Probabilistic Inference for Camera Calibration in Light Microscopy under Circular Motion
This addresses the challenge of robust camera calibration for researchers in microscopy and imaging, enabling high-fidelity 3D reconstructions and measurements, though it appears incremental as it builds on existing 3D projective geometry.
The paper tackles the problem of camera calibration for 3D reconstruction in light microscopy under circular motion, where conventional methods struggle with transparency or translucency, and proposes a probabilistic inference method that accurately recovers camera configurations without sophisticated image pre-processing.
Robust and accurate camera calibration is essential for 3D reconstruction in light microscopy under circular motion. Conventional methods require either accurate key point matching or precise segmentation of the axial-view images. Both remain challenging because specimens often exhibit transparency/translucency in a light microscope. To address those issues, we propose a probabilistic inference based method for the camera calibration that does not require sophisticated image pre-processing. Based on 3D projective geometry, our method assigns a probability on each of a range of voxels that cover the whole object. The probability indicates the likelihood of a voxel belonging to the object to be reconstructed. Our method maximizes a joint probability that distinguishes the object from the background. Experimental results show that the proposed method can accurately recover camera configurations in both light microscopy and natural scene imaging. Furthermore, the method can be used to produce high-fidelity 3D reconstructions and accurate 3D measurements.