אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Non-rigid shape similarity is an important problem in computer vision and pattern recognition. Broadly speaking, non-rigid shape similarity criteria are divided into intrinsic and extrinsic, the first referring to the metric structure of the objects and the latter to the geometry of the shapes in the Euclidean space. Both criteria have their advantages and disadvantages; extrinsic similarity is sensitive to non-rigid deformations of the shapes, while intrinsic similarity is sensitive to topological noise. In this talk, we present an approach unifying both criteria in a single measure. We consider the tradeoff between the extrinsic and intrinsic similarity and use it as a set-valued "distance" related to the notion of Pareto optimality in economics. We will show cases where using either extrinsic or intrinsic similarity criteria alone would fail whereas our approach produces meaningful results.