Adaptive regularized noise smoothing of dense range image using directional Laplacian operators

Author(s):  
Jeong-Ho Shin ◽  
Yiyong Sun ◽  
Woongchan Jung ◽  
Joon-Ki Paik ◽  
Mongi A. Abidi
2000 ◽  
Author(s):  
Yiyong Sun ◽  
Joon-Ki Paik ◽  
J.R. Price ◽  
M.A. Abidi

2020 ◽  
Vol 2020 (10) ◽  
Author(s):  
James Bonifacio ◽  
Kurt Hinterbichler

Abstract A compact Riemannian manifold is associated with geometric data given by the eigenvalues of various Laplacian operators on the manifold and the triple overlap integrals of the corresponding eigenmodes. This geometric data must satisfy certain consistency conditions that follow from associativity and the completeness of eigenmodes. We show that it is possible to obtain nontrivial bounds on the geometric data of closed Einstein manifolds by using semidefinite programming to study these consistency conditions, in analogy to the conformal bootstrap bounds on conformal field theories. These bootstrap bounds translate to constraints on the tree-level masses and cubic couplings of Kaluza-Klein modes in theories with compact extra dimensions. We show that in some cases the bounds are saturated by known manifolds.


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