RT Journal Article
T1 A Stereovision Matching Strategy for Images Captured with Fish-Eye Lenses in Forest Environments
A1 Herrera, Pedro Javier
A1 Pajares Martínsanz, Gonzalo
A1 Guijarro Mata-García, María
A1 Ruz Ortiz, José Jaime
A1 Cruz, Jesús M.
AB 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.
PB MDPI
SN 1424-8220
YR 2011
FD 2011-01-31
LK https://hdl.handle.net/20.500.14352/43325
UL https://hdl.handle.net/20.500.14352/43325
LA eng
NO Ministerio de Ciencia e Innovación (MICINN)
DS Docta Complutense
RD 15 dic 2025