A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest Environments

This paper describes a novel feature-based stereovision matching process based on a pair of omnidirectional images in forest stands acquired with a stereovision sensor equipped with fish-eye lenses. The stereo analysis problem consists of the following steps: image acquisition, camera modelling, fea...

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Main Authors:	Herrera, Pedro Javier, Pajares, Gonzalo, Guijarro, Maria, Ruz, José J., Cruz, Jesús M., Montes, Fernando
Format:	Online
Language:	English
Published:	Molecular Diversity Preservation International (MDPI) 2009
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3267182/

id	pubmed-3267182
recordtype	oai_dc
spelling	pubmed-32671822012-02-02 A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest Environments Herrera, Pedro Javier Pajares, Gonzalo Guijarro, Maria Ruz, José J. Cruz, Jesús M. Montes, Fernando Article This paper describes a novel feature-based stereovision matching process based on a pair of omnidirectional images in forest stands acquired with a stereovision sensor equipped with fish-eye lenses. The stereo analysis problem consists of the following steps: image acquisition, camera modelling, feature extraction, image matching and depth determination. Once the depths of significant points on the trees are obtained, the growing stock volume can be estimated by considering the geometrical camera modelling, which is the final goal. The key steps are feature extraction and image matching. This paper is devoted solely to these two steps. At a first stage a segmentation process extracts the trunks, which are the regions used as features, where each feature is identified through a set of attributes of properties useful for matching. In the second step the features are matched based on the application of the following four well known matching constraints, epipolar, similarity, ordering and uniqueness. The combination of the segmentation and matching processes for this specific kind of sensors make the main contribution of the paper. The method is tested with satisfactory results and compared against the human expert criterion. Molecular Diversity Preservation International (MDPI) 2009-11-26 /pmc/articles/PMC3267182/ /pubmed/22303134 http://dx.doi.org/10.3390/s91209468 Text en © 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Herrera, Pedro Javier Pajares, Gonzalo Guijarro, Maria Ruz, José J. Cruz, Jesús M. Montes, Fernando
spellingShingle	Herrera, Pedro Javier Pajares, Gonzalo Guijarro, Maria Ruz, José J. Cruz, Jesús M. Montes, Fernando A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest Environments
author_facet	Herrera, Pedro Javier Pajares, Gonzalo Guijarro, Maria Ruz, José J. Cruz, Jesús M. Montes, Fernando
author_sort	Herrera, Pedro Javier
title	A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest Environments
title_short	A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest Environments
title_full	A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest Environments
title_fullStr	A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest Environments
title_full_unstemmed	A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest Environments
title_sort	featured-based strategy for stereovision matching in sensors with fish-eye lenses for forest environments
description	This paper describes a novel feature-based stereovision matching process based on a pair of omnidirectional images in forest stands acquired with a stereovision sensor equipped with fish-eye lenses. The stereo analysis problem consists of the following steps: image acquisition, camera modelling, feature extraction, image matching and depth determination. Once the depths of significant points on the trees are obtained, the growing stock volume can be estimated by considering the geometrical camera modelling, which is the final goal. The key steps are feature extraction and image matching. This paper is devoted solely to these two steps. At a first stage a segmentation process extracts the trunks, which are the regions used as features, where each feature is identified through a set of attributes of properties useful for matching. In the second step the features are matched based on the application of the following four well known matching constraints, epipolar, similarity, ordering and uniqueness. The combination of the segmentation and matching processes for this specific kind of sensors make the main contribution of the paper. The method is tested with satisfactory results and compared against the human expert criterion.
publisher	Molecular Diversity Preservation International (MDPI)
publishDate	2009
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3267182/
_version_	1611502447316959232

A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest Environments

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