Artificial Intelligence and Machine Learning in Optical Information Processing: introduction to the feature issue.

This special feature issue covers the intersection of topical areas in artificial intelligence (AI)/machine learning (ML) and optics. The papers broadly span the current state-of-the-art advances in areas including image recognition, signal and image processing, machine inspection/vision and automotive as well as areas of traditional optical sensing, interferometry and imaging.

Artificial neural networks using complex numbers and phase encoded weights.

The model of a simple perceptron using phase-encoded inputs and complex-valued weights is proposed. The aggregation function, activation function, and learning rule for the proposed neuron are derived and applied to Boolean logic functions and simple computer vision tasks. The complex-valued neuron (CVN) is shown to be superior to traditional perceptrons.

What can we learn from the shape of a correlation peak for position estimation?

Matched filtering is a robust technique to identify and locate objects in the presence of noise. Traditionally, the amplitude of the correlation peak is used for detection of a match. However, when distinguishing objects that are not significantly different or detecting objects under high noise imaging conditions, the normalized peak amplitude alone may not provide sufficient discrimination.

Sub-imaging technique to improve phase only filter search capability.

A novel application of a phase only filter model, which is implementable in the optical domain, is proposed. In this application, automated target tracking is accomplished with a novel sub-imaging technique with correlation tracking inside a radius of interest. In this technique the image is subdivided and the correlation is tracked by comparing only the autocorrelation of the filter with itself.