The objective of this research is to help increase speed, quality, and productivity of shape handling in practice. Geometric shapes are at the core of a wide range of cutting-edge technological sectors including computer vision, computer aided design (CAD), robotics, bioinformatics, computational biology, medical imaging, geographical information systems (GIS), and drug design, in which a multitude of tasks for manipulating and handling geometric shapes have to be performed efficiently and reliably.
This research is based on two research tracks: (i) shape handling applications and (ii) shape handling theory. The investigator will continue to foster interdisciplinary communication, contribute more theoretical soundness to applied problems, and pursue a stronger theoretical foundation which can be the basis for a wider range of applications. Specifically, this research involves several applied projects including, but not limited to, computational proteomics, computational neuroscience, and spatiotemporal traffic databases. Semi-automatic algorithms will be combined with theoretical expertise in order to pave the road for high-throughput processing in areas with very noisy data. Theoretical projects include matching and distance measure problems for curves and surfaces, multi-curve matching, initiation of a general study of geodesic distance measures in which distances are measured using shortest paths on a surface, and shape simplification. Lower bounds will be investigated in order to gain better insight into the structure of the problems, and application-friendly algorithms such as output-sensitive algorithms and approximation algorithms will be devised in order to better cope with outliers and noisy inputs.
