The stable marriage problem: structure and algorithms
The stable marriage problem: structure and algorithms
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Robust recovery of the epipolar geometry for an uncalibrated stereo rig
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Sequential Updating of Projective and Affine Structure from Motion
International Journal of Computer Vision
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Experiments with a New Area-Based Stereo Algorithm
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume I - Volume I
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Matching Widely Separated Views Based on Affine Invariant Regions
International Journal of Computer Vision
Finding Region Correspondences for Wide Baseline Stereo
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Wide-Baseline Stereo Matching with Line Segments
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Towards obstacle reconstruction through wide baseline set of images
Towards obstacle reconstruction through wide baseline set of images
On feature point matching, in the calibrated and uncalibrated contexts, between widely and narrowly separated images
Computational Experiments with a Feature Based Stereo Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper addresses the sparse view matching problem where the camera parameters lie within ranges depending on the sensor used. An approach, based on homographic transformation, is proposed. The operation is split into two phases. The first phase deals with matches on the ground surface, which is considered planar. The second phase detects matches on arbitrary planes. This is done by detecting junctions of different shapes and reconstructing planes inferred by them. Two versions of that approach are suggested based on the sum of absolute differences and the variance normalized correlation techniques. The first technique is computationally inexpensive while the later is more robust to changes in lighting condition between views. Experiments show that our approach outperforms non-homographic correlation.