Authors:
Sylvie Chambon
1
and
Alain Crouzil
2
Affiliations:
1
Institut Français des Sciences et Technologies des Transports and de l’Am´enagement et des Réseaux (IFSTTAR), France
;
2
Institut de Recherche en Informatique de Toulouse (IRIT), France
Keyword(s):
Stereovision, Matching, Correlation, Classic measures, Robust statistics, Fusion.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Matching Correspondence and Flow
;
Motion, Tracking and Stereo Vision
Abstract:
In the context of dense stereo matching of pixels, we study the combination of different correlation measures. Considering the previous work about correlation measures, we use some measures that are the most significant in five kinds of measures based on: cross-correlation, classic statistics, image derivatives, nonparametric statistics and robust statistics. More precisely, this study validates the possible improvement of stereo-matching by combining complementary correlation measures and it also highlights the two measures that can be combined in order to take advantage of the different methods: Gradient Correlation measure (GC) and Smooth Median Absolute Deviation measure (SMAD). Finally, we introduce an algorithm of fusion that allows to combine automatically correlation measures.