Laplacian Mixture Model Point Based Registration

by   Mohammad Sadegh Majdi, et al.

Point base registration is an important part in many machine VISIOn applications, medical diagnostics, agricultural studies etc. The goal of point set registration is to find correspondences between different data sets and estimate the appropriate transformation that can map one set to another. Here we introduce a novel method for matching of different data sets based on Laplacian distribution. We consider the alignment of two point sets as probability density estimation problem. By using maximum likelihood methods we try to fit the Laplacian mixture model (LMM) centroids (source point set) to the data point set.


page 1

page 2

page 3

page 4


Robust Multi-view Registration of Point Sets with Laplacian Mixture Model

Point set registration is an essential step in many computer vision appl...

What was the river Ister in the time of Strabo? A mathematical approach

In this paper, we introduce a novel method for map registration and appl...

Casting graph isomorphism as a point set registration problem using a simplex embedding and sampling

Graph isomorphism is an important problem as its worst-case time complex...

Articulated Shape Matching Using Laplacian Eigenfunctions and Unsupervised Point Registration

Matching articulated shapes represented by voxel-sets reduces to maximal...

Nonrigid registration using Gaussian processes and local likelihood estimation

Surface registration, the task of aligning several multidimensional poin...

Evaluation of Robust Point Set Registration Applied to Automotive Doppler Radar

Point set registration is the process of finding the best alignment betw...

Please sign up or login with your details

Forgot password? Click here to reset