Probabilistic Inference for Camera Calibration in Light Microscopy Under Circular Motion

IEEE International Symposium on Biomedical Imaging (ISBI), pp. 149–153

Abstract

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 these 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.

Authors 3

1. Yuanhao Guo

   Chinese Academy of Sciences · Institute of Automation

   Affiliation as printed

   Computational Biology and Machine Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences

2. Fons J. Verbeek Aachen

   Leiden University

   Affiliation as printed

   LIACS, Bioinformatics and Imaging, Leiden University

3. Ge Yang

   University of Chinese Academy of Sciences

   Affiliation as printed

   School of Artificial Intelligence, University of Chinese Academy of Sciences