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

机译:用于图像处理和图像分析的基于图的表示形式和技术

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摘要

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 Robotica i Informatica 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 front 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. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved. [References: 21]
机译:在本文中,我们将讨论一些基于图形的表示形式和技术在图像处理和分析中的使用,而不是对该领域的图形技术进行全面的回顾,我们将解释我们如何在主动视觉系统中使用这些技术。适用于在Informatica工业机器人研究所开发的自主移动机器人,项目为“具有工业应用自动学习能力的主动视觉系统(CICYT TAP98-0473)”。具体来说,我们将讨论基于图的表示形式和技术在图像分割,图像感知分组和对象识别中的使用。我们首先提出用于彩色图像分割的图分区贪婪算法的概括。接下来,我们描述基于颜色的分割和深度前立体的新颖融合,产生代表场景中每个对象的图形。最后,我们描述了一组属性图(AG),标称功能描述图(FDG),将AG与FDG匹配的距离度量以及机器人视觉的一些应用的新表示形式。 (C)2001模式识别学会。由Elsevier Science Ltd.出版。保留所有权利。 [参考:21]

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