Samenvatting
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, in polygons 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 technique 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 pellets at 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 compute
globally optimal paths with a wavefront propagation technique.
Originele taal2  Engels 

Kwalificatie  Doctor in de Filosofie 
Toekennende instantie 

Begeleider(s)/adviseur 

Datum van toekenning  20 okt 2009 
Plaats van publicatie  San Antonio 
Uitgever  
Status  Gepubliceerd  2009 
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Citeer dit
Cook IV, A. F. (2009). Obstacleavoiding similarity metrics and shortestpath problems. University of Texas at San Antonio.