Publication

Graph-based representations and techniques for image processing and image analysis

Journal Article (2002)

Journal

Pattern Recognition

Pages

639-650

Volume

35

Number

3

Doc link

http://dx.doi.org/10.1016/S0031-3203(01)00066-8

File

Download the digital copy of the doc pdf document

Abstract

In this paper we will discuss the use of some graph-based representations and techniques for image processing and analysis. Instead of making an extensive review of the graph techniques in this field, we will explain how we are using these techniques in an active vision system for an autonomous mobile robot developed in the Institut de Robòtica i Informàtica Industrial within the project “Active Vision System with Automatic Learning Capacity for Industrial Applications (CICYT TAP98-0473)”. Specifically we will discuss the use of graph-based representations and techniques for image segmentation, image perceptual grouping and object recognition. We first present a generalisation of a graph partitioning greedy algorithm for colour image segmentation. Next we describe a novel fusion of colour-based segmentation and depth from stereo that yields a graph representing every object in the scene. Finally we describe a new representation of a set of attributed graphs (AGs), denominated function-described graphs (FDGs), a distance measure for matching AGs with FDGs and some applications for robot vision.

Categories

pattern recognition.

Author keywords

structural pattern recognition, graph-based representations, object recognition, color image segmentation, perceptual grouping, data fusion, depth from stereo, attributed graphs

Scientific reference

A. Sanfeliu, R. Alquézar Mancho, J. Andrade-Cetto, J. Climent, F. Serratosa i Casanelles and J. Vergés. Graph-based representations and techniques for image processing and image analysis. Pattern Recognition, 35(3): 639-650, 2002.