Stable Segmentation of Digital Image

08/13/2012
by   M. Kharinov, et al.
0

In the paper the optimal image segmentation by means of piecewise constant approximations is considered. The optimality is defined by a minimum value of the total squared error or by equivalent value of standard deviation of the approximation from the image. The optimal approximations are defined independently on the method of their obtaining and might be generated in different algorithms. We investigate the computation of the optimal approximation on the grounds of stability with respect to a given set of modifications. To obtain the optimal approximation the Mumford-Shuh model is generalized and developed, which in the computational part is combined with the Otsu method in multi-thresholding version. The proposed solution is proved analytically and experimentally on the example of the standard image.

READ FULL TEXT

page 3

page 5

research
06/10/2013

Image segmentation by optimal and hierarchical piecewise constant approximations

Piecewise constant image approximations of sequential number of segments...
research
04/13/2016

Reversible Image Merging for Low-level Machine Vision

In this paper a hierarchical model for pixel clustering and image segmen...
research
01/23/2014

Hierarchical pixel clustering for image segmentation

In the paper a piecewise constant image approximations of sequential num...
research
02/10/2022

Γ-Convergence of an Ambrosio-Tortorelli approximation scheme for image segmentation

Given an image u_0, the aim of minimising the Mumford-Shah functional is...
research
08/04/2016

An efficient iterative thresholding method for image segmentation

We proposed an efficient iterative thresholding method for multi-phase i...
research
03/04/2019

Algorithms for Piecewise Constant Signal Approximations

We consider the problem of finding optimal piecewise constant approximat...
research
09/08/2021

Lagrange-Chebyshev Interpolation for image resizing

Image resizing is a basic tool in image processing and in literature we ...

Please sign up or login with your details

Forgot password? Click here to reset