A rotation-invariant pattern recognition using spectral fringe-adjusted joint transform correlator and histogram representation

Abstract

A new rotation-invariant pattern recognition technique, based on spectral fringe-adjusted joint transform correlator (SFJTC) and histogram representation, is proposed. Synthetic discriminant function (SDF) based joint transform correlation (JTC) techniques have shown attractive performance in rotation-invariant pattern recognition applications. However, when the targets present in a complex scene, SDF-based JTC techniques may produce false detections due to inaccurate estimation of rotation angle of the object. Therefore, we herein propose an efficient rotation-invariant JTC scheme which does not require a priori rotation training of the reference image. In the proposed technique, a Vectorized Gaussian Ringlet Intensity Distribution (VGRID) descriptor is also proposed to obtain rotation-invariant features from the reference image. In this step, we divide the reference image into multiple Gaussian ringlets and extract histogram distribution of each ringlet, and then concatenate them into a vector as a target signature. Similarly, an unknown input scene is also represented by the VGRID which produces a multidimensional input image. Finally, the concept of the SFJTC is incorporated and utilized for target detection in the input scene. The classical SFJTC was proposed for detecting very small objects involving only few pixels in hyperspectral imagery. However, in our proposed algorithm, the SFJTC is applied for a two-dimensional image without limitation of the size of objects and most importantly it achieves rotation-invariant target discriminability. Simulation results verify that the proposed scheme performs satisfactorily in detecting targets in the input scene irrespective of rotation of the object.