提出新方法实现多目标在平移缩放旋转下的同时检测
Shift, Scale and Rotation Invariant Multiple Object Detection using Balanced Joint Transform Correlator
- 将极坐标梅林变换分段处理，适配单帧多目标场景
- 仿真验证系统可在多种变换下同时识别多个目标
- 适合光学相关器架构的实时多目标检测应用
极坐标梅林变换（PMT）是一种经典的图像特征提取技术，可生成对平移、缩放和旋转不变的物体检测签名，适用于光电相关器。然而，当输入图像中存在多个目标时，该方法难以有效应用。本文提出分段极坐标梅林变换（SPMT），扩展了PMT方法以应对单帧内多个物体的情况。仿真结果表明，SPMT可集成至光电联合变换相关器中，构建出能在多种变换条件下同时检测多个目标的关联系统，表现出对匹配与非匹配目标间良好的区分能力，具备较强的鲁棒性。
原文摘要 · Abstract (English)
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.
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