Reversible Image Merging for Low-level Machine Vision

TL;DR

This paper introduces a hierarchical, reversible image merging model for pixel clustering and segmentation, enabling efficient image approximation and structural analysis in low-level machine vision tasks.

Contribution

It proposes a novel hierarchical model that allows reversible merging of image segments, improving pixel clustering and segmentation accuracy.

Findings

Hierarchical clustering effectively simulates optimal pixel clustering.

The model generates a hierarchy of piecewise constant image approximations.

Convex sequences of total squared errors facilitate hierarchy conversion.

Abstract

In this paper a hierarchical model for pixel clustering and image segmentation is developed. In the model an image is hierarchically structured. The original image is treated as a set of nested images, which are capable to reversibly merge with each other. An object is defined as a structural element of an image, so that, an image is regarded as a maximal object. The simulating of none-hierarchical optimal pixel clustering by hierarchical clustering is studied. To generate a hierarchy of optimized piecewise constant image approximations, estimated by the standard deviation of approximation from the image, the conversion of any hierarchy of approximations into the hierarchy described in relation to the number of intensity levels by convex sequence of total squared errors is proposed.