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| October 27
| October 27
| Garrett Warnell
| Garrett Warnell
|
| Compressive Sensing in Visual Tracking
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|-
| November 3
| November 3
We seek to solve the face identification problem across variations in expression and lighting together in a single framework. In order to understand variations in expression, a dense correspondence between images must be found, leading to algorithms similar to Optical Flow. We present a new lighting-insensitive metric to drive this Optical Flow-like framework. An extension of this work to the manifold of face images is then proposed, where a curve on the manifold represents the way a face might morph through time, allowing pixels to vary slowly as properties of the face change. The length of the geodesic connecting a pair of faces defines their similarity for nearest neighbor matching.
We seek to solve the face identification problem across variations in expression and lighting together in a single framework. In order to understand variations in expression, a dense correspondence between images must be found, leading to algorithms similar to Optical Flow. We present a new lighting-insensitive metric to drive this Optical Flow-like framework. An extension of this work to the manifold of face images is then proposed, where a curve on the manifold represents the way a face might morph through time, allowing pixels to vary slowly as properties of the face change. The length of the geodesic connecting a pair of faces defines their similarity for nearest neighbor matching.
===Compressive Sensing in Visual Tracking===
Speaker: Garrett Warnell -- Date: October 27, 2011
Visual tracking is a classical computer vision task. However, the ubiquity of modern sensors makes it more difficult due to the large amount of data available for processing. The emerging theory of compressive sensing has the potential to address this problem in that it promises the ability to reduce the amount of data collected without sacrificing the amount of information within. In this talk, I will review recent research research toward the adaptation of some computer vision algorithms commonly used in visual tracking such that they can operate in the lower-dimensional compressive domain. Specifically, background subtraction and particle filtering will be discussed.
