Abstract

We propose a new rotation invariant correlator using dimensionality reduction. A diffractive phase element is used to focus image data into a line which serves as input for a conventional correlator. The diffractive element sums information over each radius of the scene image and projects the result onto one point of a line located at a certain distance behind the image. The method is flexible, to a large extent, and might include parallel pattern recognition and classification as well as further geometrical invariance. Although the new technique is inspired from circular harmonic decomposition, it does not suffer from energy loss. A theoretical analysis, as well as examples, are given.

Highlights

  • Many methods have been used to achieve rotation invariant pattern recognition [1]

  • One of the frequently used filters is the circular harmonic filter (CHF). The correlation using such a filter is invariant under the rotation of the scene image but suffers from the defects that result from only one circular harmonic component (CHC) being used

  • This paper presents a rotation invariant approach based on the optical lossless implementation of one circular harmonic component by means of a diffractive optical element

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Summary

Introduction

Many methods have been used to achieve rotation invariant pattern recognition [1]. Recent methods are based on deep learning techniques [2,3]. One of the frequently used filters is the circular harmonic filter (CHF) The correlation using such a filter is invariant under the rotation of the scene image but suffers from the defects that result from only one circular harmonic component (CHC) being used. The correlation peak is not sufficiently sharp To relax such a limitation, various methods have been proposed. This paper presents a rotation invariant approach based on the optical lossless implementation of one circular harmonic component by means of a diffractive optical element. Both the scene image and reference are subjected to a projection onto one CHC.

Related Works
Optical Implementation
Parallel Pattern Classification
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