A Study of Morphological Filtering Using Graph and Hypergraphs

TL;DR

This paper evaluates the effectiveness of hypergraph-based morphological filters in binary image processing, demonstrating their superior performance over graph-based filters through experimental results.

Contribution

It introduces hypergraph-based morphological filtering and compares its performance with graph-based methods, showing improved results in binary image analysis.

Findings

Hypergraph-based ASF filters outperform graph-based ASF filters.

Experimental results confirm the superior effectiveness of hypergraph filtering.

Hypergraph morphology offers better shape analysis in binary images.

Abstract

Mathematical morphology (MM) helps to describe and analyze shapes using set theory. MM can be effectively applied to binary images which are treated as sets. Basic morphological operators defined can be used as an effective tool in image processing. Morphological operators are also developed based on graph and hypergraph. These operators have found better performance and applications in image processing. Bino et al. [8], [9] developed the theory of morphological operators on hypergraph. A hypergraph structure is considered and basic morphological operation erosion/dilation is defined. Several new operators opening/closing and filtering are also defined on the hypergraphs. Hypergraph based filtering have found comparatively better performance with morphological filters based on graph. In this paper we evaluate the effectiveness of hypergraph based ASF on binary images. Experimental results shows that hypergraph based ASF filters have outperformed graph based ASF.