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.     

Rotation-invariant pattern recognition using morphological phase-only correlation

We present a rotation-invariant nonlinear correlator based on the circular harmonic filter (CHF) and the previously proposed morphological phase-only correlator (MPC) [Q. Wang, S. Liu, Opt. Commun. 244 (2005) 93]. We refer to this correlator as a rotation-invariant MPC (RIMPC). Through computer simulation, we compare the output results of RIMPC 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 RIMPC 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.     

旋转不变振幅位相合成圆谐滤波器

提出了振幅位相合成圆谐滤器用于旋转不变模式识别，该滤波器的振幅是圆谐滤波器的振幅谱加偏移量后的逆函数，位相是圆谐滤波器的位相谱。计算机模拟实验证明，与传统的圆谐滤波器和纯位相圆谐滤波器相比，振幅位相合成圆谐滤波器具有更好的识别能力，能产生更尖锐的相关峰和更大的输出信噪比。     

Modified nonlinear morphological correlation using Laplace filtering

A Laplace filtering based morphological correlation (LBMC) is proposed as a modified approach to improve the properties of the classical morphological correlation (MC). For the LBMC, power spectrum of Laplace operator is introduced to filter the joint power spectrum (JPS) of the MC before final inverse Fourier transform. Computer simulation results show that, as compared with the linear correlation (LC) and the conventional MC, LBMC provides better discrimination capability with sharp and unmistakable correlation signal and its performance metrics are more stable when input scene is corrupted by outlier noise (salt-and-pepper noise) and additive white Gaussian noise. Although the LBMC is not illumination invariant when multiplication illumination factor is larger than unity, considerable discrimination capability is still obtained.     

Binary phase only filters for rotation and scale invariant pattern recognition with the joint transform correlator

The joint transform correlator (JTC) is one of the two main optical image processing architecture which provides a highly effective way of comparing images in a wide range of applications. Traditionally, an optical correlator is used to compare an unknown input scene with a pre-captured reference image library, to detect if the reference occurs within the input. Strength of the correlation signal decreases rapidly as the input object rotates or varies in scale relative to the reference object. The aim of this paper is to overcome the intolerance of the JTC to rotation and scale changes in the target image. Many JTC systems are constructed with the use of ferroelectric liquid crystal (FLC) spatial light modulators (SLMs) as they provide fast two-dimensional binary modulation of coherent light. Due to the binary nature of the FLC SLMs used in the JTC systems, any image addressed to the device need to have some form of thresholding. Carefully thresholding the grey scale input plane and the joint power spectrum (JPS) has significant effect on the quality of correlation peaks and zero order (DC) noise. A new thresholding technique to binarise the JPS has been developed and implemented optically. This algorithm selectively enhances the desirable fringes in the JPS which provide correlation peaks of higher intensity. Zero order noise is further reduced when compared to existing thresholding techniques.Keeping in mind the architecture of the JTC and limitations of FLC SLMs, a new technique to design rotation and scale invariant binary phase only filters for the JTC architecture is presented. Filers design with this technique have limited dynamic range, higher discriminability among target and non-target objects, and convenience for implementation on FLC SLMs. Simulation and experiments shows excellent results of various rotation and scale invariant filters designed with this technique. A rotation invariant filter is needed for various machine vision applications of the JTC. By fixing the distance between camera and input object, the scale sensitivity of the correlator can be avoided. In contrast to the industrial machine vision applications, scale factor is very important factor for the applications of the JTC systems in defence and security. A security system using a scale invariant JTC will be able to detect a target object well in advance and will provide more time to take a decision.     

用相关矩阵特征判据法实现三重不变光学图像识别

本文首先在K-L变换中用相关矩阵作为特征判据进行特征压缩,然后用综合判别函数法制备空间综合匹配滤波器,可以有效地压缩特征图像数目.用这种滤波器实现了平移、旋转、尺度三重不变光学图像识别,且有较高的信噪比.     

Digital system of invariant correlation to position and rotation

A new correlation digital system invariant to position and rotation is presented. This new algorithm requires low computational cost, because it uses uni-dimensional signatures (vectors). The signature of the target so like the signature of the object to be recognized in the problem image is obtained using a binary ring mask constructed based on the real positive values of the Fourier transform of the corresponding image. In this manner, each image will have one unique binary ring mask, avoiding in this form the relevant information leak. Using linear and non-linear correlations, this methodology is applied first in the identification of the alphabet letters in Arial font style and then in the classification of fossil diatoms images. Also, this system is tested using the diatom images with additive Gaussian noise. The non-linear correlation results were excellent, obtaining in this way a simple but efficient method to achieve rotation and translation invariance pattern recognition.     

Shift,Rotation and Limited Scale Invariant Pattern Recognition Using Synthetic Discriminant Functions

Making use of the idea of synthetic discriminant functions (SDF), a simple way to synthesize a shift, rotation and limited size correlation filter is proposed. The SDF is synthesized by superimposing four 2nd order circular harmonics of a training reference pattern in 4 different sizes. Computer simulation experiments have shown that the filter is indeed shift, full rotation and limited size invariant over a size range from 1 to 1.82. The invariant range can be increased if more training patterns are used.     

光电混合综合判别相关器

论述了综合判别函数（ＳＤＦ）的基本理论，简明地给出了用综合判别函数实现畸变不变相关的技术途径。在此基础上，构造了一个光电混合综合判别相关器实验系统，并用它实现了三重（位移、旋转、尺度）畸变不变实时相关识别。     

Shift- and scale-invariant pattern recognition using morphological phase-only correlation

We propose a non-linear correlation to realize scale-invariant recognition, called shift- and scale-invariant morphological phase-only correlation (SSIMPC), which is a combination of radial harmonic filter (RHF) and morphological phase-only correlation (MPC). We form SSIMPC using phase-only RHFs (PORHF) instead of common RHFs in every sub-correlations of morphological radial harmonic correlation (MRHC). Computer simulation results demonstrate that the performance of SSIMPC is better than that of MRHC in discrimination and robustness against non-Gaussian noise, such as salt-and-pepper noise and cluttered background, and above of all, SSIMOC realizes approximately full scale-invariance. In addition, we devise an optoelectronic scheme, which is identical to the setup for MPC, to implement SSIMPC.     

基于傅里叶-极坐标变换的相关结果后处理技术

在光学相关识别中,对相关输出结果进行后处理是保证图像识别准确性的关键之一。研究者通常采用神经网络技术对相关输出结果进行处理,并取得了较好的效果,但它需要预先准备大量的相关峰和噪声峰训练样本。根据极坐标变换能将笛卡尔坐标系下的旋转转变成平移的性质以及傅里叶变换的平移不变性,提出一种基于傅里叶-极坐标变换的相关结果后处理方法。验证结果表明:目标的旋转图像与目标自身的傅里叶-极坐标变换的相似度较大,而干扰图像与目标的傅里叶-极坐标变换的相似度较小。本文的方法在后处理阶段可对目标和干扰进行有效的分类识别,而且还能避免对相关峰和噪声峰训练样本的收集,从而使得光学相关识别系统的应用更加便利。     

Distortion Invariant Binary Image Recognition Based on Central Projection Correlation

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用雅可比-傅里叶矩进行图像描述

提出了雅可比—傅里叶矩,它是用雅可比多项式作为径向函数,用傅里叶因子作为角向函数构造而成的,是广义正交傅里叶—梅林矩。经过归一化处理后的雅克比—傅立叶矩具有平移、尺度、旋转、灰度多畸不变性。从归一化图像重建误差、噪声灵敏度等方面对雅可比—傅里叶矩的图像描述能力进行了研究,结果显示雅可比—傅里叶矩在各种参量选择的情况下,具有良好的图像描述和抗噪声能力,是一个优良的图像特征。以前的研究多为单纯构建某一个函数,而现在将其扩展为构建一个函数族,开阔了矩描述量的研究视野。     

Wavelet-based morphological correlation

A wavelet-based morphological correlation (WBMC) is proposed as a new architecture to improve the properties of the classical morphological correlation (MC). For the WBMC, a dilated wavelet intensity function is introduced to filter the joint power spectrum (JPS) of the MC before final inverse Fourier transform. Computer simulation results show that, as compared with the linear correlation (LC), the conventional MC and the joint wavelet transform correlation (JWTC), the WBMC provides better discrimination capability with sharp and unmistakable correlation signal and its performance metrics are more stable under input outlier noise (salt-and-pepper noise). Although the WBMC loses illumination-invariance when input illumination factor is larger than unity, considerable discrimination capability is still maintained.     

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.     

利用不确定性提高击中击不中变换的抗畸变能力

对用于目标识别技术的形态学击不中变换进行了分析,引入不确定和模式的概念使得容易受到畸变干扰的像素点对判别结果的作用降低,得到联合排序多值击中击不中变换,从而在不损失图像信息的前提下,提高了目标识别的抗畸变能力,并且具有在输入图像和参考模板之间进行精确匹配的能力。基于非相干相关器,利用新颖的多值互补编码方法,实现了二值图像的联合序多值击中击不中变换,达到了较好的实验结果。     

基于矩和小波变换的目标图象识别

在图象目标的识别中,目标图象的不变矩对于具有旋转、尺度变换特性的目标具有良好特征匹配性能,但其识别准确度直接受到不变矩计算区域内的目标个数的影响.本文首先采用一维小波变换降噪并获取图象边缘,结合轮廓跟踪法(“爬虫法”)与投影法对图象进行区域分割获取各目标存在区域,再利用目标图象的不变矩进行目标识别,最后通过利用目标的主轴特性及质心变化计算目标的旋转角度及尺度变换倍率.通过仿真实验,证明方案可行且具有良好的识别性能.     

Shift- and scale-invariant pattern recognition using morphological fringe-adjusted joint transform correlation

A shift- and scale-invariant version of morphological fringe-adjusted joint transform correlation (MFJTC) is proposed in this paper, which we call shift- and scale-invariant MFJTC (SIMFJTC). SIMFJTC is combination of MFJTC and conventional radial harmonic filter (RHF). Using computer simulation, we compare the output results of SIMFJTC with those of morphological radial harmonic correlation (MRHC) and SDF-based FPFJTC when input scene is corrupted by salt-and-pepper noise and cluttered background. Our results show that SIMFJTC has higher discriminability and displays better stability against salt-and-pepper noise and cluttered background. Moreover, scale-invariance of SIMFJTC is much stricter than MRHC and SDF-based FPFJTC.     

Invariant Pattern Recognition Using Neural Networks Combined with Optical Wavelet Preprocessor

A novel pattern-recognition system that is invariant against scale-, position- and rotation-changes is proposed. The system is composed of an array of modular neural networks with local space-invariant interconnections (FELSI) [Appl. Opt. 29 (1990) 4790] and a multiwavelet transform preprocessor. The wavelet decomposition of two-dimensional patterns is optically realized by the VanderLugt correlator. To obtain the multiwavelet transforms simultaneously, we synthesize a correlation filter of multiwavelets using computer-generated holograms. The learning process of the FELSI with the techniques of additional noise and weight decay is shown to contribute to the invariant recognition of the system.