Paper Info
Title
A stereovision matching strategy for images captured with fish-eye lenses in forest environments.
Abstract
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 recognition strategy based on the combination of two classifiers: Fuzzy Clustering and Bayesian. At a second stage, a stereovision matching process is performed based on the application of four stereovision matching constraints: epipolar, similarity, uniqueness and smoothness. The epipolar constraint guides the process. The similarity and uniqueness are mapped through a decision making strategy based on a weighted fuzzy similarity approach, obtaining a disparity map. This map is later filtered through the Hopfield Neural Network framework by considering the smoothness constraint. The combination of the segmentation and stereovision matching approaches makes the main contribution. The method is compared against the usage of simple features and combined similarity matching strategies.
Year
DOI
Venue
2011
10.3390/s110201756
SENSORS
Keywords
Field
DocType
fish-eye stereovision matching,fuzzy clustering,Bayesian classifier,weighted fuzzy similarity,Hopfield neural networks,texture classification,fish-eye lenses,hemispherical forest images
Fuzzy clustering,Computer vision,Pattern recognition,Naive Bayes classifier,Epipolar geometry,Segmentation,Fuzzy logic,Artificial intelligence,Simple Features,Engineering,Artificial neural network,Bayesian probability
Journal
Volume
Issue
ISSN
11
2
1424-8220
Citations 
PageRank 
References 
4
0.50
28
Authors
5
Name
Order
Citations
PageRank
pedro javier herrera caro1142.14
Gonzalo Pajares269957.18
María Guijarro3495.79
José J. Ruz4387.37
Jesús M. de la Cruz5565.62