Heat Kernel Smoothing in Irregular Image Domains

10/21/2017
by   Moo K. Chung, et al.
0

We present the discrete version of heat kernel smoothing on graph data structure. The method is used to smooth data in an irregularly shaped domains in 3D images. New statistical properties are derived. As an application, we show how to filter out data in the lung blood vessel trees obtained from computed tomography. The method can be further used in representing the complex vessel trees parametrically and extracting the skeleton representation of the trees.

READ FULL TEXT

page 3

page 4

page 5

page 8

research
07/27/2020

Diffusion Equations on Graphs

In brain imaging, the image acquisition and processing processes themsel...
research
06/16/2021

Covariance-based smoothed particle hydrodynamics. A machine-learning application to simulating disc fragmentation

A PCA-based, machine learning version of the SPH method is proposed. In ...
research
12/11/2013

Heat kernel coupling for multiple graph analysis

In this paper, we introduce heat kernel coupling (HKC) as a method of co...
research
09/04/2006

An effective edge--directed frequency filter for removal of aliasing in upsampled images

Raster images can have a range of various distortions connected to their...
research
11/07/2019

Dynamic Connectivity without Sliding Windows

Objective: Sliding and tapered sliding window methods are the most often...
research
11/04/2016

Learning heat diffusion graphs

Effective information analysis generally boils down to properly identify...

Please sign up or login with your details

Forgot password? Click here to reset