Shift, Scale and Rotation Invariant Multiple Object Detection using Balanced Joint Transform Correlator

The Polar Mellin Transform (PMT) is a well-known technique that converts images into shift, scale and rotation invariant signatures for object detection using opto-electronic correlators. However, this technique cannot be properly applied when there are multiple targets in a single input. Here, we propose a Segmented PMT (SPMT) that extends this methodology for cases where multiple objects are present within the same frame. Simulations show that this SPMT can be integrated into an opto-electronic joint transform correlator to create a correlation system capable of detecting multiple objects simultaneously, presenting robust detection capabilities across various transformation conditions, with remarkable discrimination between matching and non-matching targets.

Balanced Opto-electronic Joint Transform Correlator for Enhanced Real-Time Pattern Recognition

Opto-electronic joint transform correlators (JTCs) use a focal plane array (FPA) to detect the joint power spectrum (JPS) of two input images, projecting it onto a spatial light modulator (SLM) to be optically Fourier transformed. The JPS is composed of two self-intensities and two conjugate-products, where only the latter produce the cross-correlation. However, the self-intensity terms are typically much stronger than the conjugate-products, consuming most of the available bit-depth on the FPA and SLM. Here we propose and demonstrate, through simulation and experiment, a balanced opto-electronic JTC that electronically processes the JPS to remove the self-intensity terms, thereby enhancing the quality of the cross-correlation result.