Epipolar geometry estimation based on evolutionary agents

doi:10.1016/j.patcog.2007.06.016

Return

Epipolar geometry estimation based on evolutionary agents

2008-02-01

PRE

Mingxing Hu *

Stuart Ferguson

DOI：10.1016/j.patcog.2007.06.016

Original

Original request for help

Save

Abstract

En 中文

This paper presents a novel approach based on the use of evolutionary agents for epipolar geometry estimation. In contrast to conventional nonlinear optimization methods, the proposed technique employs each agent to denote a minimal subset to compute the fundamental matrix, and considers the data set of correspondences as a ID cellular environment, in which the agents inhabit and evolve. The agents execute some evolutionary behavior, and evolve autonomously in a vast solution space to reach the optimal (or near optima) result. Then three different techniques are proposed in order to improve the searching ability and computational efficiency of the original agents. Subset template enables agents to collaborate more efficiently with each other, and inherit accurate information from the whole agent set. Competitive evolutionary agent (CEA) and finite multiple evolutionary agent (FMEA) apply a better evolutionary strategy or decision rule, and focus on different aspects of the evolutionary process. Experimental results with both synthetic data and real images show that the proposed agent-based approaches perform better than other typical methods in terms of accuracy and speed, and are more robust to noise and outliers. (c) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Keywords:

epipolar geometry

evolutionary agent

fundamental matrix

robust estimation

evolutionary behavior

subset template

AI Summary

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