Global Optimization Methods for Computer Vision
A multitude of computer vision challenges can be cast as problems of energy minimization. One of the major scientific challenges lies in efficiently computing solutions of minimal energy. I will introduce optimization methods which address problems such as the segmentation of objects in image sequences, the reconstruction of 3D shapes from a collection of 2D images or the computation of distances between 3D shapes. I will detail how respective cost functions can be minimized using either polynomial-time graph theoretic approaches or continuous convex optimization methods.
In contrast to most traditional optimization methods, the proposed algorithms do not require an initialization and allow to compute robust solutions with guaranteed optimality properties.
Daniel Cremers received Bachelor degrees in Mathematics (1994) and Physics (1994), and a Master's degree in Theoretical Physics (1997) from the University of Heidelberg. In 2002 he obtained a PhD in Computer Science from the University of Mannheim, Germany. Subsequently he spent two years as a postdoctoral researcher at the University of California at Los Angeles (UCLA) and one year as a permanent researcher at Siemens Corporate Research in Princeton, NJ. From 2005 until 2009 he was associate professor at the University of Bonn, Germany. Since 2009 he holds the chair for Computer Vision and Pattern Recognition at the Technical University, Munich. His publications received several awards, including the award of Best Paper of the Year 2003 by the Int. Pattern Recognition Society and the
2005 UCLA Chancellor's Award for Postdoctoral Research. In December
2010 the magazine Capital listed Prof. Cremers among "Germany's Top 40 Researchers Below 40".