|Ph.D Student||Alterman Marina|
|Subject||Vision through Random Dynamic Distortions|
|Department||Department of Electrical Engineering||Supervisors||Professor Yoav Schechner|
|Professor Emeritus Joseph Shamir|
|Full Thesis text|
Random dynamic distortions naturally affect images taken through atmospheric turbulence or wavy water. We show how computer vision can function under such effects, and even exploit them, relying on physical, geometric and statistical models of refractive disturbances.
We make good use of distortions created by atmospheric turbulence: distorted multi-view videos lead to tomographic reconstruction of large-scale turbulence fields, outdoors. We also demonstrate several approaches to a 'virtual periscope', to view airborne scenes from submerged cameras: (a) multiple submerged views enable stochastic localization of airborne objects in 3D; (b) the wavy water surface (and hence distortion) can be passively estimated instantly, using a special sensor, analogous to modern astronomic telescopes and (c) we show how airborne moving objects can be automatically detected, despite dynamic distortions affecting the entire scene. In all these works, exploiting physical models in new ways leads to novel imaging tasks, while the approaches we take are demonstrated in field experiments.