Amoeba Techniques for Shape and Texture Analysis

TL;DR

This paper reviews morphological amoebas, image-adaptive filters based on local metrics combining spatial and contrast information, highlighting their relation to PDE filters and their application in texture analysis.

Contribution

It summarizes existing work on amoeba filters, explores their connection to PDE-based methods, and discusses extensions and future research directions.

Findings

Amoeba filters are asymptotically equivalent to certain PDE filters.

Amoebas encode local texture information through graph structures.

The paper identifies potential for extending amoeba techniques in image analysis.

Abstract

Morphological amoebas are image-adaptive structuring elements for morphological and other local image filters introduced by Lerallut et al. Their construction is based on combining spatial distance with contrast information into an image-dependent metric. Amoeba filters show interesting parallels to image filtering methods based on partial differential equations (PDEs), which can be confirmed by asymptotic equivalence results. In computing amoebas, graph structures are generated that hold information about local image texture. This paper reviews and summarises the work of the author and his coauthors on morphological amoebas, particularly their relations to PDE filters and texture analysis. It presents some extensions and points out directions for future investigation on the subject.