A 1D approach to correlation-based stereo matching
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
A 1D approach to correlation-based stereo matching
Auteur(s) :
Lefebvre, Sébastien [Auteur correspondant]
Laboratoire Électronique Ondes et Signaux pour les Transports [IFSTTAR/LEOST]
Ambellouis, Sébastien [Auteur]
Laboratoire Électronique Ondes et Signaux pour les Transports [IFSTTAR/LEOST]
Cabestaing, Francois [Auteur]
LAGIS-SI
Laboratoire Électronique Ondes et Signaux pour les Transports [IFSTTAR/LEOST]
Ambellouis, Sébastien [Auteur]
Laboratoire Électronique Ondes et Signaux pour les Transports [IFSTTAR/LEOST]
Cabestaing, Francois [Auteur]
LAGIS-SI
Titre de la revue :
Image and Vision Computing
Pagination :
580-593
Éditeur :
Elsevier
Date de publication :
2012-08-01
ISSN :
0262-8856
Mot(s)-clé(s) en anglais :
Stereovision
Matching Process
Correlation
1D Window
Fuzzy Logic
Confidence Map
Matching Process
Correlation
1D Window
Fuzzy Logic
Confidence Map
Discipline(s) HAL :
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Résumé en anglais : [en]
In stereovision, indices allowing pixels of the left and right images to be matched are basically one-dimensional features of the epipolar lines. In some situations, these features are not significant or cannot be extracted ...
Lire la suite >In stereovision, indices allowing pixels of the left and right images to be matched are basically one-dimensional features of the epipolar lines. In some situations, these features are not significant or cannot be extracted from the single epipolar line. Therefore, many techniques use 2D neighbourhoods to increase the available information. In this paper, we discuss the systematic use of 2D neighbourhoods for stereo matching. We propose an alternative approach to stereo matching using multiple 1D correlation windows, which yields a semi-dense disparity map and an associated confidence map. A particular technique derived from this approach -- using fuzzy filtering and a basic decision rule -- is compared to about 80 other methods on the Middlebury image datasets [1]. Results are first presented in the framework of the Middlebury website, then on the Receiver Operating Characteristics (ROC) evaluation [2] and, finally, on stereo image pairs of slanted surfaces. We show that a 1D correlation window is sufficient to provide correct matchings in most cases.Lire moins >
Lire la suite >In stereovision, indices allowing pixels of the left and right images to be matched are basically one-dimensional features of the epipolar lines. In some situations, these features are not significant or cannot be extracted from the single epipolar line. Therefore, many techniques use 2D neighbourhoods to increase the available information. In this paper, we discuss the systematic use of 2D neighbourhoods for stereo matching. We propose an alternative approach to stereo matching using multiple 1D correlation windows, which yields a semi-dense disparity map and an associated confidence map. A particular technique derived from this approach -- using fuzzy filtering and a basic decision rule -- is compared to about 80 other methods on the Middlebury image datasets [1]. Results are first presented in the framework of the Middlebury website, then on the Receiver Operating Characteristics (ROC) evaluation [2] and, finally, on stereo image pairs of slanted surfaces. We show that a 1D correlation window is sufficient to provide correct matchings in most cases.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
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