Name
Abstract | ||
|---|---|---|
This paper addresses the problem of reconstructing the density of a scene from multiple projection images produced by modalities such as x-ray, electron microscopy, etc. where an image value is related to the integral of the scene density along a 3D line segment between a radiation source and a point on the image plane. While computed tomography (CT) addresses this problem when the absolute orientation of the image plane and radiation source directions are known, this paper addresses the problem when the orientations are unknown - it is akin to the structure-from-motion (SFM) problem when the extrinsic camera parameters are unknown. We study the problem within the context of reconstructing the density of protein macro-molecules in Cryogenic Electron Microscopy (cryo-EM), where images are very noisy and existing techniques use several thousands of images. In a non-degenerate configuration, the viewing planes corresponding to two projections, intersect in a line in 3D. Using the geometry of the imaging setup, it is possible to determine the projections of this 3D line on the two image planes. In turn, the problem can be formulated as a type of orthographic structure from motion from line correspondences where the line correspondences between two views are unreliable due to image noise. We formulate the task as the problem of denoising a correspondence matrix and present a Bayesian solution to it. Subsequently, the absolute orientation of each projection is determined followed by density reconstruction. We show results on cryo-EM images of proteins and compare our results to that of Electron Micrograph Analysis (EMAN) -a widely used reconstruction tool in cryo-EM. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/CVPR.2006.295 | CVPR (2) |
Keywords | Field | DocType |
tomographic reconstruction,image noise,image plane,absolute orientation,bayesian approach,line correspondence,scene density,cryo-em image,line segment,view estimation,multiple projection image,image value,density reconstruction,image reconstruction,cryogenics,electron microscopy,structure from motion,image segmentation,layout,proteins,computed tomography,bayesian methods | Structure from motion,Iterative reconstruction,Line segment,Computer vision,Tomographic reconstruction,Orthographic projection,Computer science,Image plane,Image noise,Image segmentation,Artificial intelligence | Conference |
ISBN | Citations | PageRank |
0-7695-2597-0 | 6 | 0.86 |
References | Authors | |
4 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Satya P. Mallick | 1 | 228 | 10.70 |
| Sameer Agarwal | 2 | 10328 | 478.10 |
| David Kriegman | 3 | 7693 | 451.96 |
| Serge J. Belongie | 4 | 12512 | 1010.13 |
| Bridget Carragher | 5 | 24 | 3.15 |
| Clinton S. Potter | 6 | 31 | 5.48 |