Dissertation Defense: "Obstacle-Avoiding Similarity Metrics and Shortest-Path Problems" by Atlas F. Cook IV
Date: October 20, 2009
Time: 9:00 am – 11:00 am
Where: SB 4.01.20 (CS Conference Room)
Dissertation Defense
"Obstacle-Avoiding Similarity Metrics and Shortest-Path Problems"
by Atlas F. Cook IV
Abstract:
Similarity metrics are functions that measure the similarity of geometric objects. The motivation for studying similarity metrics is that these functions are essential building blocks for areas such as computer vision, robotics, medical imaging, and drug design. Although similarity metrics are traditionally computed in environments without obstacles, we use shortest paths to compute similarity metrics in simple polygons, inpolygons with polygonal holes, and on polyhedral surfaces. We measure the length of a path either by Euclidean distance or by the number of turns on the path.
We also compute shortest paths that steer a medical needle through a sequence of treatment points in the plane. This problem is interesting because it could be used in biopsy procedures to take multiple tissue samples with a single puncture of the skin. Such an algorithm could also be applied to brachytherapy procedures that implant radioactive pelletsat many cancerous locations. Computing shortest paths for medical needles is a challenging problem because medical needles cut through tissue along circular arcs and have a limited ability to turn. Although optimal substructure can fail, we are able to compute globally optimal paths with a wavefront propagation technique.
DISSERTATION COMMITTEE:
Dr. Carola Wenk (chair)
Dr. Tom Bylander
Dr. Jose Iovino
Dr. Jianhua Ruan
Dr. Weining Zhang