Adaptive nonlinear fringe-adjusted joint transform correlator

Abstract

An optimized technique, based on the fringe-adjusted joint transform correlator architecture, is proposed and validated for rotation invariant recognition and tracking of a target in an unknown input scene. To enhance the robustness of the proposed technique, we used a three-step optimization. First, we utilized the fringe-adjusted filter (H FAF ) in the Fourier plane, then we added nonlinear processing in the Fourier plane, and, finally, we used a new decision criterion in the correlation plane by considering the correlation peak energy and the highest peaks outside the desired correlation peak. Several tests were conducted to reduce the number of reference images needed for fast tracking, while ensuring robust discrimination and efficient tracking of the desired target. Test results, obtained using the pointing head pose image database, confirm robust performance of the proposed method for face recognition and tracking applications. Thereafter, we also tested the proposed technique for a challenging application such as underwater mine detection and excellent results were obtained.