A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest EnvironmentsReport as inadecuate




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1

Departamento Arquitectura Computadores y Automática, Facultad de Informática, Universidad Complutense, 28040 Madrid, Spain

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Departamento Ingeniería del Software e Inteligencia Artificial, Facultad de Informática, Universidad Complutense, 28040 Madrid, Spain

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Centro de Estudios Superiores Felipe II, Ingeniería Técnica en informática de Sistemas, 28300 Aranjuez, Madrid, Spain

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Departamento de Sistemas y Recursos Forestales, CIFOR-INIA, Ctra. de La Coruña km 7.5, 28040 Madrid, Spain





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Abstract 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. View Full-Text

Keywords: stereovision matching; fish-eye lenses; forest image segmentation; feature based stereovision matching; fish-eye lenses; forest image segmentation; feature based





Author: Pedro Javier Herrera 1,* , Gonzalo Pajares 2, Maria Guijarro 3, José J. Ruz 1, Jesús M. Cruz 1 and Fernando Montes 4

Source: http://mdpi.com/



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