Fringe-adjusted Joint Transform Correlation Technique Research Articles

Class-associative multiple target recognition for highly compressed color images in a joint transform correlator

We propose a practical near real-time compression-based class-associative multiple target detection technique for color images. The size of the color images is reduced to its utmost ratio to speed up the processing time and to increase the storage capacity of the recognition system. A fringe-adjusted joint transform correlation technique is employed to successfully detect the compression-based multiple targets in colored images. In addition, to eliminate the false alarms and zero-order terms due to multiple desired and undesired objects in an input scene, we have used the shifted phase-encoded and the reference phase-encoded techniques. The performance of detecting the class-associative multiple targets for large compression ratios (up to 94%) and under strong noisy conditions (Gaussian and salt and pepper noises) is assessed through many computer simulations experiments. Furthermore, detecting small targets or even a small part of a target is presented for images occluded up to 90%.

Distortion-invariant multiple target detection using class-associative joint transform correlation

A distortion-invariant class-associative pattern recognition technique is proposed, where a class of objects may be defined as a group of objects with similarity and dissimilarity among them. The fractional power fringe-adjusted joint transform correlation technique as well as the synthetic discriminant function concept has been effectively utilized to achieve the distortion-invariant detection of multiple dissimilar targets simultaneously present in the input scene. Simulation results prove that the proposed scheme is an effective tool for the detection of multiple dissimilar targets in both binary and gray-level input scenes corrupted by distortion and noise.

Shifted phase-encoded fringe-adjusted joint transform correlation for multiple target detection

A new shifted phase-encoded fringe-adjusted joint transform correlation technique is proposed for pattern recognition. The proposed technique utilizes a random phase mask to the reference image along with a phase-shifted version of the phase-masked reference image before forming the input joint image. The phase mask is also applied to the joint power spectrum before applying the inverse Fourier transform to yield the correlation output. This yields delta-function-like correlation peak intensity for each target object and ensures better utilization of the input/output plane space bandwidth product. The proposed technique can effectively detect targets in simulated as well as real-life input scenes without adjusting the system parameters. Simulation results are presented to verify the performance of the proposed technique.

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.