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

arXiv: https://arxiv.org/abs/2503.14034 · HTML: https://mlsanity.com/paper/2503.14034/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Builds on (most cited first, 3 of 3)

- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [High-speed Opto-electronic Pre-processing of Polar Mellin Transform for Shift, Scale and Rotation Invariant Image Recognition at Record-Breaking Speeds](https://mlsanity.com/paper/2312.06864/) (2023)
- [Ultra-fast Real-time Target Recognition Using a Shift, Scale, and Rotation Invariant Hybrid Opto-electronic Joint Transform Correlator](https://mlsanity.com/paper/2501.19299/) (2025)

## Abstract (arXiv)

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.