A Stereovision Matching Strategy for Images Captured with Fish-Eye Lenses in Forest Environments

A Stereovision Matching Strategy for Images Captured with Fish-Eye Lenses in Forest Environments

We present a novel strategy for computing disparity maps from hemispherical stereo images obtained with fish-eye lenses in forest environments. At a first segmentation stage, the method identifies textures of interest to be either matched or discarded. This is achieved by applying a pattern recognit...

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Bibliographic Details
Main Authors: Jesús M. Cruz, José J. Ruz, María Guijarro, Pedro Javier Herrera, Gonzalo Pajares
Format: Article
Language:English
Published: MDPI AG 2011-01-01
Series:Sensors
Subjects:
fish-eye stereovision matching
fuzzy clustering
Bayesian classifier
weighted fuzzy similarity
Hopfield neural networks
texture classification
fish-eye lenses
hemispherical forest images
Online Access:http://www.mdpi.com/1424-8220/11/2/1756/

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