。利用单个匹配滤波器对角度旋转0°～30°、尺度变化0～20%的目标进行了相关识别仿真实验。结果表明：这种匹配滤波器相关输出峰尖锐、鉴别力强、畸变容限大,可用于畸变目标的光学相关识别。     

水下光束漂移激光图像抗畸变识别

针对水下激光传输存在光束漂移现象,分析了光束漂移图像对光学相关识别的影响和帧间光束漂移图像噪声的不相关性。研究了抗畸变匹配滤波器设计,提出了基于相位匹配的畸变图像共同特征提取,并作为综合鉴别函数子特征的训练样本,设计了抗畸变综合鉴别匹配滤波器,并进行了仿真验证。Matlab仿真结果证明:抗畸变综合鉴别匹配滤波器设计,结合待识别图像随机散射噪声预滤波,可有效提高光学相关识别输出相关峰的峰值-相关能量比值和相关峰鉴别率值。     

常规综合鉴别函数的改进

针对常规综合鉴别函数制作的复合匹配滤波器，对不同旋转角度的训练样本输出的相关峰值波动性较大，不便于后续探测和处理等问题，对常规综合鉴别函数到编码匹配滤波器的编码转化过程进行分析，找到导致相关峰值波动的原因，提出改进方法，即在计算综合鉴别函数的线性组合系数时将编码运算也考虑进去，并根据滤波器对各训练样本相关峰值的倒数来确定线性组合系数。计算机仿真结果表明：改进后的方法降低了复合滤波器对不同旋转角度样本输出相关峰值的波动性，提高了相关峰自动判别的准确性。     

一种抗尺度畸变的光学相关图像识别技术

为提高光学相关图像识别的准确度和抗畸变能力,引入多重复合匹配滤波器概念,探讨一种新的识别思路。它融合了综合鉴别函数和K-L变换的经典思想,改善了单个匹配滤波器动态范围太小的状况,对图像尺度变换有较宽的适应范围,从而提高了光学模式识别系统的识别效率。仿真结果表明,对尺度缩小1/2的情况,采用此思路可使其滤波的刷新速度比常规方法提高4倍,从而验证了算法的有效性和可行性。     

非约束维纳滤波综合鉴别函数算法研究

通过去掉最小平均相关能量匹配滤波器推导过程中对输出相关峰值的约束条件,定义相关判据,得出畸变不变相关识别综合鉴别函数形式为维纳滤波函数,不仅抗噪能力强,且把非约束匹配滤波器畸变容限大的优点引入其中.模拟结果表明,所设计匹配滤波器畸变容限大、抗噪能力强.     

基于小波变换的OTSDF滤波器优化设计

提出基于小波变换的优化折中综合鉴别函数(OTSDF)滤波器(W_OTSDF)算法,对其进行优化设计,利用小波变换的多尺度与带通滤波特性,提高平面集成2f光学相关器的相关输出峰值强度和信噪比(SNR)。利用Matlab搭建系统的仿真模型,获取W_OTSDF滤波器的优化算法。仿真结果表明,在平面集成2f系统中对旋转、缩放以及旋转缩放同时存在的畸变目标进行识别时,优化设计的W_OTSDF滤波器较OTSDF滤波器在峰值相关能量比(PCE)、峰值旁瓣比(PSR)、SNR等整体性能指标方面均有较大提高。理论分析与仿真结果均表明W_OTSDF滤波器较OTSDF滤波器具有更好的识别效果。     

非约束纯相位空间匹配滤波器设计研究

根据非约束相关匹配滤波器和纯相位匹配滤波器的概念,分析了非约束纯相位匹配滤波器的特性及其在光学相关识别中的应用,并与其他典型纯相位匹配滤波器进行了比较.模拟和实验结果表明,非约束纯相位匹配滤波器具有畸变容限大、相关峰高、算法简单的特点.用于光学相关识别是一种实用的空间匹配滤波器算法.     

大尺度混合畸变的目标识别技术

为解决目标识别技术中混合畸变目标的识别难题,提出了一种面积极坐标变换算法.该算法利用相似图形尺寸与面积的满射函数关系和极坐标变换的旋转不变特性,对目标与模板同时进行面积变换和极坐标变换.将面积极坐标变换算法与基于形态学膨胀的边缘加粗处理算法相结合应用于光电混合联合变换相关器,实现了大尺度混合畸变目标的识别.以空中飞机为例,光学相关实验表明,该方法可显著提高混合畸变目标的相关峰强度,旋转畸变容差为0°~100°,比例畸变容差为100%~200%,具有较大尺度的混合畸变容差,扩大了识别范围.其混合畸变识别能力优于极坐标对数变换算法与综合鉴别函数法其衍生方法,其"时间复杂度"远小于综合鉴别函数及其衍生方法.     

光学相关器在自动目标识别中的应用

为使光学相关器更好地应用于自动目标识别领域,保证在较高识别速度下,具备良好的识别效果,介绍了应用于自动目标识别领域的联合变换相关器和匹配滤波相关器.针对匹配滤波相关器实现过程中的两项关键技术,在滤波器编码技术上提出了构造等相关峰综合鉴别函数以解决畸变不变识别问题.在相关峰识别技术上,针对阈值法存在的不足,提出利用BP神经网络对相关平面相关峰进行识别,采用划分感兴趣区域的方法减少了输入层神经元数目,简化了神经网络结构.借助搭建的光学相关器系统验证了该方法可对相关信号和噪声进行有效分类,提高了光学相关器的识别效果.     

非重叠背景噪声下的自适应维纳滤波模式识别方法

维纳滤波实现模式识别的关键问题是噪声知识的获得与估计。提出一种非重叠背景噪声的提取方法,首先将对噪声的粗略估计代入维纳滤波函数,得到相关峰。然后由相关峰的位置及参考图像的尺寸确定目标图像的位置和范围,从而提取出背景噪声图像。经过二次维纳滤波,得到改善的相关输出结果,实现了自适应过程。仿真结果表明在非重叠有色噪声环境下,与噪声估计法以及传统的维纳滤波方法相比,此方案具有较好的识别效果。     

Decision optimization for face recognition based on an alternate correlation plane quantification metric

We consider a new approach for enhancing the discrimination performance of the VanderLugt correlator. Instead of trying to optimize the correlation filter, or propose a new decision correlation peak detection criterion, we propose herein to denoise the correlation plane before applying the peak-to-correlation energy (PCE) criterion. For that purpose, we use a linear functional model to express a given correlation plane as a linear combination of the correlation peak, noise, and residual components. The correlation peak is modeled using an orthonormalized function and the singular value decomposition method. A set of training correlation planes is then selected to create the correlation noise components. Finally, an optimized correlation plane is reconstructed while discarding the noise components. Independently of the filter correlation used, this technique denoises the correlation plane by lowering the correlation noise magnitude in case of true correlation and decreases the false alarm rate when the target image does not belong to the desired class. Test results are presented, using a composite filter and a face recognition application, to verify the effectiveness of the proposed technique.     

Decoherence of flux qubits due to 1/f flux noise

We have investigated decoherence in Josephson-junction flux qubits. Based on the measurements of decoherence at various bias conditions, we discriminate contributions of different noise sources. We present a Gaussian decay function extracted from the echo signal as evidence of dephasing due to 1/f flux noise whose spectral density is evaluated to be about (10(-6)Phi0)2/Hz at 1 Hz. We also demonstrate that, at an optimal bias condition where the noise sources are well decoupled, the coherence observed in the echo measurement is limited mainly by energy relaxation of the qubit.     

The effect of ocean wave on the vertical spatial correlation of ocean ambient noise

The effect of the correlation function of noise sources derived from the ocean wave spectrum on the vertical spatial correlation of ocean ambient noise is investigated. The spatial correlation models of ocean ambient noise usually assume that the surface noise sources are uncorrelated. This assumption can be used to explain some physical phenomena, but it is not consistent with the real situation. Considering the relation between the ocean wave motion and the ambient noise generated by wind, the spectrum of ocean wave is introduced to calculate the vertical correlation of ocean ambient noise as the correlation function of noise sources by using the Kuperman-Ingenito(K/I) noise model. The comparison of the simulations and the experimental data shows that the simulations of vertical correlation of ambient noise have some differences with the experimental data by assuming the noise sources are uncorrelated and the simulations of vertical correlation of ambient noise have a good agreement with the experimental data by using the correlation function of noise sources derived from the ocean wave spectrum under the situation of high wind speed.     

海面波浪对海洋环境噪声垂直空间相关性的影响

研究了由海浪谱导得的噪声源的相关函数对海洋环境噪声垂直空间相关性的影响。常用的海洋环境噪声空间相关性模型一般假设海面噪声源是非相关的,这种假设可以解释一些物理现象,但是与真实情况并不符合。考虑到风成噪声与海浪运动的相关性,引入海浪谱,得到噪声源的相关函数,利用Kuperman-Ingenito (K/I)噪声模型,计算海洋环境噪声的垂直相关性。通过仿真结果与实验数据对比可以看出,在高风速下,假设噪声源不相关时计算得到的噪声场垂直相关性与实验结果相差较大,而利用由海浪谱导得的噪声源的相关函数计算得到的噪声场的垂直相关性与实验结果符合较好。     

Distortion-invariant pattern recognition using synthetic discriminant function based multiple phase-shifted-reference fringe-adjusted joint transform correlation

This paper proposes a novel pattern recognition system for invariance to noise and distortions. The technique first generates a synthetic discriminant function of the target image from its different distorted versions. It then takes four different phase-shifted versions of the reference image, which are individually joint transform correlated with the given input scene. Thus the proposed algorithm produces a single cross-correlation signal corresponding to each potential target. Also a fringe-adjusted filter is designed to generate a delta-like correlation peak with high discrimination between the signal and the noise. The pattern recognition system is also designed for the identification of multiple targets belonging to multiple reference objects simultaneously in a given input scene. The proposed technique is investigated using computer simulation including real-life images in different complex environments.