Distance-classifier correlation filters for multiclass target recognition

We describe a correlation-based distance-classifier scheme for the recognition and the classification of multiple classes. The underlying theory uses shift-invariant filters to compute distances between the input image and ideal references under an optimum transformation. The original distance-classifier correlation filter was developed for a two-class problem. We introduce a distance-classifier correlation filter that simultaneously considers multiple classes, and we show that the earlier two-class formulation is a special case of the classifier presented. Initial results are presented to demonstrate the discrimination- and distortion-tolerance capabilities of the proposed filter.     

Quadratic correlation filter design methodology for target detection and surveillance applications

A novel method is presented for optimization of quadratic correlation filters (QCFs) for shift-invariant target detection in imagery. The QCFs are quadratic classifiers that operate directly on the image data without feature extraction or segmentation. In this sense, the QCFs retain the main advantages of conventional linear correlation filters while offering significant improvements in other respects. For example, multiple correlators work in parallel to optimize jointly the QCF performance metric and produce a single combined output, which leads to considerable simplification of the postprocessing scheme. In addition, QCFs also yield better performance than their linear counterparts for comparable throughput requirements. The primary application considered is target detection in infrared imagery for surveillance applications. In the current approach, the class-separation metric is formulated as a Rayleigh quotient that is maximized by the QCF solution. It is shown that the proposed method results in considerable improvement in performance compared with a previously reported QCF design approach and many other detection techniques. The results of independent tests and evaluations at the U.S. Army's Night Vision Laboratory are also presented.     

Wiener-like correlation filters

We introduce a new, to our knowledge, design for a Wiener-like correlation filter, which consists of cascading a phase-only filter (POF) with a photorefractive Wiener-like filter. Its performance is compared with that of the POF and the Wiener correlation filter (WCF). Correlation results show that for intermediate and higher levels of noise this correlation filter has a peak-to-noise ratio that is larger than that of either the POF or the WCF while still preserving a correlation peak that is almost as high as that of the POF.     

Unconstrained correlation filters

A mathematical analysis of the distortion tolerance in correlation filters is presented. A good measure for distortion performance is shown to be a generalization of the minimum average correlation energy criterion. To optimize the filter's performance, we remove the usual hard constraints on the outputs in the synthetic discriminant function formulation. The resulting filters exhibit superior distortion tolerance while retaining the attractive features of their predecessors such as the minimum average correlation energy filter and the minimum variance synthetic discriminant function filter. The proposed theory also unifies several existing approaches and examines the relationship between different formulations. The proposed filter design algorithm requires only simple statistical parameters and the inversion of diagonal matrices, which makes it attractive from a computational standpoint. Several properties of these filters are discussed with illustrative examples.     

Target-adaptive polarimetric synthetic aperture radar target discrimination using maximum average correlation height filters

We report the development of a technique for adaptive selection of polarization ellipse tilt and ellipticity angles such that the target separation from clutter is maximized. From the radar scattering matrix [S] and its complex components, in phase and quadrature phase, the elements of the Mueller matrix are obtained. Then, by means of polarization synthesis, the radar cross section of the radar scatters are obtained at different transmitting and receiving polarization states. By designing a maximum average correlation height filter, we derive a target versus clutter distance measure as a function of four transmit and receive polarization state angles. The results of applying this method on real synthetic aperture radar imagery indicate a set of four transmit and receive angles that lead to maximum target versus clutter discrimination. These optimum angles are different for different targets. Hence, by adaptive control of the state of polarization of polarimetric radar, one can noticeably improve the discrimination of targets from clutter.     

光谱相关能级匹配遥感目标提取算法

光谱相关能级波形匹配算法是在利用光谱相关系数确定谱带强度相似度,能级波形匹配技术确定波形特征相似度的基础上,将二者的乘积作为图像光谱与参考光谱间的总相似度来进行的高光谱遥感和多光谱遥感精确目标提取算法.在阐述基本原理的基础上,首先模拟了大气、噪声等边界条件对算法相似度的影响;其次,利用400个图像测试光谱对算法进行了精度评价;最后,对4种端元进行了目标提取.边界条件模拟结果表明,算法对大气效应具有较好的抑制作用,当大气效应小于模拟数值的50倍时,可以省略大气订正.虽然算法对噪声较为敏感,但通过噪声消除算法可以将该影响减小到最低,甚至将其消除掉;精度评价结果表明,算法的平均精度为85%.     

基于宽带相关处理的水下运动目标检测

对于水下运动目标的检测,宽带相关处理是一种有效的方法.为了实时实现的需要,选择一类多普勒宽容信号为发射信号,给出了单尺度宽带相关处理器作为运动目标检测器.仿真结果证明,该检测器性能优良,且能够实时实现.     

Application of correlation filters for texture recognition

We propose a new statistical method to design spatial filters to recognize and to discriminate between various textures. Unlike existing correlation filters, the proposed filters are not meant to recognize specific shapes or objects. Rather, they discriminate between textures such as terrains, background surfaces, and random image fields. The filters do not require any on-line statistical computations for extracting texture information. Therefore optical (or digital) correlators can be used for fast real-time texture recognition without segmentation. The procedure is based on the assumption that textures can be modeled as stationary random processes over limited regions of an image. The optimum filter coefficients are determined by use of eigenvector analysis. Several examples are given to illustrate the proposed scheme.     

Gray-level computer-generated hologram filters for multiple-object correlation

Synthesis of gray-level computer-generated holograms allows for an increase of the information storage capability that is usually achieved with conventional binary filters. This is mainly because more degrees of freedom are available. We propose to profit from this feature by synthesizing complex filters formed by many superimposed holograms, each with a different carrier frequency. We apply these gray-level filters to perform multichannel correlation and in this way enhance the capability of optical correlators to process the information in parallel and simultaneously. First, we analyze the behavior of some performance criteria on the impulse response and on the correlation as a function of the number of holograms that are multiplexed. Then we show the results of two experiments: In the first a composed phase-only filter is used in a multiple-object recognition process. In the second a composed synthetic discriminant function filter is used to implement an object classification by means of a binary code.     

Biometric verification with correlation filters

Using biometrics for subject verification can significantly improve security over that of approaches based on passwords and personal identification numbers, both of which people tend to lose or forget. In biometric verification the system tries to match an input biometric (such as a fingerprint, face image, or iris image) to a stored biometric template. Thus correlation filter techniques are attractive candidates for the matching precision needed in biometric verification. In particular, advanced correlation filters, such as synthetic discriminant function filters, can offer very good matching performance in the presence of variability in these biometric images (e.g., facial expressions, illumination changes, etc.). We investigate the performance of advanced correlation filters for face, fingerprint, and iris biometric verification.     

Class-associative multiple target detection by use of fringe-adjusted joint transform correlation

We propose a class-associative correlation filter based technique for detecting a class of objects consisting of dissimilar patterns. The fringe-adjusted joint transform correlation algorithm is utilized to enhance the correlation performance, thus ensuring a strong and equal correlation peak for each element of the selected class. For enhanced performance, an enhanced version of the fringe-adjusted filter is incorporated in the class-associative multiple target detection process. The feasibility of the proposed technique has been tested by computer simulation.     

Hyperspectral target detection via discrete wavelet-based spectral fringe-adjusted joint transform correlation

Spectral variability remains a major challenge for target detection in hyperspectral imagery (HSI). Recently, the spectral fringe-adjusted joint transform correlation (SFJTC) technique has been used effectively for hyperspectral target detection applications. In this paper, we propose to use discrete wavelet transform (DWT) coefficients of the signatures as features for detection in order to make the SFJTC technique more insensitive to spectral variability. We devised a supervised training algorithm that uses the pure target signature and randomly selected samples from input scenery to select an optimal set of DWT coefficients for detection. We have inserted target signatures into urban and vegetative hyperspectral scenery with varying levels of spectral variability to explore the performance of our DWT-based SFJTC technique in different operating conditions. Detection results in the form of receiver-operating-characteristic (ROC) curves and area-under-the-ROC (AUROC) curves show that the proposed scheme yields the largest mean AUROC values compared to SFJTC using the original signatures and traditional hyperspectral detection algorithms.     

Method of target detection in images by moment analysis of correlation peaks

Automatic target detection and recognition in images often is attempted by use of a linear correlation filter (matched filter), whose output is interpreted by a single pointwise detector (detection based on only one point). I examine a technique for significantly improving the performance of this target detection approach by supplementing the pointwise detector with several neighborhood correlation peak detectors (detection based on a domain of many points extending over much of the peak). The neighborhood detectors extract peak shape information through a moment analysis of correlation plane peaks. I describe the design of statistically quasi-optimal correlation peak discriminators based on second-order geometric moments.     

Minimum-mean-square-error filters for detecting a noisy target in background noise

A minimum-mean-square-error filter is proposed to detect a noisy target in spatially nonoverlapping background noise. In this model, both the background noise that is spatially nonoverlapping with the target and the noise that is additive to the target and the input image are considered. The criterion used to design the filter is to minimize the mean-square-error between the filter output and a delta function located at the target position in the presence of the noise. Computer-simulation results for a number of noisy input images are presented, and the performance of the filter is determined. We also test the filter discrimination against undesired objects and tolerance to target distortions, such as rotation and scaling.     

Hardware-efficient realization of a real-time ultrasonic target detection system using IIR filters - [correspondence]

In this study, we address the increased computational demands of a frequency-diverse ultrasonic target detection system by developing a zero-phase IIR (ZP-IIR) filter. Several ZP-IIR filter types including Chebyshev-I, Chebyshev- II, and Butterworth were analyzed for their detection performance. The 4th-order filters with 8-bit quantized coefficients are shown to improve the flaw-to-clutter ratio by approximately 10 dB. Furthermore, the reduced adder graph algorithm is used for a hardware realization of ZP-IIR filters that does not require any dedicated multipliers. A small number of coefficients inherent to IIR filters and their multiplierless implementation provide efficient architecture suitable for compact, real-time ultrasonic imaging devices.     

Constrained image reconstruction for magnetic detection electrical impedance tomography

Magnetic detection electrical impedance tomography (MD‐EIT) produces images of conductivity from magnetic field measurements taken around the body. The ill‐conditioned nature of the MD‐EIT inverse problem is improved by limiting the number of unknowns to be solved. In this article, a method of iterative grid refinement for MD‐EIT, which produces images significantly better than unconstrained solutions, is described. © 2008 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 379–382, 2007     

Spatial-domain implementation of optimal multicriteria correlation filters

We show that optimal regions of support for correlation filters in the frequency domain can be approximated by relatively small convolution kernels in the spatial domain. We present an optimal approach for generating regions of support, as well as a fast nonoptimal approach for conventional optical correlators. Because the convolution kernels are similar to low-pass filters, the resulting input image to a correlator is always positive valued. We show that the performance of the convolution-based approach is comparable with the optimal frequency-domain approach. An important advantage of our method is that it can be implemented on low-cost arithmetic frame grabbers that can perform convolution with small kernels in real time. In addition, our method can be used in conjunction with a filter spatial light modulator that cannot produce a zero state.     

Performance of minimum-mean-square-error filters for spatially nonoverlapping target and input-scene noise

Using computer simulations, we investigate the performance of a minimum-mean-square-error filter for input-scene noise that is spatially nonoverlapping (disjoint) with a target for a limited set of images. Different input-scene-noise statistics are used to test the filter performance. We show that in the presence of spatially nonoverlapping target and input-scene noise, the output of the minimummean- square-error filter has a well-defined correlation peak, small sidelobes, and a high peak-to-correlationenergy ratio compared with other widely used filters such as the classical matched filter, the phase-only filter, and the inverse filter. We also test the robustness of the minimum-mean-square-error filter to errors in noise statistics used in the filter design. We show that, for the images tested here, the performance of the minimum-mean-square-error filter is not sensitive to errors in noise statistics and the filter can detect the target even if a considerable error exists. The discrimination capability and the illumination sensitivity of the minimum-mean-square-error filter are also tested.     

动目标/平台条件下的宽带相关处理

1引言宽带回波信号具有信息量大、混响背景相关性弱,有利于目标检测及其参量估计、特征提取等诸多优点。随着宽带信号在水声信号处理中的广泛应用,其回波信号处理的研究成为关键。本文在动目标/平