Hypergraph imaging: an overview

Alain Bretto

doi:10.1016/S0031-3203(01)00067-X

Return

Hypergraph imaging: an overview

2002-03-01

PRE

Alain Bretto

Hocine Cherifi

Driss Aboutajdine

DOI：10.1016/S0031-3203(01)00067-X

Original

Original request for help

Save

Abstract

En 中文

Hypergraph theory as originally developed by Berge (Hypergraphe, Dunod, Pat-is, 1987) is a theory of finite combinatorial sets, modeling lot of problems of operational research and combinatorial optimization. This framework turns out to be very interesting For many other applications, in particular for computer vision. In this paper, we are going to survey the relationship between combinatorial sets and image processing. More precisely, we propose an overview of different applications from image hypergraph models to image analysis. It mainly focuses on the combinatorial representation of an image and shows the effectiveness of this approach to low level image processing; in particular to segmentation, edge detection and noise cancellation, (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

Keywords:

combinatorial optimization

image processing

image model

segmentation

edge detection

noise reduction

hypergraph

graph

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.