Heuristic algorithm for 1D and 2D unfolding

10/17/2014
by   Yordan Karadzhov, et al.
0

A very simple heuristic approach to the unfolding problem will be described. An iterative algorithm starts with an empty histogram and every iteration aims to add one entry to this histogram. The entry to be added is selected according to a criteria which includes a χ^2 test and a regularization. After a relatively small number of iterations (500 - 1000) the growing reconstructed distribution converges to the true distribution.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/20/2020

Analysis of the quotation corpus of the Russian Wiktionary

The quantitative evaluation of quotations in the Russian Wiktionary was ...
research
11/29/2019

Location histogram privacy by sensitive location hiding and target histogram avoidance/resemblance (extended version)

A location histogram is comprised of the number of times a user has visi...
research
02/17/2010

Iterative exact global histogram specification and SSIM gradient ascent: a proof of convergence, step size and parameter selection

The SSIM-optimized exact global histogram specification (EGHS) is shown ...
research
01/22/2021

Propagation and reconstruction of re-entry uncertainties using continuity equation and simplicial interpolation

This work proposes a continuum-based approach for the propagation of unc...
research
02/12/2020

FPGA Implementation of Minimum Mean Brightness Error Bi-Histogram Equalization

Histogram Equalization (HE) is a popular method for contrast enhancement...
research
09/09/2021

Almost sure convergence of the accelerated weight histogram algorithm

The accelerated weight histogram (AWH) algorithm is an iterative extende...
research
09/20/2022

Iterative Poisson Surface Reconstruction (iPSR) for Unoriented Points

Poisson surface reconstruction (PSR) remains a popular technique for rec...

Please sign up or login with your details

Forgot password? Click here to reset