Pre Registration

Image registration, the process of aligning images from different sources or modalities, is a crucial preprocessing step in many scientific fields, aiming to establish spatial correspondence between datasets for improved analysis and interpretation. Current research focuses on developing robust and efficient registration methods across diverse data types, including point clouds, medical images (MRI, CT, ultrasound, histology), and hyperspectral data, employing techniques like deep learning (e.g., U-Net, transformers), optimization algorithms (e.g., gradient descent, CMA-ES), and novel loss functions tailored to specific data characteristics. These advancements are significantly impacting various applications, from medical image analysis (e.g., improved diagnostics and surgical planning) to robotics and computer vision (e.g., accurate 3D scene reconstruction and object manipulation).