Paper Info
Title
A segmentation method using Otsu and fuzzy k-Means for stereovision matching in hemispherical images from forest environments
Abstract
In this paper we describe a novel pixel-based strategy of segmentation and stereovision matching for obtaining disparity maps from hemispherical images captured with fish-eye lenses from forest environments. At a first segmentation stage, the method identifies textures of interest to be either matched or discarded and extracts six attributes of each pixel as features. This is achieved by applying both Otsu and fuzzy k-Means methods. It is a combination of strategies appropriately sequenced to automate the process and facilitate the matching. At a second stage, a stereovision matching process is designed based on the application of three stereovision matching constraints: epipolar, similarity, and uniqueness. The epipolar guides the process. The similarity and uniqueness are mapped through a decision making strategy based on a majority voting criterion. The main finding of this paper is the combination of strategies in the both stages. The method is compared against the usage of simple features and some existing similarity matching strategies using also combination.
Year
DOI
Venue
2011
10.1016/j.asoc.2011.07.010
Appl. Soft Comput.
Keywords
Field
DocType
segmentation method,disparity map,forest environment,existing similarity,epipolar guide,fish-eye lens,stereovision matching process,hemispherical image,stereovision matching,fuzzy k-means method,novel pixel-based strategy,segmentation stage,segmentation,otsu s method,majority voting
Otsu's method,Artificial intelligence,Majority rule,Uniqueness,Computer vision,Epipolar geometry,Pattern recognition,Segmentation,Fuzzy logic,Pixel,Simple Features,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
11
8
1568-4946
Citations 
PageRank 
References 
8
0.46
28
Authors
3
Name
Order
Citations
PageRank
P. Javier Herrera1254.80
Gonzalo Pajares269957.18
María Guijarro3495.79