Combination of attributes in stereovision matching for fish-eye lenses in forest analysis

Abstract

This paper describes a novel stereovision matching approach by combining several attributes at the pixel level for omni-directional images obtained with fish-eye lenses in forest environments. The goal is to obtain a disparity map as a previous step for determining distances to the trees and then the volume of wood in the imaged area. The interest is focused on the trunks of the trees. Because of the irregular distribution of the trunks, the most suitable features are the pixels. A set of six attributes is used for establishing the matching between the pixels in both images of the stereo pair. The final decision about the matched pixels is taken by combining the attributes. Two combined strategies are proposed: the Sugeno Fuzzy Integral and the Dempster-Shafer theory. The combined strategies, applied to our specific stereo vision matching problem, make the main finding of the paper. In both, the combination is based on the application of three well known matching constraints. The proposed approaches are compared among them and favourably against the usage of simple features.