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.
Code (0)
등록된 구현이 없습니다.
Tasks
object-detectionObject DetectionSimilar Papers 제목 키워드 기반
Scale-, shift- and rotation-invariant diffractive optical networks
Recent research efforts in optical computing have gravitated towards developing optical neural networks that aim to benefit from the processing speed and parallelism of optics/photonics in machine learning applications. …
image-classificationImage ClassificationTranslationREMM:Rotation-Equivariant Framework for End-to-End Multimodal Image Matching
We present REMM, a rotation-equivariant framework for end-to-end multimodal image matching, which fully encodes rotational differences of descriptors in the whole matching pipeline. Previous learning-based methods mainly…
BenchmarkingSelf-supervised Learning of Rotation-invariant 3D Point Set Features using Transformer and its Self-distillation
Invariance against rotations of 3D objects is an important property in analyzing 3D point set data. Conventional 3D point set DNNs having rotation invariance typically obtain accurate 3D shape features via supervised lea…
Data AugmentationSelf-Supervised LearningRotation-Invariant Point Convolution With Multiple Equivariant Alignments
Recent attempts at introducing rotation invariance or equivariance in 3D deep learning approaches have shown promising results, but these methods still struggle to reach the performances of standard 3D neural networks. I…
Deep LearningSemantic SegmentationPaRot: Patch-Wise Rotation-Invariant Network via Feature Disentanglement and Pose Restoration
Recent interest in point cloud analysis has led rapid progress in designing deep learning methods for 3D models. However, state-of-the-art models are not robust to rotations, which remains an unknown prior to real applic…
3D Object ClassificationDisentanglement