Joint Transform Research Articles

New Architectures and Algorithm for Optical Pattern Recognition using Joint Transform Correlation Technique

Recently, there is increasing interest in using joint transform correlation (JTC) technique for optical pattern recognition. In this technique, the target and reference images are jointed together in the input plane and no matched filter is required. In this paper, the JTC is investigated using simulation technique. A new discrimination decision algorithm is proposed to recognize the correlation output for different object shapes (dissimilar shapes). Also, new architectures are proposed to overcome the main problems of the conventional JTC.

Opto-Mechatronic Image Stabilization for a Compact Space Camera

The paper shows the enhanced concept of an opto-mechatronic camera stabilization assembly consisting of a high-speed onboard optical processor for realtime image motion measurement and a 2-axis piezo-drive assembly for high precision positioning of the focal plane assembly. The proposed concept allows to minimize the size of the optics and the sensitivity to attitude disturbances. The image motion measurement is based on 2D spatial correlation of sequential images recorded from a motion matrix sensor in the focal plane of the camera. The demanding computational requirements for the real-time 2D-correlation are covered by an embedded optical correlation processor (Joint Transform type).

Using optical speckle correlation to study breakdown processes in structural materials

This paper proposes an optical speckle-correlation method for plotting displacement and strain fields on the surfaces of samples of structural materials. The method is implemented using computer models of a linear joint transform (JT) correlator, a JT correlator with medial and local-medial threshold, and also a JT correlator with a band-equalization filter. The errors of the surface displacements on test speckle patterns and actual speckle patterns are analyzed. Results are presented for calculations of the displacement fields of the strained surface of a duralumin sample, obtained by means of computer models developed for the JT correlator. © 2004 Optical Society of America

Enhanced Optical Image Verification Based on Joint Transform Correlator Adopting Fourier Hologram

In this paper two new architectures for optical image verification are proposed. Both architectures are based on conventional joint transform correlators (JTCs) adopting a Fourier hologram and can significantly improve the recovered image quality. First, an input phase-only function is Fourier transformed and then interferes with a reference wave that is diffracted from a plane wave incident on another random phase mask. Second, two phase-only functions are placed at the two input sides of a beamsplitter such that the interference pattern of their Fourier transforms can be detected. To obtain a predefined target image in the output plane, one of the input phase functions is iteratively retrieved by the use of the projection onto constraint sets algorithm. Simulation results show that the less mean squared error and better image quality are obtained for both the binary and grayscale images.

Three-dimensional fringe-adjusted joint transform correlation.

A three-dimensional (3D) pattern recognition technique using the concept of fringe-adjusted joint-transform correlation is proposed in this paper. The proposed technique yields better correlation discrimination ability compared with alternate 3D classical joint-transform correlation by producing sharper and stronger correlation peak intensity. Simulation results verify the performance of the proposed technique.

광학적 상관관계를 기반으로 하는 지문인식 방법에 관한 연구

지문 인식은 보통 지문 영상의 획득과 획득된 지문 영상을 비교하는 단계로 구분해서 생각할 수 있다. 본 논문에서는 지문 영상을 획득하는 단계에서 지문 입력 센서를 사용하여 연구의 초점을 지문의 비교 방법에 맞추었다. 지문 입력 센서에서 는 지문 영상이 영상처리되어 출력되기 때문에 지문을 획득할 때 발생할 수 있는 잡음들에 대해서는 고려하지 않았고 사용자가 임의적으로 여러 번 지문을 입력하게 하여 회전과 이동이 복합적으로 존재하는 영상 왜곡을 고려하였다. 사용자의 지문 인식을 위한 방법으로 광학적 상관관계(Optical Correlation)를 출력하는 Non-linear Joint Transform Correlator(NRC)를 컴퓨터 상에서 구현하였고, 지문 입력 시에 발생할 수 있는 왜곡에 불변적인 특징을 갖도록 지문의 중심점을 찾는 알고리즘을 추가하여 지문 인식의 정확도를 보완하였다. 또한, 찾아진 지문 영상의 중심점을 가지고 100<TEX>$\times$</TEX>100픽셀 크기의 중심 영역만을 추출하여 지문 인식에 필요한 시간과 입력 영상의 정보를 줄이면서 높은 정확도를 갖는 매칭 기법을 제시하였다 Fingerprint recognition is concerned with fingerprint acquisition and matching. Our research was focused on a fingerprint matching method using an inkless fingerprint input sensor at the fingerprint acquisition step. Since an inkless fingerprint sensor produces a digital-image-processed fingerprint image, we did not consider noise that can happen while acquiring the fingerprint. And making the user attempt fingerprint input as random, we considered image distortion that translation and rotation are included as complex. NJTC algorithm is used for fingerprint identification and verification. The method to find the center of the fingerprint is added in the NJTC algorithm to supplement discrimination of fingerprint recognition. From this center point, we decided the optimum cropping size for effective matching with pixels and demonstrated that the proposed method has high discrimination and high efficiency.

Space Application of a Self-Calibrating Optical Processor for Harsh Mechanical Environment

An optical image processing device based on a Joint Transform Optical Correlator provides high processing speed and could be very usable in real-time image processing applications. In harsh mechanical environment the performance of the correlator could degrade due to mechanical deformations of the optical system. With special self-calibration methods the Joint Transform Optical Correlator can be built insensitive to mechanical disturbances.

Defect Detection in Fabrics with a Joint Transform Correlation Technique: Theoretical Basis and Simulation

The theoretical basis of a technique for real time defect detection in fabrics is presented using a joint transform correlator. This correlation technique is an extension of Fourier transform analysis and is extremely useful for real time pattern recognition. The regular periodic nature of a woven fabric makes it possible to use the Fourier transform technique to detect defects. However, classifying various defect types is difficult from the Fourier analytical and experimental results. A solution to this problem is to use a joint Fourier transform of a reference pattern and the test pattern, and the joint power spectrum is further processed. Cross- and auto-correlation peaks, generated after the execution of the second Fourier transform on the filtered joint power spectrum, indicate the existence of a particular defect type. Because of the parallel processing ability of the optical system, implementing the joint transform correlation technique in an optical domain is advanta geous. A fractional power fringe adjusted filter is used for efficient detection of defects. The mathematical formulation of the technique is supported by the simulated results for identifying some defects such as the existence of thick yarns, knots, and missing yarns.

Micro-displacement by Means of Optoelectronic Nonlinear Joint Transform Correlation of Speckle Pattern

Two objective speckle patterns are obtained—one before and one after the object has been moved—and are placed in the input plane of an optoelectronic nonlinear joint transform correlator.Nonlinear transformation of the joint power spectrum permits a sharper correlation peak and a high signal-to-noise ratio. The autocorrelation peak coordinates of the first pattern are set as a reference for measuring displacements of the cross-correlation peak, and also, the calibration of the measurement system is performed.It is well known that speckle can be regarded as a random spatial carrier in which information on the shape and position of the diffusing surface is encoded. The correlation of speckle patterns is highly appropriate for measuring displacements of tens of micrometers.1,2 In this work, an alternative technique based on the correlation between two speckle patterns by means of a digital bipolar joint transform correlator3 is presented. Initially the technique is described and then experimental results are shown.

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.

Phase-Encoded Joint Transform Correlator to Reduce the Influence of Extraneous Signals

A technique to reduce the influence of the dc and the complex-conjugate correlation signals that are inherent in a joint transform correlator is proposed. The key part of this technique is the use of a phase mask. The mask contributes only extraneous signals, so the influence of the signals is reduced. The desirable correlation signal is not affected, and its shape is retained. Computer simulations confirm the performance of the proposed phase-encoded joint transform correlator.

複数のリファレンスパターンを使用するＪｏｉｎｔ Ｔｒａｎｓｆｏｒｍ Ｃｏｒｒｅｌａｔｏｒとその文字認識への応用

In this paper, we investigate how the positioning and size of input and reference patterns affect the use of multiple reference patterns in a classical Joint Transform Correlator (JTC), and we produce an optical system using a JTC with multiple reference patterns. This system is applied to feature extraction from handwritten alphabetical letters, using four linear elements aligned vertically, horizontally, and obliquely at angles of ±45 degrees as features. The extracted features were input into a three-layer neural network to decide which letter was input. This system achieved an average recognition rate of 88.8% for 30 sets of handwritten alphabetical letters.

Photorefractive effect in the Fourier plane

On the basis of the Whole Beam Method, we describe the steady state photorefractive effect in optical devices where the crystal illumination is not a uniform interference pattern. The example of BSO, sitting in the Fourier plane of the two images of a Joint Transform Optical Correlator is given as an illustration.

Uses of Joint Transform Correlators in Supervised and Unsupervised Hybrid Computational-Optical Neural Networks

The application of a joint transform correlator to supervised neural networks is discussed. In addition, the application to unsupervised neural networks following Kohonen’s self organizing algorithm is also described.

Characterization of an Optimized Nonlinear Joint Transform Correlator

Characterization of an Optimized Nonlinear Joint Transform Correlator

Comparison Between Experiment and Simulation in a Real-Time Joint Transform Correlator

A joint transform correlator which operates in real time with a liquid crystal television has been made for pattern recognition purposes. Discrimination is improved by means of saturation effects. A digital simulation of this process has been carried out with the same limitations of resolution and dimensions of experimental architecture. A comparison between the experiment and simulation shows good agreement.

Multiple Object Identification By Bipolar Joint Transform Correlation

We investigate the performance of a recently introduced bipolar joint transform image correlator when multiple reference objects and single and multiple targets are present at the input plane. The bipolar joint transform correlator uses nonlinearity at the Fourier plane to threshold the Fourier trans-forms' interference intensity to only two values, 1 and -1. The performance of the bipolar correlator is compared to the classical joint transform image correlator when multiple objects are present at the input plane. We show that the performance of the bipolar correlator is substantially superior to that of the classical joint transform image correlator in the areas of autocorrelation peak intensity, autocorrelation peak to sidelobe ratio, autocorrelation bandwidth, and discrimination sensitivity. It is shown that when multiple objects are present at the input plane, the classical joint transform image correlator produces large cross-correlation signals and poorly defined autocorrelation signals with large bandwidth. The large cross-correlation signals are comparable in intensity with the autocorrelation peak and can falsely indicate the presence of many targets. On the other hand, the bipolar joint transform image correlator produces well-defined autocorrelation signals with very low cross-correlation level. Computer simulations are used to test both types of correlators, and 3-D plots of the correlation functions are provided and discussed.