Distortion-invariant target detection using shifted-reference joint transform correlator (original) (raw)
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This paper proposes a novel pattern recognition system for invariance to noise and distortions. The technique first generates a synthetic discriminant function of the target image from its different distorted versions. It then takes four different phase-shifted versions of the reference image, which are individually joint transform correlated with the given input scene. Thus the proposed algorithm produces a single cross-correlation signal corresponding to each potential target. Also a fringe-adjusted filter is designed to generate a delta-like correlation peak with high discrimination between the signal and the noise. The pattern recognition system is also designed for the identification of multiple targets belonging to multiple reference objects simultaneously in a given input scene. The proposed technique is investigated using computer simulation including real-life images in different complex environments.
A new target detection technique is presented in this paper for the identification of small boats in coastal surveillance. The proposed technique employs an adaptive progressive thresholding (APT) scheme to first process the given input scene to separate any objects present in the scene from the background. The preprocessing step results in an image having only the foreground objects, such as boats, trees and other cluttered regions, and hence reduces the search region for the correlation step significantly. The processed image is then fed to the shifted phase-encoded fringe-adjusted joint transform correlator (SPFJTC) technique which produces single and delta-like correlation peak for a potential target present in the input scene. A post-processing step involves using a peak-to-clutter ratio (PCR) to determine whether the boat in the input scene is authorized or unauthorized. Simulation results are presented to show that the proposed technique can successfully determine the presence of an authorized boat and identify any intruding boat present in the given input scene.
Optical Engineering, 2008
An optoelectronic neural network based detection technique is proposed for multi-class distortion-invariant pattern recognition. The neural network is utilized in the training stage for a sequence of multi-class binary and gray level images for supervised learning using shifted phase-encoded joint transform correlator with fringe adjusted filter in the hidden layer to create composite images that are invariant to distortion. Simulation results show that the proposed technique is efficient in recognizing targets in variable environmental conditions.
Multiple Targets Recognition for Highly-Compressed Color Images in a Joint Transform Correlator
Optics and Photonics Journal
In this paper, we are proposing a compression-based multiple color target detection for practical near real-time optical pattern recognition applications. By reducing the size of the color images to its utmost compression, the speed and the storage of the system are greatly increased. We have used the powerful Fringe-adjusted joint transform correlation technique to successfully detect compression-based multiple targets in colored images. The colored image is decomposed into three fundamental color components images (Red, Green, Blue) and they are separately processed by three-channel correlators. The outputs of the three channels are then combined into a single correlation output. To eliminate the false alarms and zero-order terms due to multiple desired and undesired targets in a scene, we have used the reference shifted phase-encoded and the reference phase-encoded techniques. The performance of the proposed compression-based technique is assessed through many computer simulation tests for images polluted by strong additive Gaussian and Salt & Pepper noises as well as reference occluded images. The robustness of the scheme is demonstrated for severely compressed images (up to 94% ratio), strong noise densities (up to 0.5), and large reference occlusion images (up to 75%).
Optical pattern recognition using two-channel joint transform correlation
Optical Memory and Neural Networks, 2007
A new joint transform correlation (JTC) technique, named two-channel JTC (TJTC), is proposed in this paper for optical pattern recognition applications. The TJTC technique independently evaluates the autocorrelation and crosscorrelation values of the reference and the target images and employs a modified decision algorithm. In addition, optical threshold operation and fringe-adjusted filter are incorporated in the proposed technique to enhance the correlation output and to improve the discrimination performance. The proposed technique shows better recognition performance compared to existing JTC techniques. Computer simulation are presented to investigate the salient features of the proposed TJTC technique with noise-free as well as noisy input scenes.
Distortion-invariant fringe-adjusted joint transform correlation
Applied Optics, 1997
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.
Multiple-target detection by using joint transform correlator with compressed reference images
Optics Communications, 2005
Effects of JPEG compression of reference image on multiple-target detection by using joint transform correlator are quantitatively studied by using computer simulation. Two types of images with different spatial-frequency contents are used as test scenes in the presence of noise in the input plane and the contrast difference. The results show that in comparison with the use of the compressed reference with high spatial-frequency contents, the multiple-target detection by using the joint transform correlator with the compressed reference with low spatial-frequency contents produces better detection performance in that it is robust to noise and contrast difference for a wide range of compression qualities. While in the presence of noise and contrast difference, the compression of the reference image with high spatialfrequency contents may cause false alarms.
Color pattern recognition using fringe-adjusted joint transform correlation
Optical Engineering, 2001
A fringe-adjusted joint-transform correlator (JTC) based technique for improved color pattern recognition is introduced. In the proposed technique, a real-valued filter, called the fringe-adjusted filter is used to reshape the joint power spectrum in order to yield better correlation output. A color image is processed through three channels, and the fringe-adjusted filtering is applied to each of these channels to obtain excellent correlation discrimination. The correlation outputs from these, channels are then fused together to achieve a decision on the detection of a given color pattem. It is also shown that the fringe-adjusted filtering can be applied to a multichannel single-output JTC to obtain excellent correlation output that represents the coherence level between the input target image and the reference image for all color channels. These two techniques can be easily implemented in real time as for practical color pattern recognition applications. Two architectures for all-optical implementation of the proposed techniques are presented.
Iraqi Journal for Electrical And Electronic Engineering
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.
Wavelet-modified fringe-adjusted joint transform correlator
Optics & Laser Technology, 2008
In this paper, we implement a wavelet-modified fringe-adjusted joint transform correlator (JTC) for real-time target recognition applications. In real-time situation the input scene is captured using a charge-coupled device (CCD) camera. The obtained joint power spectrum is multiplied by a pre-synthesized fringe-adjusted filter and the resultant function is processed with an appropriately scaled wavelet filter. Three performance measure parameters: correlation peak intensity, peak-to-sidelobe ratio, and signal-to-clutter ratio have been calculated for fringe-adjusted joint transform correlator (FJTC) and wavelet-modified fringe-adjusted joint transform correlator (WFJTC). The WFJTC has been found to yield better results in comparison to conventional FJTC. To suppress the undesired strong dc, the resultant function is differentiated. Differential processing wavelet-modified fringe-adjusted joint power spectrum removes the zeroorder spectra and hence improves the detection efficiency. To focus the correlation terms in different planes in order to capture one of the desired autocorrelation peaks and discard the strong dc and another autocorrelation peak, chirp-encoding technique has also been applied. Targets with Gaussian and speckle noise have also been used to check the correlation outputs. Computer simulation and experimental results are presented. r