| Paper: | WP-P2.2 |
| Session: | Biomedical Imaging V: Molecular & Cellular Bioimaging / Segmentation |
| Time: | Wednesday, September 19, 14:30 - 17:10 |
| Presentation: |
Poster
|
| Title: |
EFFICIENT ACQUISITION AND LEARNING OF FLUORESCENCE MICROSCOPE DATA MODELS |
| Authors: |
Charles Jackson; Carnegie Mellon University | | |
| | Robert Murphy; Carnegie Mellon University | | |
| | Jelena Kovacevic; Carnegie Mellon University | | |
| Abstract: |
We present a method for efficient acquisition of fluorescence microscope datasets, to allow for higher spatial and temporal resolution, and with less damage from photobleaching. Our proposal is to restrict acquisition to regions where we expect to find an object. Given that the objects are continuously moving, we must have an accurate model to describe objects' motion to predict their future locations. We outline a system for learning and applying this motion model, demonstrate its application in a case study, and summarize results from more complex applications. |
