Author ORCID Identifier
Year of Publication
Doctor of Philosophy (PhD)
Dr. Nathan Jacobs
The ultimate goal of image understanding is to transfer visual images into numerical or symbolic descriptions of the scene that are helpful for decision making. Knowing when, where, and in which direction a picture was taken, the task of geo-calibration makes it possible to use imagery to understand the world and how it changes in time. Current models for geo-calibration are mostly deterministic, which in many cases fails to model the inherent uncertainties when the image content is ambiguous. Furthermore, without a proper modeling of the uncertainty, subsequent processing can yield overly confident predictions. To address these limitations, we propose a probabilistic model for camera geo-calibration using deep neural networks. While our primary contribution is geo-calibration, we also show that learning to geo-calibrate a camera allows us to implicitly learn to understand the content of the scene.
Digital Object Identifier (DOI)
NSF CAREER grant IIS-1553116
ARPA-E Award DE-AR0000594
Google Faculty Research Award
AWS Research Education grant
Intelligence Advanced Research Projects Activity (IARPA) via Air Force Research Laboratory, contract FA8650-12-C-7212
DARPA (contract CSSG D11AP00255)
Zhai, Menghua, "Deep Probabilistic Models for Camera Geo-Calibration" (2018). Theses and Dissertations--Computer Science. 74.