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.
A new three-dimensional (3D) color pattern recognition technique, utilizing the concept of fringe-adjusted joint transform correlator (JTC) and CIELAB color space, is proposed in this paper. The proposed technique yields better discrimination capability, sharper and stronger correlation peak intensity, compared to classical joint transform correlator with conventional red-green-blue (RGB) components. Simulation results verify the robustness of the proposed technique.
To achieve scale and rotation invariant pattern recognition, we implemented synthetic discriminant function (SDF) based reference image in a nonzero order Fringe-adjusted Joint Transform Correlator (FJTC) using binary random phase mask. The binary random phase mask encodes the SDF based reference image before it is introduced in the joint input image. The joint power spectrum is then multiplied by the 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. Detailed analysis for the proposed SDF based nonzero order FJTC using binary random phase mask is presented. Simulation results verify the effectiveness of the proposed technique.
A three-dimensional (3D) pattern recognition technique using classical joint transform correlation was proposed recently. However, it exhibits the same drawbacks of classical two-dimensional classical joint transform correlation by producing wide and broad correlation peak in the output plane. Thus, we propose a technique based on fringe-adjusted joint transform correlation to be used in 3D pattern recognition. The proposed technique yields better correlation discrimination ability compared to alternate 3D classical joint transform correlation by producing sharper and stronger correlation peak intensity. Simulation results verify the performance of the proposed technique.
Several metrics for quantifying the performance of fringe-adjusted joint transform correlator (JTC) technique are investigated in this paper. The criteria used for measuring the performance of fringe-adjusted JTC include peak sharpness, signal-to-clutter measure, distortion invariance, signal-to-noise ratio and a new metric called peak-to-background correlation energy is proposed in this paper. These metrics are used to estimate the reliability of signal detection in the input scene with respect to clutter, noise and other associated distortions. Detailed analysis and simulation results for quantifying the performance of fringe-adjusted JTC are presented.
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