Fuzzy multi-criteria decision making in stereovision matching for fish-eye lenses in forest analysis

Authors:
P. J. Herrera;G. Pajares;M. Guijarro;J. J. Ruz;J. M. De La Cruz
Affiliations:
Dpto. Ingeniería del Software e Inteligencia Artificial, Facultad de Informática, Universidad Complutense, Madrid, Spain;Dpto. Ingeniería del Software e Inteligencia Artificial, Facultad de Informática, Universidad Complutense, Madrid, Spain;Centro Superior de Estudios Felipe II, Ingeniería Técnica en informática de Sistemas, Aranjuez, Madrid, Spain;Dpto. Arquitectura Computadores y Automática, Facultad de Informática, Universidad Complutense, Madrid, Spain;Dpto. Arquitectura Computadores y Automática, Facultad de Informática, Universidad Complutense, Madrid, Spain
Venue:
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Year:
2009

Citing 5
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A segmentation method using Otsu and fuzzy k-Means for stereovision matching in hemispherical images from forest environments

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Abstract

This paper describes a novel stereovision matching approach based on omni-directional images obtained with fish-eye lenses in forest environments. The goal is to obtain a disparity map as a previous step for determining the volume of wood in the imaged area. The interest is focused on the trunks of the trees. Due to the irregular distribution of the trunks, the most suitable features are the pixels. A set of six attributes is used for establishing the matching between the pixels in both images of each stereo pair analysed. The final decision about the matched pixels is taken based on a well tested Fuzzy Multi-Criteria Decision Making approach, where the attributes determine the criteria and the potential matches in one image of the stereo pair for a given pixel in the other one determine the alternatives. The application of this decision making approach makes the main finding of the paper. The full procedure is based on the application of three well known matching constraints. The proposed approach is compared favourably against the usage of simple features.