A 1D approach to correlation-based stereo matching
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
A 1D approach to correlation-based stereo matching
Author(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
Journal title :
Image and Vision Computing
Pages :
580-593
Publisher :
Elsevier
Publication date :
2012-08-01
ISSN :
0262-8856
English keyword(s) :
Stereovision
Matching Process
Correlation
1D Window
Fuzzy Logic
Confidence Map
Matching Process
Correlation
1D Window
Fuzzy Logic
Confidence Map
HAL domain(s) :
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]
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Popular science :
Non
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