Fringe-adjusted Joint Transform Correlation Research Articles

Multiclass Object Detection With Single Query in Hyperspectral Imagery Using Class-Associative Spectral Fringe-Adjusted Joint Transform Correlation

We present a deterministic object detection algorithm capable of detecting multiclass objects in hyperspectral imagery (HSI) without any training or preprocessing. The proposed method, which is named class-associative spectral fringe-adjusted joint transform correlation (CSFJTC), is based on joint transform correlation (JTC) between object and nonobject spectral signatures to search for a similar match, which only requires one query (training-free) from the object's spectral signature. Our method utilizes class-associative filtering, modified Fourier plane image subtraction, and fringe-adjusted JTC techniques in spectral correlation domain to perform the object detection task. The output of CSFJTC yields a pair of sharp correlation peaks for a matched target and negligible or no correlation peaks for a mismatch. Experimental results, in terms of receiver operating characteristic (ROC) curves and area-under-ROC (AUROC), on three popular real-world hyperspectral data sets demonstrate the superiority of the proposed CSFJTC technique over other well-known hyperspectral object detection approaches.

Logarithmic fringe-adjusted joint transform correlation

An improved fringe-adjusted joint transform correlation (FJTC) technique is proposed, which employs a new real-valued filter, called the logarithmic fringe-adjusted filter. The Fourier-plane image subtraction technique is applied to the joint power spectrum before applying the inverse Fourier transform to generate better correlation output. The proposed technique yields better correlation output compared to alternate optical pattern recognition techniques when the target is embedded in noise-corrupted input scenes. Test results are presented to verify the performance of the proposed technique.

Image Segmentation and Shape Analysis for Road-Sign Detection

This paper proposes an automatic road-sign recognition method based on image segmentation and joint transform correlation (JTC) with the integration of shape analysis. The presented system is universal, which is able to detect traffic signs of any countries with any color and any of the existing shapes (e.g., circular, rectangular, triangular, pentagonal, and octagonal) and is invariant to transformation (e.g., translation, rotation, scale, and occlusion). The main contributions of this paper are: 1) the formulation of two new criteria for analyzing different shapes using two basic geometric properties, 2) the recategorization of the rectangular signs into diamond or nondiamond shapes based on the inclination of the four sides with the ground and 3) the employment of the distortion-invariant fringe-adjusted JTC (FJTC) technique for recognition. There are three main stages in the proposed algorithm: 1) segmentation by clustering the pixels based on the color features to find the regions of interest (ROIs); 2) traffic-sign detection by using two novel shape classification criteria, i.e., the relationship between area and perimeter and the number of sides of a given shape; and 3) recognition of the road sign using FJTC to match the unknown signs with the known reference road signs stored in the database. Experimental results on real-life images show a high success rate and a very low false hit rate and demonstrate that the proposed framework is invariant to translation, rotation, scale, and partial occlusions.

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.

Optoelectronic 3-D Object Classification From 2-D Images

The task of object classification is complicated by variations in the 3-D object, which translate into distortions in 2-D images. The pattern matching for 3-D invariance classification requires a large amount of data and computation time. Proposed here is an efficient 3-D object classification algorithm for real-time fringe-adjusted joint transform correlator (FJTC)-based automatic target recognition (ATR) system. The proposed classification technique employed a fragment-based recognition approach and a new type of synthetic discriminant function filter in the generation of distortion-invariant correlation filter sets. The optoelectronic FJTC is then used to provide correlation of the filter sets with the input under a proper arrangement. This classification method is simple and fast, hence is suitable to be in use by real-time ATR systems. For the conclusion, simulation results are provided to prove the effectiveness of the proposed system in the classification of objects invariant to 3-D out-of-plane rotation distortion.

Morphological fringe-adjusted joint transform correlation

We present a modified nonlinear correlation, called the morphological fringe-adjusted joint transform correlation (MFJTC), which is based on fringe-adjusted joint transform correlation (FJTC) and morphological correlation (MC). In MFJTC, the classical joint transform correlation (JTC) to implement the sublinear correlations (sub-LC) of MC is replaced with FJTC. The computer simulation results and an optoelectronic setup for MFJTC are given and compared with those of MC and FJTC. Simulations show that the MFJTC provides better stability against salt-and-pepper noise in the input scene, while maintaining high and sharp correlation signal. Although MFJTC loses the illumination-invariant recognition of MC when the input scene is brighter than the reference, it still ensures considerable discriminability.

Metrics for evaluating the performance of joint-transform-correlation-based target recognition and tracking algorithms

The performance of target detection and tracking algorithms generally depends on the signature, clutter, and noise that are usually present in the input scene. To evaluate the effectiveness of a given algorithm, it is necessary to develop performance metrics based on the input plane as well as output plane information. We develop two performance metrics for assessing the effects of input plane data on the performance of detection and tracking algorithms by identifying three regions of operation—excellent, average, and risky intervals. To evaluate the performance of a given algorithm based on the output plane information, we utilize several metrics that use primarily correlation peak intensity and clutter information. Since the fringe-adjusted joint transform correlation (JTC) was found to yield better correlation output compared to alternate JTC algorithms, we investigate the performance of two fringe-adjusted JTC (FJTC)-based detection and tracking algorithms using several metrics involving the correlation peak sharpness, signal-to-noise ratio, and distortion invariance. The aforementioned input and output plane metrics are used to evaluate the results for both single/multiple target detection and tracking algorithms using real life forward-looking infrared (FLIR) video sequences.

Improved fingerprint identification with supervised filtering enhancement

An important step in the fingerprint identification system is the reliable extraction of distinct features from fingerprint images. Identification performance is directly related to the enhancement of fingerprint images during or after the enrollment phase. Among the various enhancement algorithms, artificial-intelligence-based feature-extraction techniques are attractive owing to their adaptive learning properties. We present a new supervised filtering technique that is based on a dynamic neural-network approach to develop a robust fingerprint enhancement algorithm. For pattern matching, a joint transform correlation (JTC) algorithm has been incorporated that offers high processing speed for real-time applications. Because the fringe-adjusted JTC algorithm has been found to yield a significantly better correlation output compared with alternate JTCs, we used this algorithm for the identification process. Test results are presented to verify the effectiveness of the proposed algorithm.

Remove dc term and false alarms in fringe-adjusted joint transform correlator by using binary random phase mask

In this paper, we proposed a new architecture, called nonzero-order fringe-adjusted joint transform correlator (FJTC) using a binary random phase mask, for near real time pattern recognition applications. The binary random phase mask encodes the reference image by two equal probability phases before it is introduced in the joint input image. The joint power spectrum is then multiplied by the same phase mask to remove the zero-order term and false alarms that may be generated in the correlation plane due to the presence of multiple identical target or non-target objects in the input scene. The criteria used for measuring the performance of nonzero order fringe-adjusted JTC include correlation peak, intensity, peak-to-correlation energy, peak-to-sidelobe ratio are used in this paper. Detailed analysis for the proposed nonzero-order FJTC using binary random phase mask is presented. Simulation results verify the effectiveness of the proposed technique.

Fringe-adjusted joint transform correlator based target detection and tracking in forward looking infrared image sequence

The joint transform correlator (JTC) technique has been found to be suitable for real time pattern recognition applications. Among the various JTC techniques proposed in the literature, the fringe-adjusted JTC has been found to yield better correlation output for target detection. We propose a generalized fringe-adjusted JTC (GFJTC) based algorithm for efficient detection and tracking of a target in a video sequence. The proposed algorithm has been found to be suitable for near real-time recognition and tracking of a static or moving target, while accommodating the detrimental effects of background variations as well as other artifacts. The performance of the proposed technique has been verified with real life forward looking infrared (FLIR) image sequences.

Optoelectronic implementation of fringe-adjusted joint transform correlation

The performance of fringe-adjusted joint transform correlator (FJTC) has been widely investigated via analytical modeling and computer simulation. In this paper, we have successfully demonstrated the performance of FJTC for the first time using an optoelectronic experimental setup. The architecture for optoelectronic implementation of the FJTC has been tested for both binary and gray level noise-free and noise corrupted input scenes. The experimental results are in close agreement with the simulation results obtained earlier.

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.

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.

Real-time fingerprint identification

The pattern matching for fingerprints requires a large amount of data and computation time. Practical fingerprint identification systems require minimal errors and ultrafast processing time to perform real-time verification and identification. By utilizing the two-dimensional processing capability, ultrafast processing speed and non-interfering communication of optical processing techniques, fingerprint identification systems can be implemented in real-time. Among the various pattern matching systems, the joint transform correlator (JTC) has been found to be inherently suitable for real-time matching applications. Among the various JTCs, the fringe-adjusted JTC has been found to yield significantly better correlation output compared to alternate JTCs. In this paper, a fingerprint identification system has been developed based on the fringe-adjusted JTC. Since all pattern matching systems suffer from high sensitivity to distortions, the synthetic discriminant function concept has been incorporated in fringe-adjusted JTC to ensure distortion-invariant fingerprint identification.

Face recognition through pose estimation and fringe-adjusted joint transform correlation

We describe a simple pose estimation-based optoelectronic system for the recognition of facial images. The pose estimation de- scribed is a simple digital algorithm that is used to replace the composite image generation techniques while maintaining a low interclass cross- correlation. The optoelectronic fringe-adjusted joint transform correlator (FJTC) is then used to provide correlation. A description of the optoelec- tronic system and the entire process is presented. In addition, simulation results and comparison to synthetic discriminant function (SDF)-based recognition are provided to prove the effectiveness of the proposed sys- tem. © 2003 Society of Photo-Optical Instrumentation Engineers.

Thin photorefractive‐crystal‐based joint transform processor

An efficient four-wave mixing all-optical fringe-adjusted joint transform correlator (JTC) architecture employing a thin photorefractive crystal is proposed. This technique is suitable for both single- and multiple-target detection. It is very fast since all processing is done in the optical domain in one step. The proposed technique also alleviates the effects of nonlinearity introduced by square-law devices used in optoelectronic architectures. Computer simulation results are presented for both binary and gray-level target detection using the proposed all-optical fringe-adjusted JTC architecture. © 2000 John Wiley & Sons, Inc. Microwave Opt Technol Lett 26: 192–196, 2000.

Synthetic discriminant function fringe-adjusted joint transform correlator

A synthetic discriminant function (SDF) fringe-adjusted joint transform correlator is proposed that is able to provide a high degree of image distortion invariance and classify different objects in the input scene. The SDF reference function, which is displayed alongside the input scene, is a linear combination of the training image set. An iterative algorithm is presented and utilized to obtain the linear combination coefficients from the nonlinear equations of the fringe-adjusted joint transform correlation (JTC) system. When compared with the SDF-based classical JTC and binary JTC, the SDF fringe-adjusted JTC delivers a better capability to give localized equal correlation peak heights for the same class of objects. Furthermore, when the input scene contains the different objects from the different classes of images, the SDF fringe-adjusted JTC is shown to efficiently classify the different target objects and reject the nontarget object in the input scene, whereas the SDF-based classical JTC and binary JTC fail to achieve this.

Joint-transform correlation under varying illumination

The effect of varying the illumination of the target scene on the performance of classical, binary, and fringe-adjusted joint-transform correlators is investigated. Simulation results show that the fringeadjusted joint-transform correlator yields better correlation output than the classical or the binary joint-transform correlator under all illumination conditions of the target scene.