Multiple-target detection with use of a modified amplitude-modulated joint transform correlator

Multiple-target detection with a modified amplitude-modulated joint transform correlator is proposed. With this technique the joint power spectrum is first modified by the subtraction of the power spectrum of the input scene only and of the reference image from it; the resultant modified joint power spectrum is next multiplied by the amplitude-modulated filter function. The effect of noise in the input scene on the performance of the joint transform correlator is analyzed and quantified. This technique is found to deliver a correlation output and the capacity to accommodate noise in the input scene better than both the fringe-adjusted filter-based joint transform correlator and the modified fringe-adjusted filter-based joint transform correlator.     

Distortion-invariant fringe-adjusted joint transform correlation

A distortion-invariant joint transform correlator based on the concepts of the fringe-adjusted joint transform correlator and the synthetic discriminant function is presented. Computer-simulation results show that the proposed joint transform correlator is distortion-invariant for the target image from the training set and produces sharper correlation peaks and lower sidelobes compared with the classical joint transform correlator.     

Image classification with a chirp-encoded joint transform correlator

We describe a method of performing image classification with a chirp-encoded joint transform correlator. In the proposed system the reference images and the input image that is to be classified are placed in different input planes of the joint transform correlator. As a result, different output planes of the correlator are associated with each reference image. The input image is classified on the basis of the intensity and the spatial position of the correlation peak. The reference images and the input image can be positioned in one input plane with glass blocks of different thicknesses placed on each reference image. This produces the same effect as having the reference images and the input image in different planes. Analytical expressions, computer simulations, and optical experiments are presented to investigate the performance of the chirp-encoded joint transform correlator for image classification.     

Adaptive phase-input joint transform correlator

An adaptive phase-input joint transform correlator for pattern recognition is presented. The input of the system is two phase-only images: input scene and reference. The reference image is generated with a new iterative algorithm using phase-only synthetic discriminant functions. The algorithm takes into account calibration lookup tables of all optoelectronics elements used in optodigital experiments. The designed adaptive phase-input joint transform correlator is able to reliably detect a target and its distorted versions embedded into a cluttered background. Computer simulations are provided and compared with those of various existing joint transform correlators in terms of discrimination capability, tolerance to input additive noise, and to small geometric image distortions. Experimental optodigital results are also provided and discussed.     

抗噪性联合变换相关器

我们对联合变换相关器的输入图象进行Roberts梯度处理,同时对参考图象采取相移技术,并在频域中将功率谱二值化。与传统的联合变换相关器（CJTC）相比,新型的联合变换相关器（NJTC）消除了功率谱的直流分量,锐化了相关峰强度,提高了衍射效率和识别能力,同时增强了联合变换相关器的抗噪性能。用计算机模拟出预言结果。     

Performance improvement of a phase-shifting joint transform correlator by use of phase-iterative techniques

We propose a new technique to improve the performance of the joint transform correlator (JTC). In this technique we have applied the phase-iterative algorithm to a phase-shifting JTC (PSJTC). By doing so, we restrain the noise that is contained in the recovered phase of the joint transform power spectra for the input images with background and additive noise. In the case in which the input image is embedded in the input noise, we find that, by using the phase-iterative techniques with the PSJTC, one can get a higher cross-correlation peak and signal-to-noise ratio than with a PSJTC alone. From the computer-simulation results, one can conclude that the proposed algorithm successfully enhances PSJTC performance, especially for an input image with large noise.     

Nonlinear processing and fractional-order filtering in a joint fractional fourier-transform correlator: performance evaluation in multiobject recognition

We investigated the correlation performance of a joint fractional Fourier-transform correlator (JFRTC) using computer simulation results. We present a mathematical analysis suggesting use of processing techniques based on a nonlinear transformation and fractional-order fractional-power fringe-adjusted filter to attain improved performance in terms of discrimination sensitivity and input space-bandwidth utilization. Optimal noise performance for the JFRTC is predicted in the presence of additive white Gaussian noise. An all-optical implementation scheme based on incoherent erasure in a photorefractive crystal is proposed.     

Heteroassociative multiple-target tracking by fringe-adjusted joint transform correlation

A heteroassociative joint transform correlation (JTC) technique is proposed for recognizing and tracking multiple heteroassociative or dissimilar targets from gray-level image sequences by use of the concept of fringe-adjusted JTC and a multiple-target-detection algorithm. A fringe-adjusted JTC technique is used to ensure quantification of the similarities among several input images while it satisfies the equal-correlation-peak criterion. Tracking is accomplished by retrieval of the target motion information estimated from multiple consecutive image frames. An enhanced version of the fringe-adjusted filter is incorporated into the heteroassociative multiple-target-detection process to optimize the correlation performance. The feasibility of the proposed technique is tested by computer simulation with real infrared image data.     

All-optical subtracted joint transform correlator with a holographic interferometer

A new, to our knowledge, subtracted joint transform correlator (SJTC) is proposed that has no digital processing in a computer. All processing for obtaining correlation signals between an object and multiple reference patterns were treated optically by use of a joint transform correlator with a holographic interferometer similar to the Mach-Zehnder one. The joint power spectrum of the reference patterns was subtracted from that of the input image (the object pattern plus the reference patterns), and the spurious correlation signals between the different reference patterns were removed. Because of the optical parallel computations of the Fourier spectra and the subtraction, the real-time SJTC is possible to achieve by use of only an optical system. An experimental arrangement of the system and system performances of the shift-invariant characteristics and discriminability are described. The results show the good performance of this system.     

Dual nonlinear correlator based on computer controlled joint transform processor: Digital analysis and optical results

Abstract

The hybrid optoelectronic processor, presented in this paper, realizes the dual nonlinear correlation (DNC) in a set-up based on a two-step nonlinear joint transform correlator architecture. In the first step three power spectrum distributions (input scene power spectrum, reference target power spectrum, and the joint power spectrum) necessary for the nonlinear processing are captured with a CCD camera. Nonlinear modification of the joint power spectrum, which does not have to be symmetrical in the input and reference channels, is introduced digitally. In the second step, the modified joint power spectrum is Fourier transformed optically. Numerical analysis of this processor shows a crucial influence of the dynamic range and the limited number of grey levels of the CCD camera during image acquisition in the first step, on the output signal parameters and the discrimination capability of the set-up. Optical results of recognition obtained for noise-free segmented input scenes show that the set-up enables the realization of various correlation operations as CMF, POF, IF, PPC, etc. without any filters and that the discrimination capability is easy to control by a proper selection of the type of nonlinear processing.     

Approximate performance of the nonlinear joint transform correlator in signallike noise

We present a statistical-analysis technique for a nonlinear joint transform correlator (JTC) based on two assumptions: the noise and the signal spectra are identical, and the signal energy is small relative to the noise energy. The first assumption, while admittedly convenient, is also defensible in that it is a worst case and in that image and scene noise can be similar in texture. The second is also reasonable, given that even a clearly visible signal may have small energy compared with the scene noise if it is of limited extent; in any case, the results appear moderately faithful even for the case that signal and noise energies are equal. We discover that the optimal Fourier-plane transformation is spatially variant and tends to remove the Fourier amplitudes of the input image, and indeed functions in a way very similar to the spatially variant binary JTC. We also see that the classic (or spatially invariant linear) JTC is a very inferior technique for signallike noise, that the best spatially variant binary JTC uses a threshold proportional to the noise power spectrum, and that, if a spatially invariant binary-thresholded JTC is desired, then the median Fourier-plane value is an excellent choice of threshold. The performance predictions are verified by simulation and appear to be reasonable even for the highly nonlinear binary schemes.     

Complementary-reference joint transform correlator

We present a new input configuration for the joint transform correlator called a complementary-reference joint transform correlator (CRJTC). The difference between the CRJTC and the existingjoint transform correlator is that the CRJTC has an additional complementary reference in the input plane. We use a criterion defined as the ratio of the cross correlation between the target and the reference to the cross correlation between the target and the complementary reference to perform recognition. This ratio can attain its maximum if and only if the reference and the target are matched and if it is stable to the light-source intensity fluctuations; therefore it is unnecessary to normalize the input images. The CRJTC is suitable for the recognition of a binary-amplitude image only. The experimental demonstration of a CRJTC is given.     

Experimental verifications of a joint transform correlator using compressed reference images

Single-target and multiple-target detections by using a joint transform correlator (JTC) with compressed reference images are experimentally verified. Two high-contrast images with different spatial-frequency content are used as test scenes. Although an effect of the additive noise on detection performance of the proposed correlator is more severe than that of the compression, the experimental results confirm the feasibility of implementing the JTC with compressed reference images.     

Performance of a phase-transformed input joint transform correlator

Conventionally a detected image is represented by an intensity array owing to the square-law nature of most detectors. However, this does not mean that we have to restrict ourselves to using intensity images for the correlation process. Transforming intensity images into phase images before correlation, which can be easily realized by a phase-modulation spatial light modulator, offers an alternative approach for high-performance pattern recognition. A phase-transformed input joint transform correlator is investigated in detail in terms of pattern discriminability, detection efficiency, and noise robustness. We show that the phase-transformed joint transform correlator has higher pattern discriminability and detection efficiency than the conventional joint transform correlator, and it also offers a better trade-off between the pattern discriminability and noise tolerance. A proof-of-concept experiment is also provided.     

Morphologically preprocessed joint transform correlation

A morphologically preprocessed joint transform correlation is proposed that combines the techniques of morphological filtering and joint transform correlation. We improve on the performance of a joint transform correlator by eliminating noise with morphological preprocessing and by performing edge detection of input images. Computer simulation results show that the corresponding system contributes to a better discrimination capability than gradient operator-based and wavelet-based preprocessed joint transform correlation.     

Modified joint transform correlator binarized by error-diffusion. I. Spatially constant noise-dependent range limit

Two error-diffusion-based binarization methods for joint transform correlator configurations, which adaptively take into account the effects of input-additive white Gaussian noise, are analyzed. Before binarization, the operations performed upon the joint power spectrum are either truncation and normalization or subtraction of a noise pedestal followed by truncation and normalization. The noise-pedestal value is defined as the measurable estimate of the noise power spectral density. Truncation and normalization are carried out with a spatially constant noise-dependent range limit, based on the statistical properties of the noise, and the noise-pedestal value. All required parameters, dependent on the input-noise level, can be measured from the joint power spectrum distribution and are updated for every new input scene. A computer-simulation comparison of correlation-peak characteristics demonstrates the advantages of the suggested approaches. Optical experiments with compatible results are also presented.     

Adaptive binary joint transform correlator for image recognition

Our research has shown that the autocorrelation peaks of a binary joint transform correlator are affected by input scenes' backgrounds. An adaptive method is proposed to overcome this problem. The image of interest is first extracted from the background based on the position of the highest correlation peak of the input and reference images. The extracted image is then correlated with the reference to obtain the final correlation peak. Numerical simulations showed that the final autocorrelation peak is the maximum constant for a specified reference image.     

Experimental systems implementation of a hybrid optical-digital correlator

A high-speed hybrid optical-digital correlator system was designed, constructed, modeled, and demonstrated experimentally. This correlator is capable of operation at approximately 3000 correlations/s. The input scene is digitized at a resolution of 512 x 512 pixels and the phase information of the two-dimensional fast Fourier transform calculated and displayed in the correlator filter plane at normal video frame rates. High-fidelity reference template images are stored in a phase-conjugating optical memory placed at the nominal input plane of the correlator and reconstructed with a high-speed acousto-optic scanner; this allows for cross correlation of the entire reference data set with the input scene within one frame period. A high-speed CCD camera is used to capture the correlation-plane image, and rapid correlation-plane processing is achieved with a parallel processing architecture.     

Two-beam-coupling correlator for synthetic aperture radar image recognition with power-law scattering centers preenhancement

Synthetic radar image recognition is an area of interest for military applications including automatic target recognition, air traffic control, and remote sensing. Here a dynamic range compression two-beam-coupling joint transform correlator for detecting synthetic aperture radar targets is utilized. The joint input image consists of a prepower-law, enhanced scattering center of the input image and a linearly synthesized power-law-enhanced scattering center template. Enhancing the scattering center of both the synthetic template and the input image furnishes the conditions for achieving dynamic range compression correlation in two-beam coupling. Dynamic range compression (a) enhances the signal-to-noise ratio, (b) enhances the high frequencies relative to low frequencies, and (c) converts the noise to high frequency components. This improves the correlation-peak intensity to the mean of the surrounding noise significantly. Dynamic range compression correlation has already been demonstrated to outperform many optimal correlation filters in detecting signals in severe noise environments. The performance is evaluated via established metrics such as peak-to-correlation energy, Horner efficiency, and correlation-peak intensity. The results showed significant improvement as the power increased.     

Modified joint transform correlator binarized by error diffusion. II. Spatially variant range limit

Nonlinear scaling of the joint power spectrum in one approach and noise-subtraction followed by nonlinear scaling in a second approach are analyzed in conjunction with error-diffusion binarization for joint transform correlator configurations in the presence of input-additive white Gaussian noise. Nonlinear scaling is performed with a spatially variant range limit. For the second approach the subtracted noise value is an estimate of the noise power spectral density. Computer simulations and optical experiments demonstrate the advantages of the proposed error-diffusion-based joint transform correlator approaches. In particular the approach based on noise subtraction before nonlinear scaling is advantageous even for very high noise levels.