Shift, Scale and Rotation Invariant Multiple Object Detection using Balanced Joint Transform Correlator
This work addresses a specific limitation in object detection for opto-electronic systems, offering an incremental improvement for scenarios with multiple targets in a single frame.
The paper tackled the problem of detecting multiple objects in a single image under shift, scale, and rotation transformations by proposing a Segmented Polar Mellin Transform (SPMT) that extends the existing Polar Mellin Transform. Simulations showed that SPMT integrated into an opto-electronic correlator enables robust simultaneous detection of multiple objects with high discrimination between matching and non-matching targets.
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.