אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Finding dense intrinsic correspondence between non-rigid shapes is a notoriously difficult problem with many important applications in computer graphics and pattern recognition.

In the first part of the talk, I will present a novel sparse modeling approach to non-rigid shape matching using only the ability to detect repeatable regions. As the input to our algorithm, we are given only two sets of regions in two shapes; no descriptors are provided so the correspondence between the regions is not know, nor we know how many regions correspond in the two shapes. I will show that even with such scarce information, it is possible to establish very accurate correspondence between the shapes by posing it as a problem of permuted sparse coding.

In the second part of the talk, I will show how to extend the method to the setting of non-isometric shapes using quasi-harmonic bases constructed by joint approximate diagonalization of Laplacian matrices.