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.

Segmentation and Stereoscopic Correspondence in Images Obtained with Omnidirectional Projection for Forest Environments

“…The first tries to isolate the leaves based on statistical measures and the second classifies the other two kinds of textures. The performance of combined classifiers has been reported as a promising approach against individual classifiers (Kuncheva, 2004;Guijarro et al, 2008Guijarro et al, , 2009Pajares et al, 2009;Herrera et al, 2011a). One might wonder why not to identify the textures belonging to the trunks.…”

“…correlation, gradient direction and Laplacian (Herrera, 2010;Herrera et al, 2011aHerrera et al, , 2011b. While gradient magnitude, RGB color and texture obtained the best individual results, respectively.…”

“…Other combined decision making approaches have been successfully used in previous works in the same forest environment, where the final decision about the correct match, among the candidates in the list, is made according to techniques used for combining classifiers conveniently adapted in our approach to be applied for the stereovision matching (Herrera et al, 2009a(Herrera et al, , 2009b(Herrera et al, , 2009c(Herrera et al, , 2011bHerrera, 2010;Pajares et al, 2011). In (Herrera et al, 2011a) the similarity and uniqueness constraints are mapped through a decision making strategy based on a weighted fuzzy similarity approach.…”

Photogrammetric measurement of tree stems from vertical fisheye images

“…However, several camera positions were needed to generate the horizontal fisheye stereo models using this technique. Herrera et al (2011) proposed a fisheye stereovision method for forest environments combining a step for image segmentation (to separate textures of interest) with the matching process. The final decision about the correct match was made based on a weighted fuzzy similarity approach; the objective of the methodology was to compute disparity maps of the tree stems.…”

Tree Stem Reconstruction Using Vertical Fisheye Images: A Preliminary Study

“…Hapca et al (2007) used two digital images taken from two stations with convergence defining an intersection angle of 90° to reconstruct the 3D shape of standing trees. Herrera et al (2011) presented a fisheye stereovision method using image segmentation and image matching to separate textures of interest in forest environments and generating disparity maps of tree trunks with their approach. Liang et al (2014) presented a work assessing the potentiality of point clouds generated using an uncalibrated hand-held camera at a forest plot.…”