基于Matlab仿真的联合变换相关器实验

利用Matlab软件平台对JTC进行了图像识别仿真实验,得到了二维及三维相关输出图像,显示出尖锐的相关峰.仿真实验表明:联合变换进行图像识别中存在强烈的零级干扰,干扰信号是有用信号的7.14倍,输出结果不理想;采用功率谱相减法可以消除零级强干扰,进而提高了图像的识别能力.     

基于微分滤波的联合变换相关

分析了微分滤波对联合变换功率谱的影响，得到了一种称之为功率谱形式的微分滤波器，该滤波器的滤波函数为一实数并且有一定的特点，因此很容易制得，计算机模拟表明，功率谱微分滤波联合变换相关器在相关峰值，旁瓣比，半宽度等方面比一般联合变换相关器有显著的改善，可以大大提高相关图像识别的可靠性，文中给出了实验结果，与模拟计算结果吻合，引入一个新参数可以更好地描述相关图像识别中的相关性。     

基于功率谱相减实现无零级衍射的联合变换相关器

联合变换相关器的零级衍射光斑会影响相关峰探测。将参考目标和待识别目标的联合功率谱与参考目标本身的功率谱做相减运算可消除零级衍射光斑,可提高联合变换相关器的识别能力。通过计算机仿真和实验验证了该方法的可行性。     

多目标识别的联合变换相关器的研究

提出了一种可用于多目标识别的联合变换相关器。为改善相关信号的性能,对功率谱作了优化处理。为消除相关面上的零级项和目标间的相关项,可用联合功率谱减去纯目标输入的功率谱和参考图像的功率谱;为增强和锐化相关峰,将相减的功率谱作指数函数滤波处理。分析了指数滤波参数对相关结果的影响。计算机模拟结果表明,这种相关器所输出的相关信号比经典联合变换相关器和二元联合变换相关器输出的相关信号更好,互相关得到了抑制,自相关得到了增强,具有很好的抗噪能力。     

使用振幅调制和功率谱相减的联合变换相关器作多目标检测

提出一种使用振幅调制和功率谱相减的联合变换相关器作多目标检测，这一方法对联合功率谱作了修正，先将联合功率谱减去纯输入景物的功率谱和参考图象的功率谱，再将所得修正的联合功率谱乘以振幅调制滤波函数。分析和量化了输入景物噪声对联合变换相关器性能的影响。这种方法比条纹调节的联合变换相关器和修正的条纹调节的联合变换相关器能产生更好的相关输出和适应输入景物噪声的能力。     

一种基于功率谱对数变换的联合变换相关器

联合功率谱的高频成份对相关峰识别具有重要作用,实验中记录的联合功率谱随着频率的增加其强度迅速降低。为增强联合功率谱的高频成分,改善其调制程度,可对功率谱进行对数变换,从而有效保留更多的高频成份,压低零级衍射光斑,提高联合变换相关器的识别能力。     

非准确联合变换相关

分析了联合变换相关中相关物或联合功率谱输入面偏离透镜前焦面的情况,或记录联合功率谱时透过两相关物的两相干光束间存在常数相位差时的情况,类似的非准确傅里叶变换被称为非准确联合变换相关。理论分析和实验验证表明,该种情况的相关点分布不变。     

基于光学小波微分预处理的联合变换相关目标识别

提出了一种基于光学小波变换微分预处理的光电混合联合变换相关系统,该系统利用同一光路可实时实现对输入图象的小波变换微分预处理和联合变换相关识别.实验结果表明:该系统能得到很尖锐的互相关峰,可显著提高联合变换相关器的识别能力.     

基于联合子波变换的目标轮廓抽取技术

子波变换是目标特征抽取的有效方法。本文把联合变换相关技术与子波变换结合起来，建立了一套新的子波变换光学实验系统。推导了联合子波变换的基本理论，并给出了联合子波换相关器在目标特征抽取应用中的实验结果。     

仅用参考物功率谱取阈的二元联合变换相关器

提出一种用特征物功率谱取阈值的新型二元联合变换相关器。与传统的联合变换相关器相比较,它有高光能效率、高鉴别率和输入空间宽带的优点;与现行的二元联合变换相关器相比较,它有鉴别率高和不需靶物功率谱测量的特点,因而是一种简便和高性能的相关器。     

基于联合变换相关的机载航空相机像移测量

为了提高像移测量精度,针对机载航空摆扫相机提出一种坐标变换法和图像相关法相结合的像移测量方法。利用坐标变换法得到初始像移速度,利用图像联合变换相关法对像移速度残差进行补偿。联合图像通过一个面阵CCD获得,该面阵CCD放置在相机焦平面上并与成像线阵TDI CCD平行,其输出的当前图像与参考图像合并构成联合图像进行二维空间联合变换相关运算,得到像移修正矢量。对该矢量分别在相机摆扫方向和载机飞行方向进行分解,从而得到摆扫像移和前向像移的修正量。仿真实验结果表明,在输入图像信噪比为4 dB时,像移测量误差在0.1 pixel以内。     

Performance of joint transform correlation under varying illumination and noise

The effect of varying the input scene illumination and noise on the performance of binary and fringe-adjusted joint transform correlators (JTCs) has been investigated in this paper. Simulation results show that the fringe-adjusted JTC yields superior correlation output when compared with the binary JTC for all types of input scenes affected by both illumination and noise. Furthermore, the binary JTC has been found to yield a good correlation output when zero mean, unity variance Gaussian white noise is added to input scenes affected by poor illumination.     

Optical security system employing shifted phase-encoded joint transform correlation

A new optical security system is proposed using a shifted phase-encoded joint transform correlation (JTC) architecture. In the proposed technique, at first, the address code is fed into two channels where one channel is shifted by 180°. The output signals from both the channels are phase-masked and then added with the input image to be encrypted. The joint power spectrum (JPS) obtained from one channel is subtracted from the JPS of the other channel, and the modified JPS is inverse Fourier transformed to yield the encrypted image. For decryption, the received signal is Fourier transformed and multiplied by the phase mask and the address code, which is then inverse Fourier transformed to generate the output signal. The proposed technique does not require complex conjugate of the address code otherwise required in the classical double random phase encryption. Also the decryption result is much more enhanced when compared to the output generated by alternate JTC techniques. Computer simulation results verify that the encryption and decryption are very much secure and efficient in both noise-free and noisy conditions.     

基于特征点的整数小波目标跟踪方法的研究

基于提升方法的整数小波变换的诸多优点,以Harr整数小波变换为例,提出了动态目标跟踪算法。对标准图像进行整数Harr小波变换,并将提升项取整,对提升项的参数用一定数量训练图像进行学习。选取在训练参数平方和为最小意义上的、使整数小波变换后的图像高频部分具有较大值的点作为特征点。对包含有目标的参考图像进行整数小波变换,选择高频分量具有较大值的点,利用训练过的提升项参数使目标和基准图像配准。由于算法采用整数小波变换,使提取具有较好的鲁棒性,从而实现目标发生旋转、平移及尺度变化等的跟踪过程。仿真实验表明,该方法能对动态运动目标进行跟踪。     

Rotation-invariant pattern recognition using morphological fringe-adjusted joint transform correlation

We propose a rotation-invariant nonlinear correlator based on circular harmonic filter (CHF) and morphological Fringe-adjusted Joint Transform correlation (MFJTC). We refer to this correlator as a rotation-invariant MFJTC (RIMFJTC). Through computer simulation, we compare the output results of RIMFJTC with those of rotation-invariant MC (RIMC) and CHF when input scene is corrupted by salt-and-pepper noise, white additive Gaussian noise and cluttered background. Our results show that RIMFJTC yields higher discriminability, sharper and higher correlation peaks, and displays better stability against the above three kinds of noise than do the RIMC and common CHF.     

基于小波变换的新型数字散斑相关方法

通过将小波分析的方法应用到传统的空域相关计算中,提出了基于小波变换的数字散斑相关方法。详细介绍了该方法的原理,并对传统空域相关方法与小波相关方法进行了实验计算比较。实验结果表明,与传统的空域相关方法相比,基于小波变换的数字散斑相关方法大大提高了相关计算的精度,精度由0 05像素提高到了0 01像素以下。最后利用该方法测量了编织结构复合材料板在三点弯曲载荷作用下的位移场。     

Pattern recognition in hyperspectral imagery using one-dimensional shifted phase-encoded joint transform correlation

Pattern recognition in hyperspectral imagery is a challenging issue because of the high false alarm rate and computation complexity. In this paper, a one-dimensional shifted phase-encoded fringe-adjusted joint transform correlation (SPFJTC) technique is developed for hyperspectral image processing system. The proposed technique processes the reference spectral signature using a random phase mask and correlates it with the spectral signature corresponding to each pixel of the unknown input hyperspectral image cube using a simple architecture. This technique generates very high discrimination between the object of interest and background clutter. Computer simulation results using real life hyperspectral imagery show that the proposed SPFJTC technique can effectively recognize the objects of interest while alleviating the effects of false alarms and other artifacts.