Fast geodesic distance approximation using mesh decimation and front propagation

2009 ◽  
Author(s):  
Joachim Giard ◽  
Benoît Macq
Keyword(s):  
Geodesic Distance ◽  
Front Propagation ◽  
Distance Approximation ◽  
Mesh Decimation

scholarly journals Geodesic Computations on Surfaces

2013 ◽  
Author(s):  
Karthik Krishnan
Keyword(s):  
Geodesic Distance ◽  
Front Propagation ◽  
Geodesic Curve ◽  
Fast Marching Method ◽  
Triangle Mesh ◽  
Fast Marching ◽  
Geometry Processing ◽  
Distance Field ◽  
Triangulated Surface ◽  
Termination Criteria

The computation of geodesic distances on a triangle mesh has many applications in geometry processing. The fast marching method provides an approximation of the true geodesic distance field. We provide VTK classes to compute geodesics on triangulated surface meshes. This includes classes for computing the geodesic distance field from a set of seeds and to compute the geodesic curve between source and destination point(s) by back-tracking along the gradient of the distance field. The fast marching toolkit (Peyre et. al.) is internally used. A variety of options are exposed to guide front propagation including the ability to specify propagation weights, constrain to a region, specify exclusion regions, and distance based termination criteria. Interpolators that plug into a contour widget, are provided to enable interactive tracing of paths on meshes.

Novel geodesic distance Fourier shape descriptor

2010 ◽  
Vol 30 (2) ◽  
pp. 362-363
Author(s):  
Sheng CHEN ◽  
Xun LIU
Keyword(s):  
Geodesic Distance ◽  
Shape Descriptor

scholarly journals Massively Parallel Algorithms for Distance Approximation and Spanners

2021 ◽  
Author(s):  
Amartya Shankha Biswas ◽  
Michal Dory ◽  
Mohsen Ghaffari ◽  
Slobodan Mitrović ◽  
Yasamin Nazari
Keyword(s):  
Parallel Algorithms ◽  
Massively Parallel ◽  
Distance Approximation

scholarly journals Some Information Geometric Aspects of Cyber Security by Face Recognition

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 878
Author(s):  
C. T. J. Dodson ◽  
John Soldera ◽  
Jacob Scharcanski
Keyword(s):  
Face Recognition ◽  
Cyber Security ◽  
Probability Distributions ◽  
Geodesic Distance ◽  
Recognition Method ◽  
Frequency Distributions ◽  
Face Images ◽  
User Access ◽  
Multidimensional Methods ◽  
Joint Probabilities

Secure user access to devices and datasets is widely enabled by fingerprint or face recognition. Organization of the necessarily large secure digital object datasets, with objects having content that may consist of images, text, video or audio, involves efficient classification and feature retrieval processing. This usually will require multidimensional methods applicable to data that is represented through a family of probability distributions. Then information geometry is an appropriate context in which to provide for such analytic work, whether with maximum likelihood fitted distributions or empirical frequency distributions. The important provision is of a natural geometric measure structure on families of probability distributions by representing them as Riemannian manifolds. Then the distributions are points lying in this geometrical manifold, different features can be identified and dissimilarities computed, so that neighbourhoods of objects nearby a given example object can be constructed. This can reveal clustering and projections onto smaller eigen-subspaces which can make comparisons easier to interpret. Geodesic distances can be used as a natural dissimilarity metric applied over data described by probability distributions. Exploring this property, we propose a new face recognition method which scores dissimilarities between face images by multiplying geodesic distance approximations between 3-variate RGB Gaussians representative of colour face images, and also obtaining joint probabilities. The experimental results show that this new method is more successful in recognition rates than published comparative state-of-the-art methods.

WEIGHTED AVERAGE GEODESIC DISTANCE OF PENTADENDRITE NETWORKS

Fractals ◽  
2020 ◽  
Vol 28 (05) ◽  
pp. 2050075
Author(s):  
YUANYUAN LI ◽  
XIAOMIN REN ◽  
KAN JIANG
Keyword(s):  
Complex Networks ◽  
Geodesic Distance ◽  
Average Distance ◽  
Weighted Average ◽  
Similar Measure ◽  
Important Index ◽  
Self Similar

The average geodesic distance is an important index in the study of complex networks. In this paper, we investigate the weighted average distance of Pentadendrite fractal and Pentadendrite networks. To provide the formula, we use the integral of geodesic distance in terms of self-similar measure with respect to the weighted vector.

Infiltration-mediated combustion in quasi-2D geometry: Stability of front propagation

2010 ◽  
Vol 19 (2) ◽  
pp. 79-84 ◽  
Author(s):  
P. M. Krishenik ◽  
N. I. Ozerkovskaya ◽  
A. N. Firsov ◽  
K. G. Shkadinsky
Keyword(s):  
Front Propagation
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