Robust Estimation of Multiple Inlier Structures

09/20/2016
by   Xiang Yang, et al.
0

The robust estimator presented in this paper processes each structure independently. The scales of the structures are estimated adaptively and no threshold is involved in spite of different objective functions. The user has to specify only the number of elemental subsets for random sampling. After classifying all the input data, the segmented structures are sorted by their strengths and the strongest inlier structures come out at the top. Like any robust estimators, this algorithm also has limitations which are described in detail. Several synthetic and real examples are presented to illustrate every aspect of the algorithm.

READ FULL TEXT

page 8

page 9

page 10

page 11

research
09/26/2017

Scale Adaptive Clustering of Multiple Structures

We propose the segmentation of noisy datasets into Multiple Inlier Struc...
research
07/24/2018

Deterministic Fitting of Multiple Structures using Iterative MaxFS with Inlier Scale Estimation and Subset Updating

We present an efficient deterministic hypothesis generation algorithm fo...
research
11/07/2014

Efficient Estimation of Mutual Information for Strongly Dependent Variables

We demonstrate that a popular class of nonparametric mutual information ...
research
04/11/2018

Improved Horvitz-Thompson Estimator in Survey Sampling

The Horvitz-Thompson (HT) estimator is widely used in survey sampling. H...
research
05/17/2020

Robust subset selection

The best subset selection (or "best subsets") estimator is a classic too...
research
06/08/2017

Automatic tracking of vessel-like structures from a single starting point

The identification of vascular networks is an important topic in the med...
research
05/20/2020

Model Repair: Robust Recovery of Over-Parameterized Statistical Models

A new type of robust estimation problem is introduced where the goal is ...

Please sign up or login with your details

Forgot password? Click here to reset