This paper presents a new method to achieve fast dense matching in a pair of uncalibrated images. Classical area-based dense matching methods suffer from the high computational time resulting from intensive correlation calculations during the search/selection process. In contrast to conventional methods that are based on similarity and correlation techniques, this method is based on enforcing known geometric constraints and uses correlations only on a very small number of points. In particular, this paper proposes a hybrid matching technique that segment the image into two sets: the edge and the nonedge regions. For the edge regions, where discontinuities usually occur, the correlation-based classical matching method is used whereas, for nonedge regions, a segment mapping is used to achieve a correlation-free pixel matching. This segment mapping implicitly enforces all the four well known constraints in stereo matching: epipolar, continuity, uniqueness and, order constraints. The experiments on real images validated our method and showed drastic CPU-time reduction compared to classical methods.
Achieving efficient dense matching for uncalibrated images
01.01.2005
219916 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Achieving Efficient Dense Matching for Uncalibrated Images
British Library Conference Proceedings | 2005
|Matching color uncalibrated images using differential invariants
British Library Online Contents | 2000
|View Synthesis from Two Uncalibrated Images
British Library Conference Proceedings | 2001
|On Robust Rectification for Uncalibrated Images
British Library Conference Proceedings | 1999
|On robust rectification for uncalibrated images
IEEE | 1999
|